JP7707847B2 - Surface texture measuring device and surface texture measuring method - Google Patents

Description

This disclosure relates to a surface texture measuring device and a surface texture measuring method.

The condition of an object's surface is an important factor in determining the quality of the object, such as its appearance and design. One known method for evaluating the surface condition of an object is to evaluate the distortion of a reflected image reflected on the object's surface. Typically, the degree of distortion of the reflected image is evaluated visually.

Distortion of the reflected image occurs, for example, due to unevenness or undulations on the object surface. For example, Patent Document 1 discloses a flatness measuring device that measures the flatness of the unevenness or undulations on the surface of an object to be measured, in which a lattice image is projected onto the surface of the sample to be measured, the pitch of the lattice image is detected, and the average value of the standard deviation of the pitch of the lattice image is taken as the flatness.

Patent No. 2521729

However, existing devices are unable to adequately quantify the distortion of reflected images.

This disclosure has been made in consideration of the above-mentioned circumstances, and aims to provide a surface texture measuring device and a surface texture measuring method that can quantify and quantitatively evaluate the distortion of a reflected image.

One embodiment of the present disclosure includes an irradiation unit having a light source and irradiating a surface to be measured with illumination light having one or more bright and dark regions, a light detection unit that focuses the light source through the surface to be measured, receives reflected light having one or more bright and dark regions corresponding to the bright and dark regions of the illumination light, and detects the light intensity distribution of the reflected light on the surface to be measured, and determines, for each bright region, a plurality of selection points located on a line connecting measurement points of the light intensity of the reflected light using a predetermined light intensity threshold in the light intensity distribution of the reflected light on the surface to be measured. To achieve this, a surface texture measuring device is provided that has a first processing unit that identifies the position on the measured surface that indicates the selected point, a second processing unit that creates a graph for each of the bright areas, in which the positions on the measured surface that indicate the selected point are plotted, with a first direction on the measured surface as the horizontal axis and a second direction on the measured surface that is perpendicular to the first direction as the vertical axis, a third processing unit that determines a reference line for each of the bright areas based on the shape of the bright area of the illumination light, and a fourth processing unit that determines the difference between the point plotted on the graph and the reference line for each of the bright areas, and quantifies the variation in the difference for each of the bright areas.

In the surface texture measuring device disclosed herein, it is preferable that the light detection unit is an imaging device.

In the surface texture measuring device of the present disclosure, it is preferable that the illumination light has a linear bright region as the bright region, and the reflected light has a linear bright region as the bright region that corresponds to the linear bright region of the illumination light.

In the above case, the light detection unit divides the measured surface into M x N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects the light intensity distribution of the reflected light on the measured surface; the first processing unit divides the light intensity distribution of the reflected light on the measured surface into n divided regions in the longitudinal direction of the linear bright region of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and in the light intensity distribution of the reflected light of each divided region, detects the light intensity of the linear bright region for each linear bright region. The light intensity distribution of the region may be divided into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as the boundary, and a measurement point showing a light intensity equal to or greater than the light intensity threshold and closest to the light intensity threshold may be selected in either the left light intensity distribution or the right light intensity distribution of each linear bright region, or a measurement point showing a light intensity equal to or greater than the light intensity threshold and closest to the light intensity threshold may be selected for each of the left light intensity distribution and the right light intensity distribution of each linear bright region, and the measurement point may be set as the selected point.

In the above case, the light detection unit divides the measured surface into M × N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects the light intensity distribution of the reflected light on the measured surface; the first processing unit divides the light intensity distribution of the reflected light on the measured surface into n divided regions in the longitudinal direction of the linear bright region of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and detects the light intensity distribution of the reflected light for each divided region. In the light intensity distribution of the incident light, for each linear bright region, the light intensity distribution of the linear bright region is divided into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as the boundary, and a measurement point showing a light intensity closest to the light intensity threshold is selected in either the left light intensity distribution or the right light intensity distribution of each linear bright region, or a measurement point showing a light intensity closest to the light intensity threshold is selected for each of the left light intensity distribution and the right light intensity distribution of each linear bright region, and the measurement point is set as the selected point.

In the above case, the light detection unit divides the measured surface into M×N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects the light intensity distribution of the reflected light on the measured surface; the first processing unit divides the light intensity distribution of the reflected light on the measured surface into n divided regions in the longitudinal direction of the linear bright region of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and in the light intensity distribution of the reflected light in each divided region, for each linear bright region, divides the light intensity distribution of the linear bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as a boundary, and detects the left light intensity distribution and In either one of the right-side light intensity distributions, a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or greater than the light intensity threshold and closest to the light intensity threshold, and a point that indicates the light intensity threshold is selected; alternatively, for each of the left-side light intensity distribution and the right-side light intensity distribution of each of the linear bright regions, a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or greater than the light intensity threshold and closest to the light intensity threshold, and a point that indicates the light intensity threshold is selected, and the point that indicates the light intensity threshold is the selected point.

In addition, in the surface texture measuring device disclosed herein, the third processing unit may determine an approximation equation from the points plotted on the graph, and use the approximation equation as the reference line.

In addition, in the surface texture measuring device disclosed herein, the fourth processing unit can calculate the standard deviation of the difference between the points plotted on the graph and the reference line.

In addition, the surface texture measuring device disclosed herein may have a fifth processing unit that determines the maximum value of the light intensity of the bright region and the minimum value of the light intensity of the dark region in the light intensity distribution of the reflected light on the measured surface, and calculates image clarity using the following equation (1).
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.)

Another embodiment of the present disclosure includes an irradiation step of irradiating a surface to be measured with illumination light having one or more bright and dark regions using a light source, a light detection step of focusing the light source through the surface to be measured, receiving reflected light having one or more bright and dark regions corresponding to the bright and dark regions of the illumination light, and detecting a light intensity distribution of the reflected light on the surface to be measured, and a step of determining, for each bright region, a plurality of selection points located on a line connecting measurement points of the light intensity of the reflected light using a predetermined light intensity threshold in the light intensity distribution of the reflected light on the surface to be measured. The method for measuring surface texture includes a first process for identifying the positions on the surface to be measured that indicate the selected points, a second process for creating a graph for each bright area in which the positions on the surface to be measured that indicate the selected points are plotted, with a first direction on the surface to be measured as the horizontal axis and a second direction on the surface to be measured that is perpendicular to the first direction as the vertical axis, a third process for determining a reference line for each bright area based on the shape of the bright area of the illumination light, and a fourth process for determining the difference between the points plotted on the graph and the reference line for each bright area, and quantifying the variation in the difference for each bright area.

In addition, in the surface texture measurement method disclosed herein, it is preferable to use an imaging device in the light detection process.

In addition, in the surface texture measurement method disclosed herein, it is preferable that the illumination light has a linear bright region as the bright region, and the reflected light has a linear bright region as the bright region that corresponds to the linear bright region of the illumination light.

In the above case, in the light detection process, the measured surface is divided into M×N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, and the light intensity of the reflected light is detected for each measurement region to detect the light intensity distribution of the reflected light on the measured surface. In the first processing unit, the light intensity distribution of the reflected light on the measured surface is divided into n divided regions in the longitudinal direction of the linear bright region of the reflected light, the light intensity distribution of the reflected light for each divided region is extracted, and in the light intensity distribution of the reflected light of each divided region, the linear bright region is detected for each linear bright region. The light intensity distribution of the region may be divided into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as the boundary, and a measurement point showing a light intensity equal to or greater than the light intensity threshold and closest to the light intensity threshold may be selected in either the left light intensity distribution or the right light intensity distribution of each linear bright region, or a measurement point showing a light intensity equal to or greater than the light intensity threshold and closest to the light intensity threshold may be selected for each of the left light intensity distribution and the right light intensity distribution of each linear bright region, and the measurement point may be set as the selected point.

In the above case, in the light detection process, the measured surface is divided into M×N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, and the light intensity of the reflected light is detected for each measurement region to detect the light intensity distribution of the reflected light on the measured surface. In the first processing unit, the light intensity distribution of the reflected light on the measured surface is divided into n divided regions in the longitudinal direction of the linear bright region of the reflected light, the light intensity distribution of the reflected light for each divided region is extracted, and the In the light intensity distribution of the reflected light, for each linear bright region, the light intensity distribution of the linear bright region is divided into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as the boundary, and a measurement point showing a light intensity closest to the light intensity threshold is selected in either the left light intensity distribution or the right light intensity distribution of each linear bright region, or a measurement point showing a light intensity closest to the light intensity threshold is selected for each of the left light intensity distribution and the right light intensity distribution of each linear bright region, and the measurement point is set as the selected point.

In the above case, the light detection process divides the measured surface into M×N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects the light intensity distribution of the reflected light on the measured surface. The first processing unit divides the light intensity distribution of the reflected light on the measured surface into n divided regions in the longitudinal direction of the linear bright region of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and in the light intensity distribution of the reflected light in each divided region, for each linear bright region, divides the light intensity distribution of the linear bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each linear bright region as a boundary, and extracts the left light intensity distribution and the right light intensity distribution of each linear bright region. Alternatively, for each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions, a point that is located on a line connecting a measurement point that is less than the light intensity threshold or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that is greater than or equal to the light intensity threshold or exceeds the light intensity threshold and is closest to the light intensity threshold, and a point that is located on a line connecting a measurement point that is less than the light intensity threshold or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that is greater than or equal to the light intensity threshold or exceeds the light intensity threshold and is closest to the light intensity threshold, may be selected, and the point that is the light intensity threshold may be the selected point.

In addition, in the surface texture measurement method disclosed herein, in the third processing step, an approximation equation may be found from the points plotted on the graph, and the approximation equation may be used as the reference line.

In addition, in the surface texture measurement method disclosed herein, the fourth processing step can calculate the standard deviation of the difference between the points plotted on the graph and the reference line.

In addition, the surface texture measuring method disclosed herein may include a fifth processing step of determining the maximum value of light intensity in the bright region and the minimum value of light intensity in the dark region in the light intensity distribution of the reflected light on the measured surface, and calculating image clarity using the following formula (1):
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.)

The present disclosure has the advantage of being able to convert the distortion of a reflected image into a numerical value and quantitatively evaluate it.

FIG. 1 is a diagram illustrating a surface texture measuring device according to the present disclosure. 4 is a schematic plan view illustrating a mask that configures an illumination unit in the surface texture measuring device of the present disclosure. FIG. 1 is a diagram illustrating an example of an image captured by a light detection unit in the surface texture measuring device of the present disclosure. FIG. 4 is a graph illustrating an example of a light intensity distribution of reflected light on a surface to be measured. 4 is a graph illustrating an example of a light intensity distribution of reflected light on a surface to be measured. 1 is a graph in which positions on the surface to be measured indicating selected points are plotted, with a first direction on the surface to be measured as the horizontal axis and a second direction on the surface to be measured perpendicular to the first direction as the vertical axis. 4 is a schematic plan view illustrating a mask that configures an illumination unit in the surface texture measuring device of the present disclosure. FIG. 1 is a diagram illustrating an example of an image captured by a light detection unit in the surface texture measuring device of the present disclosure. FIG. 1 is a diagram illustrating an example of an image captured by a light detection unit in the surface texture measuring device of the present disclosure. FIG. 1 is a diagram illustrating an example of an image captured by a light detection unit in the surface texture measuring device of the present disclosure. FIG. 4 is a graph illustrating an example of a light intensity distribution of reflected light on a surface to be measured. 4 is a graph illustrating an example of a light intensity distribution of reflected light on a surface to be measured. 1 is a graph in which positions on the surface to be measured indicating selected points are plotted, with a first direction on the surface to be measured as the horizontal axis and a second direction on the surface to be measured perpendicular to the first direction as the vertical axis. FIG. 1 is a diagram illustrating a surface texture measuring device according to the present disclosure. 1 is a process diagram illustrating a surface texture measuring method according to the present disclosure. 1 is a process diagram illustrating a surface texture measuring method according to the present disclosure.

Below, an embodiment of the present disclosure will be described with reference to the drawings. However, the present disclosure can be implemented in many different forms, and should not be interpreted as being limited to the description of the embodiment exemplified below. In addition, in order to make the explanation clearer, the drawings may show the width, thickness, shape, etc. of each part in a schematic manner compared to the actual form, but these are merely examples and do not limit the interpretation of the present disclosure. In this specification and each figure, elements similar to those described above with respect to the previous figures are given the same reference numerals, and detailed explanations may be omitted as appropriate.

The surface texture measuring device and surface texture measuring method disclosed herein are described in detail below.

A. Surface Texture Measuring Device The surface texture measuring device in the present disclosure includes an irradiation unit that has a light source and irradiates a measured surface with illumination light having one or more bright regions and dark regions, a light detection unit that focuses the light source through the measured surface, receives reflected light that is reflected by the measured surface and has one or more bright regions and dark regions corresponding to the bright regions and the dark regions of the illumination light, and detects a light intensity distribution of the reflected light on the measured surface, and detects positions on a line connecting measurement points of the light intensity of the reflected light using a predetermined light intensity threshold for each bright region in the light intensity distribution of the reflected light on the measured surface. a first processing unit that determines a plurality of selection points to be measured and identifies positions on the surface to be measured that indicate the selection points; a second processing unit that creates a graph for each of the bright areas, in which the positions on the surface to be measured that indicate the selection points are plotted, with a first direction on the surface to be measured as the horizontal axis and a second direction on the surface to be measured that is perpendicular to the first direction as the vertical axis; a third processing unit that determines, for each of the bright areas, a reference line based on a shape of the bright area of the illumination light; and a fourth processing unit that determines, for each of the bright areas, a difference between the points plotted on the graph and the reference line and quantifies a variation in the difference for each of the bright areas.

The surface texture measuring device of the present disclosure will be described with reference to the drawings. FIG. 1 is a diagram illustrating the surface texture measuring device of the present disclosure. As shown in FIG. 1, the surface texture measuring device 10 includes an irradiation unit 2 having a light source 3, which irradiates a measured surface 1 with illumination light L1 having one or more bright and dark regions, a light detection unit 5 that focuses the light source 3 through the measured surface 1, receives reflected light L2 having one or more bright and dark regions corresponding to the bright and dark regions of the illumination light L1, and detects the light intensity distribution of the reflected light L2 on the measured surface 1, and detects the light intensity of the reflected light L2 using a predetermined light intensity threshold for each bright region in the light intensity distribution of the reflected light L2 on the measured surface 1. The system includes a first processing unit 6 that determines a number of selection points located on a line connecting the measurement points and identifies the positions on the surface to be measured that indicate the selection points; a second processing unit 7 that creates a graph for each bright area in which the positions on the surface to be measured that indicate the selection points are plotted, with a first direction on the surface to be measured 1 as the horizontal axis and a second direction on the surface to be measured 1 that is perpendicular to the first direction as the vertical axis; a third processing unit 8 that determines a reference line for each bright area based on the shape of the bright area of the illumination light L1; and a fourth processing unit 9 that determines the difference between the points plotted on the graph and the reference line for each bright area and quantifies the variation in the difference for each bright area.

The illumination unit 2 has a light source 3 and a mask 4. For example, as shown in FIG. 2, the mask 4 has a plurality of rectangular transmissive regions 11 and light-shielding regions 12, and further has a cross-shaped transmissive region 13 for focusing. In the illumination unit 2, the light from the light source 3 passes through the mask 4, and illumination light L1 having a plurality of linear bright and dark regions is irradiated onto the measurement surface 1.

When illumination light L1 is irradiated onto the surface 1 to be measured, the illumination light L1 is reflected by the surface 1 to be measured. As shown in FIG. 3, this reflected light L2 has a number of linear bright regions 21 and dark regions 22 that correspond to the linear bright regions and dark regions of the illumination light L1.

For example, in FIG. 1, when the light detection unit 5 is an imaging device, the light detection unit 5 focuses on the light source 3 through the measured surface 1 to image the reflected light L2. At this time, for example, by focusing on the cross-shaped light projected from the light source 3 through the cross-shaped focusing transmission area 13 of the mask 4 of the illumination unit 2 onto the measured surface 1, the light source 3 is focused through the measured surface 1. As a result, an image of the reflected light on the measured surface 1 is obtained, as shown in FIG. 3, for example. In addition, the imaging device (light detection unit 5) detects the light intensity of the reflected light L2 for each pixel and detects the light intensity distribution of the reflected light L2 on the measured surface 1.

In the first processing unit 6, for example in FIG. 3, when the image of the reflected light on the surface to be measured has N×M pixels, with N pixels in the longitudinal direction D1 of the linear bright region of the reflected light and M pixels in the lateral direction D2 of the linear bright region of the reflected light, the image of the reflected light on the surface to be measured is divided into n divided regions A1 to An in the longitudinal direction D1 of the linear bright region of the reflected light. The number of pixels N in the longitudinal direction D1 of the linear bright region of the reflected light is the same as the number of divided regions n. Then, the light intensity distribution of the reflected light for each divided region A1 to An is extracted. The light intensity distribution of the reflected light in each divided region A1 to An is, for example, a scatter diagram as shown in FIG. 4. In the light intensity distribution shown in FIG. 4, the horizontal axis represents the position of the pixel in the lateral direction D2 of the linear bright region of the reflected light.

The first processing unit 6 also uses a predetermined light intensity threshold value to determine, for each linear bright area 21 in the light intensity distribution of the reflected light in each divided area A1 to An, a selection point located on a line connecting the measurement points of the light intensity of the reflected light. Then, the position on the surface to be measured indicating the selection point is identified.

Here is an example of how to determine a selection point using a light intensity threshold.

5A is an example of the light intensity distribution of the reflected light of the divided region A1. In the light intensity distribution of the reflected light of the divided region A1, the peak value of the light intensity is obtained for each of the linear bright regions 21a to 21d, and the light intensity values that are, for example, 50% of the peak value of the light intensity are set as the light intensity thresholds T _a1 to T _d1 . In the light intensity distribution of the reflected light of the divided region A1, the light intensity distribution of each of the linear bright regions 21a to 21d is divided into a left light intensity distribution L and a right light intensity distribution R with the peak value of each light intensity as the boundary. The selection point P _L1 is a measurement point in the left light intensity distribution L of the linear bright region 21a that indicates a light intensity _that is equal to or greater than the light intensity threshold T _a1 or that exceeds the light intensity threshold T _{a1 and is closest to the light intensity threshold T a1} . Moreover, the selection point P _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _a1 and closest to _{the light intensity threshold T a1 in} the right light intensity distribution R of the linear bright region 21a. Similarly, the selection point Q _L1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _b1 and closest to the light intensity threshold T _b1 in the left light intensity distribution L _of the linear bright region 21b. Also, the selection point Q _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _b1 and closest to the light intensity threshold T _b1 in the right light intensity distribution R of the linear bright region 21b. Similarly, the selection point R _L1 is a measurement point showing a light intensity _that is equal to or greater than _the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 in the left light intensity distribution L of the linear bright region 21c. Moreover, the selection point R _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _c1 or exceeds the light intensity threshold T _c1 and is closest to the light intensity threshold T _c1 in the right-side light intensity distribution R of the linear bright region 21 c. Similarly, the selection point S _L1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _d1 or exceeds the light intensity threshold T _d1 and is closest to the light intensity threshold T _d1 in the left-side light intensity distribution L of the linear bright region 21 d. Moreover, the selection point S _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _d1 or exceeds the light intensity threshold T _d1 and is closest to the light intensity threshold T _d1 in the right-side light intensity distribution R of the linear bright region 21 d.

Then, the positions (pixel positions) on the image of reflected light that indicate the selection points P _L1, P _R1, Q _L1, Q _R1, R _L1, R _R1, S _{L1, and} S _R1 are identified.

5B is an example of the light intensity distribution of the reflected light of the divided region An. In the light intensity distribution of the reflected light of the divided region An, similarly to the light intensity distribution of the reflected light of the divided region A1, the selection points P _{Ln, P Rn,} Q _Ln, Q Rn, R Ln, R Rn, S Ln, and S Rn are determined for each of the linear bright regions 21a to 21d and for each of the left light intensity distribution L and the right light intensity distribution R using the light intensity thresholds T _an to _{T dn} . Then, the positions (pixel positions) of the reflected light on the image indicating the selection points P _{Ln ,} P _{Rn ,} Q _{Ln ,} Q _{Rn ,} R _Ln, R _Rn, S _{Ln, and S Rn} are specified.

The first processing unit 6 determines a selection point for each linear bright region and for each left-side and right-side light intensity distribution of the light intensity distribution of the reflected light in each divided region A1 to An using a light intensity threshold, and identifies the position on the image of the reflected light (position of the pixel) that indicates the selection point. Note that the position on the image of the reflected light (position of the pixel) corresponds to the position on the surface to be measured.

The second processing unit 7 creates a graph in which the positions on the surface to be measured that indicate the selected points are plotted, with the horizontal axis representing the first direction on the surface to be measured and the vertical axis representing the second direction on the surface to be measured that is perpendicular to the first direction.

For example, Fig. 6(a) is a graph plotting positions (pixel positions) on the image of reflected light indicating the selection points P _L1 to P _Ln for the left light intensity distribution L of the linear bright area 21a in the light intensity distribution of the reflected light of each divided area A1 to An. In Fig. 6(a), the horizontal axis represents the pixel position in the longitudinal direction D1 of the linear bright area of the reflected light, and the vertical axis represents the pixel position in the lateral direction D2 of the linear bright area of the reflected light. Note that the positions on the image of reflected light (pixel positions) correspond to the positions on the surface to be measured, and in Fig. 6(a), the longitudinal direction D1 of the linear bright area of the image of reflected light corresponds to the first direction on the surface to be measured, and the lateral direction D2 of the linear bright area of the image of reflected light corresponds to the second direction on the surface to be measured.

The second processing unit 7 creates a graph that plots the position on the reflected light image (pixel position) that indicates the selection point for each linear bright area and for each left light intensity distribution and right light intensity distribution. For example, as shown in FIG. 3, if the reflected light image has four linear bright areas and each linear bright area is divided into two, a left light intensity distribution and a right light intensity distribution, eight graphs are created.

The third processing unit 8 determines a reference line based on the shape of the linear bright area of the illumination light. For example, as shown in FIG. 2, when the illumination light L1 has a linear bright area, linear approximation can be performed. For example, the points plotted on the graph shown in FIG. 6(a) are linearly approximated to determine the linear approximation equation shown in FIG. 6(b) (solid line in FIG. 6). This linear approximation equation is used as the reference line.

The third processing unit 8 finds a reference line based on the shape of the linear bright area of the illumination light for each linear bright area and for each left light intensity distribution and right light intensity distribution. For example, as shown in FIG. 3, when the image of reflected light has four linear bright areas 21 and each linear bright area is divided into two, a left light intensity distribution and a right light intensity distribution, eight reference lines are found.

In the fourth processing unit 9, the difference between the points plotted on the graph and the reference line is calculated for each linear bright region and for each left light intensity distribution and right light intensity distribution. Then, the variation of the difference is quantified for each linear bright region and for each left light intensity distribution and right light intensity distribution. Specifically, the standard deviation of the difference is calculated for each linear bright region and for each left light intensity distribution and right light intensity distribution. These standard deviations are set as the variation of the difference for each linear bright region and for each left light intensity distribution and right light intensity distribution. In addition, the arithmetic mean value of the standard deviation of the difference for each linear bright region and for each left light intensity distribution and right light intensity distribution is calculated. This arithmetic mean value can be set as the variation of the difference for all linear bright regions.

In the surface texture measuring device disclosed herein, the distortion of the reflected image of the surface being measured can be quantified, for example as the standard deviation of the above difference, and quantitatively evaluated.

Furthermore, when determining the pitch of bright areas as in conventional devices, the relative distortion between bright areas due to the pitch is quantified. In this case, for example, if multiple bright areas are distorted in the same direction, the variation in the pitch of the bright areas tends to be small, and the distortion of the reflected image cannot be adequately evaluated. In contrast, in the present disclosure, the variation in the difference is determined for each bright area, so the absolute distortion of each bright area can be quantified. Therefore, even if multiple bright areas are distorted in the same direction, the distortion of the reflected image can be quantitatively evaluated.

In addition, in this disclosure, a reference line is obtained based on the shape of the bright area of the illumination light, and an analysis method is used that approximates the shape of the bright area of the illumination light. Therefore, a numerical value that directly represents the distortion relative to the light source can be calculated. In addition, it is possible to quantitatively evaluate distortion with a large period.

The configuration of the surface texture measuring device disclosed herein is described below.

1. Illumination Unit The illumination unit in the present disclosure has a light source and irradiates the surface to be measured with illumination light having one or more bright and dark regions.

The illumination light emitted by the illumination unit may have one or more bright areas and one or more dark areas. In particular, it is preferable that the illumination light has multiple bright areas. By irradiating the surface to be measured with such illumination light, the number of data points (sample size) can be increased, and the surface properties of the surface to be measured can be measured accurately.

The shape of the bright area is not particularly limited, and may be, for example, linear, planar, or dot-like.

When the bright area is linear, the shape of the linear bright area is not particularly limited as long as it is linear, and may be, for example, straight or curved.

In addition, when the bright area is planar, the shape of the planar bright area is not particularly limited as long as it is planar, and examples of such shapes include a square, rectangular, circular, elliptical, etc.

In addition, when the bright areas are dot-like, the shape of the dot-like bright areas is not particularly limited as long as it is dot-like, and examples of such shapes include a square shape, a rectangle shape, etc.

In particular, it is preferable that the illumination light has a linear bright region, and more preferably has multiple linear bright regions. In particular, when the illumination light has multiple linear bright regions, it is preferable that it has stripe-shaped bright and dark regions. By irradiating the surface to be measured with such illumination light, the number of data points (sample size) can be increased, and the surface properties of the surface to be measured can be measured accurately.

In addition, when the illumination light has multiple point-like bright areas, the bright areas may be arranged in a lattice pattern.

The illumination unit is not particularly limited as long as it has a light source and can irradiate illumination light having bright and dark regions. For example, it may be an illumination unit having a light source and a mask having a transmissive region and a light-shielding region, or a line light source. In particular, it is preferable that the illumination unit has a light source and a mask having a transmissive region and a light-shielding region. By using the mask, the shape of the bright region can be easily controlled.

When the illumination unit has a light source and a mask, the light source is not particularly limited, and examples include LEDs (light-emitting diodes), OLEDs (organic light-emitting diodes), halogen lamps, tungsten lamps, etc. Among these, OLEDs are preferred. OLED light sources have high uniformity in light intensity.

The mask also has transmissive regions and light-shielding regions. The number of transmissive regions and light-shielding regions in the mask may be one or more.

The shape of the transparent region is not particularly limited, and may be, for example, linear, planar, or linear.

When the transmission area is linear, the shape of the linear transmission area is not particularly limited as long as it is linear, and may be, for example, straight or curved.

When the transparent region is planar, the shape of the planar transparent region is not particularly limited as long as it is planar, and examples of the shape include a square, rectangular, circular, elliptical, etc.

In addition, when the transmissive regions are dot-like, the shape of the dot-like bright regions is not particularly limited as long as it is dot-like, and examples of such shapes include a square shape, a rectangle shape, etc.

In particular, it is preferable for the mask to have a linear transmissive region, and more preferably to have multiple linear transmissive regions. In particular, when the mask has multiple linear transmissive regions, it is preferable for the transmissive regions to be arranged in a stripe pattern. By using such a mask, the number of data points (sample size) can be increased, and the surface properties of the surface to be measured can be measured accurately.

Also, if the mask has multiple point-like transparent regions, the transparent regions may be arranged in a lattice pattern.

2 shows an example in which the transmissive regions 11 are linear and straight, and are arranged in a stripe pattern, in which case the planar shape of the transmissive regions 11 is rectangular. FIG. 7(a) shows an example in which the transmissive regions 11 are linear and curved, and in this case the planar shape of the transmissive regions 11 can be an arc shape. FIG. 7(b) shows an example in which the transmissive regions 11 are planar, and are rectangular in plan. FIG. 7(c) shows an example in which the transmissive regions 11 are dot-like, are arranged in a lattice pattern, and are square in plan.

When the mask has multiple linear transparent regions arranged in a stripe pattern, the line width, length, and pitch of the linear transparent regions may be equal or different, but are preferably equal. The line width, length, and pitch of the linear transparent regions are not particularly limited and may be set as appropriate.

In addition, when the mask has a planar transmissive area, the size of the planar transmissive area is not particularly limited and may be set as appropriate.

In addition, when the mask has multiple dot-like transparent areas that are arranged in a grid pattern, the dot-like transparent areas may be equal or different in size, but it is preferable that they are equal. The size of the dot-like transparent areas is not particularly limited and is set as appropriate. It is preferable that the size of the dot-like transparent areas is larger than the period of the distortion of the reflected image. The distortion of the reflected image can be represented by one dot-like bright area.

In addition, when the mask has a plurality of dot-like transparent regions that are arranged in a lattice pattern, the pitch of the dot-like transparent regions may be equal or different, but is preferably equal. The pitch of the dot-like transparent regions is not particularly limited and may be set as appropriate. It is preferable that the pitch of the dot-like transparent regions is smaller than the period of the distortion of the reflected image. If the pitch of the dot-like transparent regions is larger than the period of the distortion of the reflected image, it may be difficult to sufficiently quantitatively evaluate the distortion of the reflected image.

The mask may further have a focusing transmissive region. The planar shape of the focusing transmissive region is not particularly limited as long as it can focus the light source through the surface to be measured in the light detection unit described below, and can have various shapes, such as a cross shape.

The mask may have a transparent region and a light-shielding region, and may, for example, have a transparent substrate and a patterned light-shielding layer disposed on one side of the transparent substrate, or may have a light-shielding substrate and an opening penetrating the light-shielding substrate.

When the illumination unit is a light source, the light source may be, for example, a linear light source, a point light source, or a surface light source. Among these, a linear light source is preferred. A linear light source can emit illumination light having a linear bright area.

Examples of linear light sources include fluorescent lamps, linear LED light sources, and linear OLED light sources.

Examples of surface light sources include surface LED light sources and surface OLED light sources.

A display may also be used as the illumination unit. In this case, the display shows a pattern having light and dark areas.

The angle of incidence of the illumination light on the surface to be measured is not particularly limited as long as the light detection unit can receive the reflected light from the surface to be measured, and is usually less than 90°. In addition, the variation in the above difference may be quantified for each angle of incidence of the illumination light. The angle of incidence of the illumination light on the surface to be measured is the angle between the direction of incidence of the illumination light on the surface to be measured and the normal direction of the surface to be measured.

The distance between the surface to be measured and the illumination unit is not particularly limited and may be set appropriately as long as the illumination light emitted from the illumination unit can be reflected by the surface to be measured and the reflected light having light and dark areas can be received by the light detection unit.

The light detection unit in the present disclosure focuses the light source through the surface to be measured, receives reflected light having bright and dark regions that correspond to the bright and dark regions of the illumination light, and detects the light intensity distribution of the reflected light on the surface to be measured.

The light detection unit is not particularly limited as long as it can focus on the light source through the surface to be measured, receive reflected light from the surface to be measured, and detect the light intensity distribution of the reflected light on the surface to be measured. For example, a photoelectric conversion device that converts light into an electrical signal can be used. Examples of photoelectric conversion devices include an imaging device and a photodiode array. The photoelectric conversion device may also be a device that scans a single photodiode using a driving device such as a stepping motor. Among these, a photoelectric conversion device is preferred, and an imaging device is more preferred.

The imaging device focuses the light source through the surface to be measured, and captures the reflected light that is reflected by the surface to be measured and has bright and dark regions that correspond to the bright and dark regions of the illumination light. The light intensity distribution of the reflected light on the surface to be measured is then obtained from the image obtained.

The imaging device has an imaging element. Examples of the imaging element include a CCD image sensor and a CMOS image sensor. Examples of the imaging device include a digital camera. Examples of the imaging device include an area camera and a line camera. In the case of a line camera, if the reflected light has a linear bright area, it is preferable to scan in the longitudinal direction of the linear bright area of the reflected light. In the case of a line camera, if the reflected light has a lattice-point-shaped bright area, it is preferable to scan in the x direction or y direction of the lattice points of the bright area of the reflected light.

The light detection unit receives the specularly reflected light of the illumination light. Therefore, the reception angle of the reflected light is equal to the incident angle of the illumination light. The reception angle of the reflected light is the angle with respect to the normal direction of the surface to be measured. Furthermore, the reception angle θ2 of the reflected light being equal to the incident angle θ1 of the illumination light means that the reception angle θ2 is within θ1±10°, preferably within θ1±5°, and more preferably within θ1±3°.

The distance between the surface to be measured and the light detection unit is not particularly limited as long as the light detection unit can receive reflected light having light and dark areas, and is set appropriately. The distance between the surface to be measured and the light detection unit may be the same as the distance between the surface to be measured and the illumination unit, or it may be different.

If the light detection unit is an imaging device, the number of pixels of the imaging element can be set appropriately.

In the light detection unit, the light source is focused through the surface to be measured. For example, when the illumination unit has a light source and a mask, a method of focusing the light source through the surface to be measured can be exemplified by using a mask having a focusing transmission area and focusing the light projected from the light source through the focusing transmission area of the mask on the surface to be measured. In addition, when the illumination unit is a light source, a method of placing an alignment mark on the light source and focusing on the alignment mark projected on the surface to be measured can be exemplified.

The light reflected from the surface to be measured has one or more bright and dark regions that correspond to the bright and dark regions of the illumination light. The bright and dark regions in the reflected light are similar to the bright and dark regions in the illumination light.

In the light detection unit, it is preferable to divide the surface to be measured into a plurality of measurement regions, detect the light intensity of the reflected light for each measurement region, and detect the light intensity distribution of the reflected light on the surface to be measured. For example, if the reflected light has a linear bright region, it is preferable to divide the surface to be measured into M x N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light, and M in the lateral direction of the linear bright region of the reflected light, and detect the light intensity of the reflected light for each measurement region to detect the light intensity distribution of the reflected light on the surface to be measured. This allows the number of data (sample size) to be increased, and the surface properties of the surface to be measured to be measured accurately.

The number of measurement regions is not particularly limited. For example, when the light detection unit is an imaging device, the number of pixels in the image of the reflected light can be the number of measurement regions. Specifically, when the reflected light has a linear bright region, the number of pixels in the longitudinal direction of the linear bright region in the image of the reflected light obtained by the imaging device can be N, the number of pixels in the lateral direction of the linear bright region can be M, and the number of pixels in the image of the reflected light can be the number of measurement regions (M x N). In other words, one measurement region of the measured surface can correspond to one pixel of the image of the reflected light. The more measurement regions there are, the more data (sample size) can be obtained, and the more accurately the surface properties of the measured surface can be measured.

Here, the longitudinal direction of the linear bright area of the reflected light refers to the direction in which the linear bright area of the reflected light extends. For example, if the shape of the linear bright area of the reflected light is linear, the longitudinal direction of the linear bright area of the reflected light is the direction in which the linear linear bright area extends. Also, for example, if the shape of the linear bright area of the reflected light is curved, the longitudinal direction of the linear bright area of the reflected light is the direction in which the curved linear bright area extends.

For example, if the shape of the linear bright area of the reflected light is linear, the longitudinal direction of the linear bright area of the illumination light is the direction in which the linear linear bright area extends, as indicated by the symbol D1 in FIG. 3, for example. Also, if the shape of the linear bright area of the reflected light is curved, the longitudinal direction of the linear bright area of the reflected light is the direction in which the curved linear bright area extends, as indicated by the symbol D1 in FIG. 8, for example.

For example, when the reflected light has a lattice-point bright area, it is preferable that the size of the measurement area, i.e., the size of one pixel, is smaller than the period of the distortion of the reflected image. In this case, as described above, it is preferable that the size of the point-like bright area is larger than the period of the distortion of the reflected image. In such a case, the distortion of the reflected image can be represented by one point-like bright area.

3. First Processing Unit In the first processing unit according to the present disclosure, a plurality of selection points located on a line connecting measurement points of the light intensity of the reflected light are determined for each bright region in the light intensity distribution of the reflected light on the measured surface by using a predetermined light intensity threshold, and positions on the measured surface indicating the selection points are identified.

For example, when the reflected light has a linear bright region, the first processing unit preferably divides the light intensity distribution of the reflected light on the surface to be measured into n divided regions in the longitudinal direction of the linear bright region of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and, in the light intensity distribution of the reflected light of each divided region, determines, for each bright region, a number of selection points located on a line connecting the measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifies the positions on the surface to be measured that indicate the selection points. This makes it possible to increase the number of data (sample size), and to accurately measure the surface properties of the surface to be measured.

In the above case, when the light intensity distribution of the reflected light on the surface to be measured is a light intensity distribution obtained by dividing the surface to be measured into M x N measurement regions, N in the longitudinal direction of the linear bright region of the reflected light and M in the lateral direction of the linear bright region of the reflected light, and detecting the light intensity of the reflected light for each measurement region, the number of divided regions (n) is usually the same as the number of measurement regions (N) in the longitudinal direction of the linear bright region of the reflected light.

The number of divided regions (n) does not have to be the same as the number of measurement regions (N) in the longitudinal direction of the linear bright region of the reflected light. For example, the number of divided regions (n) may be 1/2 the number of measurement regions (N) in the longitudinal direction of the linear bright region of the reflected light. Specifically, when the measurement regions in the short direction of the linear bright region of the reflected light are rows and the measurement regions in the longitudinal direction of the linear bright region of the reflected light are columns, if the light intensity distribution of the reflected light is extracted for every other column, the number of divided regions (n) will be 1/2 the number of measurement regions (N) in the longitudinal direction of the linear bright region of the reflected light. In this case, the light intensity distribution of the even columns may be extracted, or the light intensity distribution of the odd columns may be extracted. For example, when the measurement areas in the short direction of the linear bright area of the reflected light are defined as rows and the measurement areas in the long direction of the linear bright area of the reflected light are defined as columns, if the light intensity distribution of the reflected light is extracted every two rows, the number of divided areas (n) is 1/3 of the number of measurement areas (N) in the long direction of the linear bright area of the reflected light, and if the light intensity distribution of the reflected light is extracted every three rows, the number of divided areas (n) is 1/4 of the number of measurement areas (N) in the long direction of the linear bright area of the reflected light. In this case, it is preferable to set the number of divided areas (n) so that the pitch of each divided area is smaller than the period of the distortion of the reflected image. If the pitch of the divided areas is larger than the period of the distortion of the reflected image, it may be difficult to sufficiently quantitatively evaluate the distortion of the reflected image.

For example, when the reflected light has a planar bright region, the first processing unit divides the light intensity distribution of the reflected light on the measured surface into n divided regions in any direction, extracts the light intensity distribution of the reflected light for each divided region, and, for each bright region in the light intensity distribution of the reflected light of each divided region, determines multiple selection points located on a line connecting the measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifies the positions on the measured surface indicating the selection points. The number of data (sample size) can be increased.

In the above case, the direction in which the light intensity distribution of the reflected light on the measured surface is divided into n divided regions is not particularly limited, and is appropriately set according to the shape of the planar bright region. For example, as shown in FIG. 9(a), when the planar shape of the planar bright region 21 is rectangular, the image of the reflected light on the measured surface can be cut out by a frame line 26 so that the dark region 22, the bright region 21, and the dark region 22 are arranged in order in the short direction D4 of the rectangular bright region 21 of the image of the reflected light on the measured surface. In this case, an image of the reflected light is obtained, for example, as shown in FIG. 9(b). Next, as shown in FIG. 9(b), the image of the reflected light may be divided into n divided regions A1 to An in the longitudinal direction D3 of the original rectangular bright region 21 shown in FIG. 9(a). Alternatively, for example, as shown in FIG. 9(a), if the planar shape of the planar bright region 21 is rectangular, the image of the reflected light on the measured surface can be cut out by a frame line 26 so that the dark region 22, the bright region 21, and the dark region 22 are arranged in order in the longitudinal direction D3 of the rectangular bright region 21 in the image of the reflected light on the measured surface. In this case, for example, an image of the reflected light as shown in FIG. 9(c) is obtained. Next, as shown in FIG. 9(c), the image of the reflected light may be divided into n divided regions A1 to An in the lateral direction D4 of the original rectangular bright region 21 shown in FIG. 9(a).

In the above case, the number of divided regions (n) can be the same as in the case where the reflected light has a linear bright region.

In addition, for example, when the reflected light has bright regions in the form of lattice points, the first processing unit extracts j partial regions including the bright regions from the light intensity distribution of the reflected light on the measured surface along the x direction of the lattice points of the bright regions of the reflected light, divides each partial region into k divided regions in the x direction of the lattice points of the bright regions of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and, in the light intensity distribution of the reflected light in each divided region, determines, for each bright region, multiple selection points located on lines connecting the measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifies positions on the measured surface indicating the selection points. Alternatively, the first processing unit extracts j partial regions including bright regions from the light intensity distribution of the reflected light on the measured surface along the y direction of the lattice points of the bright regions of the reflected light, divides each partial region into k divided regions in the y direction of the lattice points of the bright regions of the reflected light, extracts the light intensity distribution of the reflected light for each divided region, and, in the light intensity distribution of the reflected light of each divided region, determines, for each bright region, multiple selection points located on lines connecting the measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifies the positions on the measured surface indicating the selection points.

In the above case, as shown in FIG. 10(a), for example, first, j partial regions B1 to Bj including the bright region 21 can be extracted from the image of the reflected light on the measured surface along the y direction D6 of the lattice points of the bright region 21. In this case, the number of partial regions (j) is usually the same as the number of bright regions in the y direction D6 of the lattice points of the bright region. Next, as shown in FIG. 10(b), each partial region B1 to Bj can be divided into k divided regions C1 to Ck in the y direction D6 of the lattice points of the bright region of the reflected light. In this case, the number of divided regions (k) is usually the same as the number of measurement regions in the y direction D6 of the lattice points of the bright region in each partial region. Alternatively, as shown in FIG. 10(c), for example, first, j partial regions B1 to Bj including the bright region 21 can be extracted from the image of the reflected light on the measured surface along the x direction D5 of the lattice points of the bright region 21. In this case, the number of partial regions (j) is usually the same as the number of bright regions in the x-direction D5 of the lattice points of the bright regions. Then, as shown in FIG. 10(d), each partial region B1-Bj can be divided into k divided regions C1-Ck in the x-direction D5 of the lattice points of the bright regions of the reflected light. In this case, the number of divided regions (k) is usually the same as the number of measurement regions in the x-direction D5 of the lattice points of the bright regions in each partial region. In these cases, it is preferable that the pitch of the dot-like bright regions, i.e., the pitch of each partial region, is smaller than the period of the distortion of the reflected image. If the pitch of the dot-like bright regions is larger than the period of the distortion of the reflected image, it may be difficult to sufficiently quantitatively evaluate the distortion of the reflected image.

For example, when the light detection unit is an imaging device and the reflected light has a linear bright region, when the image of the reflected light obtained by the imaging device is divided into n divided regions in the longitudinal direction of the linear bright region of the reflected light, an angle adjustment process may be performed to adjust the angle of the image so that the longitudinal direction of the linear bright region of the reflected light is horizontal or vertical.

Specifically, first, in order to remove noise, the image of the reflected light obtained by the imaging device is subjected to smoothing and binarization in order. Examples of the smoothing method include the moving average method and the Gaussian filter method. Next, for one linear bright area, linear approximation is performed in the case of a linear bright area. In this case, linear approximation can be performed, for example, using data from one end in the longitudinal direction in one linear bright area after binarization. Examples of the linear approximation method include the least squares method. Next, in the case of linear approximation, the image is rotated so that the slope of the approximation line is 0. This makes it possible to make the longitudinal direction of the linear bright area of the reflected light vertical. Furthermore, by further rotating the image by 90 degrees, the longitudinal direction of the linear bright area of the reflected light can be made horizontal.

In the light intensity distribution of the reflected light of each divided region, a light intensity threshold can be set for each bright region. For example, when the reflected light has four linear bright regions as shown in Fig. 3, four light intensity thresholds T _a1 to T _d1 are set for each linear bright region 21a to 21d in the light intensity distribution of the reflected light of each divided region as shown in Fig. 5(a).

The light intensity threshold may be smaller than the peak value (maximum value) of the light intensity in the bright region and larger than the peak value (minimum value) of the light intensity in the dark region. In particular, it is preferable that the light intensity threshold is a light intensity value that is 10% to 90% of the peak value (maximum value) of the light intensity in the bright region, more preferably 30% to 70%, and particularly preferably 40% to 60%.

The first processing unit uses the light intensity threshold value to determine, for each bright region, a number of selection points located on a line connecting the measurement points of the light intensity of the reflected light in the light intensity distribution of the reflected light in each divided region.

We will use an example to explain how to determine the selection point using a light intensity threshold.

The first method of determining the selection point using the light intensity threshold is to divide, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and select, for each left light intensity distribution and right light intensity distribution of each bright region, a measurement point that shows a light intensity equal to or greater than the light intensity threshold and that is closest to the light intensity threshold, and set the measurement point as the selection point.

For example, Fig. 5A shows an example of the light intensity distribution of the reflected light of the divided region A1, in which the reflected light has a linear bright region. In the light intensity distribution of the reflected light of the divided region A1, the peak value of the light intensity is obtained for each of the linear bright regions 21a to 21d, and the light intensity values that are 50% of the peak value of the light intensity are set as the light intensity thresholds T _a1 to T _d1 . In the light intensity distribution of the reflected light of the divided region A1, the light intensity distribution of each of the linear bright regions 21a to 21d is divided into a left light intensity distribution L and a right light intensity distribution R with the peak value of each light intensity as the boundary. The selection point P _L1 is a measurement point in the left light intensity distribution L of the linear bright region 21a that indicates a light intensity that is equal to or greater than the light intensity threshold T _a1 or that exceeds the light intensity threshold T _a1 and is closest to the light intensity threshold T _a1 . Moreover, the selection point P _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _a1 and closest to _{the light intensity threshold T a1 in} the right light intensity distribution R of the linear bright region 21a. Similarly, the selection point Q _L1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _b1 and closest to the light intensity threshold T _b1 in the left light intensity distribution L _of the linear bright region 21b. Also, the selection point Q _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _b1 and closest to the light intensity threshold T _b1 in the right light intensity distribution R of the linear bright region 21b. Similarly, the selection point R _L1 is a measurement point showing a light intensity _that is equal to or greater than _the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 in the left light intensity distribution L of the linear bright region 21c. Moreover, the selection point R _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _c1 or exceeds the light intensity threshold T _c1 and is closest to the light intensity threshold T _c1 in the right-side light intensity distribution R of the linear bright region 21 c. Similarly, the selection point S _L1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _d1 or exceeds the light intensity threshold T _d1 and is closest to the light intensity threshold T _d1 in the left-side light intensity distribution L of the linear bright region 21 d. Moreover, the selection point S _R1 is a measurement point showing a light intensity that is equal to or greater than the light intensity threshold T _d1 or exceeds the light intensity threshold T _d1 and is closest to the light intensity threshold T _d1 in the right-side light intensity distribution R of the linear bright region 21 d.

The second method of determining the selection point using the light intensity threshold is to divide, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and select a measurement point in either the left light intensity distribution or the right light intensity distribution of each bright region that shows a light intensity equal to or greater than the light intensity threshold and closest to the light intensity threshold, and set the measurement point as the selection point.

For example, in the case of Fig. 5(a) described above, in the light intensity distribution of the reflected light of the divided region A1, the left light intensity distribution L of each linear bright region 21a-21d may be extracted, and only the selection points P _L1 , Q _L1 , R _L1 , and S _L1 , which are measurement points showing light intensities that are equal to or greater than the light intensity threshold or exceed the light intensity threshold and are closest to the light intensity threshold, may be used as the selection points. Also, in the case of Fig. 5(a) described above, the right light intensity distribution R of each linear bright region 21a-21d may be extracted, and only the selection points P R1 , Q _R1 , R _R1 , and S _R1 , which are measurement points showing light intensities that are equal to or greater than the light intensity threshold or exceed the light intensity threshold and are closest to the light intensity threshold _, may be used as the selection points.

A third method of determining a selection point using a light intensity threshold is to divide, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and to select, for each left light intensity distribution and right light intensity distribution of each bright region, a measurement point that shows a light intensity that is equal to or less than the light intensity threshold or that is closest to the light intensity threshold, and to set the measurement point as the selection point.

A fourth method of determining a selection point using a light intensity threshold is to divide, for each bright region, the light intensity distribution of the reflected light of each divided region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and select a measurement point in either the left light intensity distribution or the right light intensity distribution of each bright region that shows a light intensity that is equal to or less than the light intensity threshold or that is closest to the light intensity threshold, and set the measurement point as the selection point.

A fifth method of determining a selection point using a light intensity threshold is to divide, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and for each left light intensity distribution and right light intensity distribution of each bright region, select a measurement point that shows a light intensity closest to the light intensity threshold, and set the measurement point as the selection point. In this case, the measurement point that shows a light intensity closest to the light intensity threshold may be equal to or greater than the light intensity threshold, or may be equal to or less than the light intensity threshold.

For example, FIG. 11 shows an example of the light intensity distribution of the reflected light of the divided region A1, in which the reflected light has a linear bright region. In the light intensity distribution of the reflected light of the divided region A1, the peak value of the light intensity is obtained for each of the linear bright regions 21a to 21d, and the light intensity values that are 50% of the peak value of the light intensity are set as the light intensity thresholds T _a1 to T _d1 . In the light intensity distribution of the reflected light of the divided region A1, the light intensity distribution of each of the linear bright regions 21a to 21d is divided into a left light intensity distribution L and a right light intensity distribution R with the peak value of each light intensity as the boundary. The selection point P _L1 is a measurement point that shows the light intensity closest to the light intensity threshold T _a1 in the left light intensity distribution L of the linear bright region 21a. In addition, the selection point P _R1 is a measurement point that shows the light intensity closest to the light intensity threshold T _a1 in the right light intensity distribution R of the linear bright region 21a. Similarly, the selection point Q _L1 is a measurement point showing the light intensity closest to the light intensity threshold T _b1 in the left light intensity distribution L of the linear bright region 21b. Also, the selection point Q _R1 is a measurement point showing the light intensity closest to the light intensity threshold T _b1 in the right light intensity distribution R of the linear bright region 21b. Similarly, the selection point R _L1 is a measurement point showing the light intensity closest to the light intensity threshold T _c1 in the left light intensity distribution L of the linear bright region 21c. Also, the selection point R _R1 is a measurement point showing the light intensity closest to the light intensity threshold T _c1 in the right light intensity distribution R of the linear bright region 21c. Similarly, the selection point S _L1 is a measurement point showing the light intensity closest to the light intensity threshold T _d1 in the left light intensity distribution L of the linear bright region 21d. Moreover, the selected point S _R1 is a measurement point that indicates the light intensity closest to the light intensity threshold value T _d1 in the right-side light intensity distribution R of the linear bright region 21d.

A sixth method of determining a selection point using a light intensity threshold is to divide, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and select a measurement point in either the left light intensity distribution or the right light intensity distribution of each bright region that shows a light intensity closest to the light intensity threshold, and set the measurement point as the selection point. In this case as well, the measurement point that shows a light intensity closest to the light intensity threshold may be equal to or greater than the light intensity threshold, or may be equal to or less than the light intensity threshold.

For example, in the case of Fig. 11 described above, in the light intensity distribution of the reflected light of divided region A1, the left light intensity distribution L of each linear bright region 21a-21d may be extracted, and only the selection points P _L1 , Q _L1 , R _L1 , and S _L1, which are the measurement points showing the light intensity closest to the light intensity threshold, may be used as the selection points. Also, in the case of Fig. 11 described above, in the light intensity distribution of the reflected light of divided region A1, the right light intensity distribution R of each linear bright region 21a-21d may be extracted, and only the selection points P _R1 , Q _R1 , R _R1 , and S _R1, which are the measurement points showing the light intensity closest to the light intensity threshold, may be used as the selection points.

The seventh method of determining the selection point using the light intensity threshold is to divide the light intensity distribution of the reflected light of each divided region into a left light intensity distribution and a right light intensity distribution for each bright region, with the peak value of the light intensity of each bright region as the boundary, and select, for each left light intensity distribution and right light intensity distribution of each bright region, a point that is located on a line connecting a measurement point that shows a light intensity that is less than or equal to the light intensity threshold and is closest to the light intensity threshold, and a measurement point that is greater than or equal to the light intensity threshold and is closest to the light intensity threshold, and selects the point that shows the light intensity threshold as the selection point. In this case, the point that shows the light intensity threshold may or may not be a measurement point.

12 shows an example of the light intensity distribution of the reflected light of divided region A1, in which the reflected light has a linear bright region. In the light intensity distribution of the reflected light of divided region A1, the peak value of the light intensity is obtained for each of linear bright regions 21a-21d, and the light intensity values that are 50% of the peak values of the light intensity are set as light intensity thresholds T _a1 -T _d1 . In the light intensity distribution of the reflected light of divided region A1, the light intensity distribution of each of linear bright regions 21a-21d is divided into a left light intensity distribution L and a right light intensity distribution R, with the peak value of each light intensity as the boundary. The selection point P _L1 is _located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T _a1 and closest to the light intensity threshold T _a1 in the left light intensity distribution L of the linear bright region _21a and a measurement point showing a light intensity that is equal to or more than the light intensity threshold T _a1 and closest to the light intensity threshold T _a1 , and the selection point P _R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T _a1 and closest to the light intensity threshold T _a1 in the right light intensity distribution R of the linear bright region 21a and closest to the light intensity threshold T _a1 and the selection point P _R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T _a1 and closest to _{the light intensity threshold T a1 in} the right light intensity distribution R of the linear bright region 21a and closest to the light intensity threshold T _a1 . Similarly, the selection point Q _L1 is _{located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T b1 and} closest to the light intensity threshold T _b1 in the left light intensity distribution L of the linear bright region _21b and a measurement point showing a light intensity that is equal to or _more than the light intensity threshold T _b1 and closest to the light intensity threshold T _b1 , and is a point showing the light intensity threshold T _b1 . Also, the selection point Q _R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than _the light intensity threshold T _b1 and closest to _{the light intensity threshold T b1 in} the right light intensity distribution R of the linear bright region 21b and is a point showing the light intensity threshold T _{b1 .} Similarly, the selection point R _L1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 in the left light intensity distribution L of the linear bright region _{21c, and a measurement point showing a light intensity that is equal to or more than the light intensity threshold T c1 and} closest to the light intensity threshold T _c1 , and a selection point R _R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 in the right light intensity distribution R of the linear bright region 21c, and a measurement point showing a light intensity that is equal to or more than the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 , and a selection point R R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than _{the light intensity threshold T c1 and} closest to the light intensity threshold T _c1 in the right light intensity distribution R of the linear bright region 21c, and a measurement point showing a light intensity that is equal to or more than the light intensity threshold T _c1 and closest to the light intensity threshold T _c1 . Similarly, the selection point S _L1 is _{located on a line connecting a measurement point showing a light intensity that is equal to or less than the light intensity threshold T d1 and} closest to the light intensity threshold T _d1 in the left light intensity distribution L of the linear bright region _{21 d} and a measurement point showing a light intensity that is equal to or more than the light intensity threshold T _d1 and closest to the light intensity threshold T _d1 , _{and is a point showing the light intensity threshold T d1 .} Also, the selection point S _R1 is located on a line connecting a measurement point showing a light intensity that is equal to or less than _{the light intensity threshold T d1 and} closest to the light intensity threshold T _d1 in the right light intensity distribution R _of the linear bright region 21 d and is a point showing the light intensity threshold T _d1 .

The eighth method of determining the selection point using the light intensity threshold is a method in which, for each bright region in the light intensity distribution of the reflected light of each divided region, the light intensity distribution of the bright region is divided into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and a point that is located on a line connecting a measurement point that is less than the light intensity threshold or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that is greater than the light intensity threshold or exceeds the light intensity threshold and is closest to the light intensity threshold, in either the left light intensity distribution or the right light intensity distribution of each bright region, and that indicates the light intensity threshold, is selected, and the point that indicates the light intensity threshold is set as the selection point.

For example, in the case of Figure 12 mentioned above, in the light intensity distribution of reflected light in divided area A1, the left side light intensity distribution L of each linear bright area 21a to 21d can be extracted, and as selection points, only selection points P L1, Q L1, R L1, and S L1, which are points indicating the light intensity threshold and are located on a line connecting a measurement point indicating a light intensity that is below the light intensity threshold or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point indicating a light intensity that is above the light intensity _threshold or exceeds _{the light} intensity threshold and is closest to the light _{intensity threshold,} can be used. Also, for example, in the case of Figure 12 mentioned above, in the light intensity distribution of the reflected light of the divided area A1, the right-side light intensity distribution R of each linear bright area 21a to 21d can be extracted, and as the selection points, only selection points P R1, Q R1, R R1, and S R1, which are points indicating the light intensity threshold and are located on a line connecting a measurement point indicating a light intensity that is below the light intensity threshold or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point indicating a light intensity that is above the light intensity _threshold or exceeds _the light intensity _threshold and is closest to the light _{intensity threshold,} can be used.

The first processing unit identifies the position on the surface to be measured that indicates the selected point. For example, if the light detection unit is an imaging device, the position on the image of the reflected light (position of the pixel) corresponds to the position on the surface to be measured, so it is sufficient to identify the position on the image of the reflected light that indicates the selected point.

4. Second Processing Unit In the second processing unit according to the present disclosure, a graph is created for each bright region, in which the positions on the measurement surface indicating the selected points are plotted, with the horizontal axis representing a first direction on the measurement surface and the vertical axis representing a second direction on the measurement surface perpendicular to the first direction.

For example, if the light detection unit is an imaging device, the position on the reflected light image (position of the pixel) corresponds to the position on the surface to be measured. Therefore, for example, if the reflected light has a linear bright area, a graph can be created in which the positions on the reflected light image (position of the pixel) indicating the above-mentioned selection point are plotted, with the longitudinal direction of the linear bright area of the reflected light image on the horizontal axis and the lateral direction of the linear bright area of the reflected light image on the vertical axis. In this case, the longitudinal direction D1 of the linear bright area of the reflected light image corresponds to the first direction on the surface to be measured, and the lateral direction D2 of the linear bright area of the reflected light image corresponds to the second direction on the surface to be measured.

When the first processing unit divides the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution for each bright region with the peak value of the light intensity of each bright region as the boundary, and when a selection point is determined for each left light intensity distribution and right light intensity distribution of each bright region, the second processing unit may create a graph plotting the positions on the measured surface indicating the selection point for each bright region and for each left light intensity distribution and right light intensity distribution, or may create a graph plotting the positions on the measured surface indicating the selection point for each bright region.

For example, Fig. 6(a) is a graph plotting positions on the image of reflected light indicating selection points P _L1 to P _Ln (positions of pixels) for the left light intensity distribution L of the linear bright region 21a in the light intensity distribution of reflected light in each divided region A1 to An as shown in Figs. 5(a) to 5(b), 11 and 12. In the above case, when creating a graph plotting positions on the measured surface indicating the above selection points for each linear bright region and for each left light intensity distribution and right light intensity distribution, a graph such as that shown in Fig. 6(a) is obtained.

Also, for example, Fig. 13(a) is a graph plotting positions on the image of reflected light indicating selection points P _L1 to P _Ln and P _R1 to P _Rn for linear bright region 21a in the light intensity distribution of reflected light in each divided region A1 to An as shown in Figs. 5(a) to 5(b), 11 and 12. In the above case, when creating a graph plotting positions on the measurement surface indicating the selection points for each linear bright region, a graph such as that shown in Fig. 13(b) is obtained.

In FIG. 6(a) and FIG. 13(a), the horizontal axis represents the pixel position in the longitudinal direction D1 of the linear bright area of the reflected light image, and the vertical axis represents the pixel position in the lateral direction D2 of the linear bright area of the reflected light image.

In addition, in the case where the first processing unit divides the light intensity distribution of the bright region for each bright region into a left light intensity distribution and a right light intensity distribution with the peak value of the light intensity of each bright region as the boundary, and a selection point is determined in either the left light intensity distribution or the right light intensity distribution of each bright region, the second processing unit may create a graph that plots the position on the measured surface indicating the selection point for each bright region.

5. Third Processing Unit The third processing unit in the present disclosure determines, for each bright region, a reference line based on the shape of the bright region of the illumination light.

We will explain how to find the baseline using an example.

The first method of determining the reference line is to determine an approximation formula from the points plotted on the graph created by the second processing unit, and use the approximation formula as the reference line. The approximation is based on the shape of the bright area of the illumination light. For example, if the bright area is linear and straight, linear approximation can be performed. Also, for example, if the bright area is planar and square or rectangular, linear approximation can be performed. Also, for example, if the bright area is lattice-point shaped and square or rectangular, linear approximation can be performed. Examples of linear approximation methods include the first square method and the gradient descent method. Also, for example, if the bright area is linear and curved, curve approximation can be performed. Also, for example, if the bright area is planar and circular or elliptical, curve approximation can be performed. Examples of curve approximation methods include polynomial regression. The coefficients of the polynomial regression can be determined by the least squares method, for example.

In the first method, when the first processing unit divides the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution for each bright region with the peak value of the light intensity of each bright region as a boundary, and when a selection point is determined for each left light intensity distribution and right light intensity distribution of each bright region, the second processing unit creates a graph plotting the positions on the measured surface indicating the selection point for each bright region and for each left light intensity distribution and right light intensity distribution, and obtains a reference line for each bright region and for each left light intensity distribution and right light intensity distribution. For example, when the plotted points in the graph shown in FIG. 6(b) are linearly approximated, a linear approximation formula (solid line in FIG. 6(b)) is obtained, and this linear approximation formula can be used as the reference line.

In the first method, when the first processing unit divides the light intensity distribution of the bright region into a left light intensity distribution and a right light intensity distribution for each bright region with the peak value of the light intensity of each bright region as the boundary, and when a selection point is determined for each left light intensity distribution and right light intensity distribution of each bright region, the second processing unit creates a graph plotting the positions on the measured surface indicating the selection point for each bright region, and obtains a reference line for each bright region. For example, when the plotted points in the graph shown in FIG. 13(b) are linearly approximated, a linear approximation formula (solid line in FIG. 13(b)) is obtained, and this linear approximation formula can be used as the reference line.

The second method of determining the reference line is to use a smooth reference substrate having a predetermined arithmetic mean roughness Ra, and to determine the reference line based on the shape of the bright area of reflected light on the surface of the reference substrate. The method of determining the reference line is the same as the above-mentioned first method of determining the reference line when the surface to be measured is the surface of the reference substrate. Specifically, by using the surface to be measured as the surface of the reference substrate and passing through the above-mentioned irradiation unit, light detection unit, first processing unit, and second processing unit, a graph can be created when the surface to be measured is the surface of the reference substrate.

The arithmetic mean roughness Ra of the surface of the reference substrate is, for example, preferably 1 nm or less, more preferably 0.6 nm or less, and even more preferably 0.3 nm or less.

Here, the arithmetic mean roughness Ra of the surface of the reference substrate can be measured using an atomic force microscope (AFM) or a white light interferometer.

The reference substrate may be any substrate that satisfies the above flatness, and may be, for example, a glass substrate. Specifically, a glass substrate for flat panel displays may be used. For example, the alkali-free glass substrate "OA-10G" manufactured by Nippon Electric Glass Co., Ltd. has an arithmetic mean roughness Ra of 0.2 nm and is highly smooth.

6. Fourth Processing Unit A fourth processing unit in the present disclosure obtains, for each of the bright regions, a difference between the point plotted on the graph and the reference line, and quantifies the variation in the difference for each of the bright regions.

As the variation of the difference, for example, standard deviation, variance, deviation value, mean absolute deviation, etc. can be used. Among them, standard deviation is preferable.

In addition, for example, when the difference is calculated for each bright region and for each left light intensity distribution and right light intensity distribution, when quantifying the variation in the difference for each bright region, the variation in the difference can be calculated for each bright region and for each left light intensity distribution and right light intensity distribution. In this case, the arithmetic mean value of the variation in the difference for each bright region and for each left light intensity distribution and right light intensity distribution is calculated, and this arithmetic mean value of the variation in the difference for each bright region and for each left light intensity distribution and right light intensity distribution can be set as the variation in the difference for all bright regions.

Furthermore, for example, when the difference is calculated for each bright region and for either the left light intensity distribution or the right light intensity distribution, when quantifying the variation in the difference for each bright region, the variation in the difference can be calculated for each bright region and for either the left light intensity distribution or the right light intensity distribution. In this case, the arithmetic mean value of the variation in the difference for each bright region and for either the left light intensity distribution or the right light intensity distribution is calculated, and this arithmetic mean value of the variation in the difference for each bright region and for either the left light intensity distribution or the right light intensity distribution can be set as the variation in the difference for all bright regions.

The surface texture measuring device according to the present disclosure may have a fifth processing unit 31 that determines the maximum value of the light intensity in a bright region and the minimum value of the light intensity in a dark region in the light intensity distribution of reflected light from the surface to be measured, as shown in FIG. 14, and calculates image clarity by the following formula (1).
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.)

Image clarity is the degree to which the reflected image of the measured surface appears clear and undistorted. By quantifying the variation in the above difference, it is possible to measure image clarity and quantitatively evaluate the degree of distortion of the reflected image of the measured surface.

The maximum light intensity of the bright region and the minimum light intensity of the dark region may be the maximum light intensity of the bright region and the minimum light intensity of the dark region in the entire light intensity distribution of the reflected light on the surface to be measured, or may be the maximum light intensity of the bright region and the minimum light intensity of the dark region in the light intensity distribution of the reflected light of one divided region.

8. Display Unit The surface texture measuring device according to the present disclosure may have a display unit 32 that displays the numerical value of the variation in the difference, as shown in Fig. 14. The display unit can also display the image clarity.

9. Uses The uses of the surface texture measuring device of the present disclosure are not particularly limited. The surface texture measuring device of the present disclosure can be used to measure the surface texture of, for example, a coating, a film, a sheet, a painted surface, a processed surface, etc. In particular, the surface texture measuring device of the present disclosure is preferably used to measure the surface texture of an optical film, and more preferably used to measure the surface texture of a surface member of a display device.

B. Surface Texture Measurement Method The surface texture measurement method in the present disclosure includes an irradiation step of irradiating a measurement surface with illumination light having one or more bright regions and dark regions using a light source, a light detection step of focusing the light source through the measurement surface, receiving reflected light having one or more bright regions and dark regions corresponding to the bright regions and the dark regions of the illumination light, and detecting a light intensity distribution of the reflected light on the measurement surface, and detecting a light intensity distribution of the reflected light on the measurement surface using a predetermined light intensity threshold for each bright region in the light intensity distribution of the reflected light on the measurement surface. a first processing step of determining a plurality of selection points to be measured and specifying positions on the surface to be measured indicating the selection points; a second processing step of creating, for each of the bright regions, a graph in which the positions on the surface to be measured indicating the selection points are plotted, with a first direction on the surface to be measured as the horizontal axis and a second direction on the surface to be measured that is perpendicular to the first direction as the vertical axis; a third processing step of determining, for each of the bright regions, a reference line based on a shape of the bright region of the illumination light; and a fourth processing step of determining, for each of the bright regions, a difference between the points plotted on the graph and the reference line and quantifying a variation in the difference for each of the bright regions.

15 is a process diagram illustrating the surface texture measurement method of the present disclosure, for example, a process diagram showing the surface texture measurement method performed by the surface texture measurement device 10 shown in FIG. 1. As shown in FIG. 1 and FIG. 15, first, in the irradiation process S11, a light source is used to irradiate the measured surface 1 with illumination light L1 having one or more bright and dark regions. Next, in the light detection process S12, the light detection unit 5 focuses on the light source 3 through the measured surface 1, receives reflected light L2 having one or more bright and dark regions that are reflected by the measured surface 1 and correspond to the bright and dark regions of the illumination light L1, and detects the light intensity distribution of the reflected light L2 on the measured surface 1. Next, in the first processing process S13, in the light intensity distribution of the reflected light L2 on the measured surface 1, a predetermined light intensity threshold is used to determine a plurality of selection points located on a line connecting the measurement points of the light intensity of the reflected light L2 for each bright region, and the position on the measured surface 1 indicating the selection point is specified. Next, in the second processing step S14, a graph is created in which the positions on the measurement surface 1 indicating the selected points are plotted for each bright area, with the horizontal axis representing a first direction on the measurement surface 1 and the vertical axis representing a second direction on the measurement surface 1 perpendicular to the first direction. Next, in the third processing step S15, a reference line is found for each bright area based on the shape of the bright area of the illumination light L1. Next, in the fourth processing step S16, the difference between the points plotted on the graph and the reference line is found for each bright area, and the variation in the difference is quantified for each bright area.

In the surface texture measurement method disclosed herein, similar to the surface texture measurement device described above, the distortion of the reflected image of the measured surface can be quantified, for example as the standard deviation of the above difference, and quantitatively evaluated.

Each step in the surface texture measurement method disclosed herein is explained below.

1. Illumination Step In the light detection step of the present disclosure, a light source is used to irradiate the surface to be measured with illumination light having one or more bright and dark regions.

The lighting process can be the same as that described in the section on the lighting section of the surface texture measuring device above.

In an object to be measured having a surface to be measured, a light-shielding layer may be disposed on the surface of the object to be measured opposite the surface to be measured. The light-shielding layer can suppress backside reflection. The light-shielding layer is not particularly limited, and for example, a black resin film or a blackboard can be used. In addition, a black resin composition may be applied to the surface of the object to be measured opposite the surface to be measured to form a light-shielding layer.

2. Light Detection Step In the light detection step of the present disclosure, a light source is focused through the surface to be measured, reflected light having one or more bright and dark regions corresponding to the bright and dark regions of the illumination light is received, and the light intensity distribution of the reflected light on the surface to be measured is detected.

The optical detection process can be the same as that described above in the section on the optical detection unit of the surface texture measuring device.

3. First Processing Step In the first processing step of the present disclosure, a predetermined light intensity threshold is used to determine, for each bright region in the light intensity distribution of reflected light on the measured surface, a number of selection points located on a line connecting measurement points of the light intensity of reflected light, and the positions on the measured surface indicating the selection points are identified.

The first processing step can be the same as that described in the section on the first processing section of the surface texture measuring device above.

4. Second Processing Step In the second processing step of the present disclosure, a graph is created for each bright region, in which the positions on the measurement surface indicating the selected points are plotted, with the horizontal axis representing a first direction on the measurement surface and the vertical axis representing a second direction on the measurement surface that is perpendicular to the first direction.

The second processing step can be the same as that described in the section on the second processing section of the surface texture measuring device above.

5. Third Processing Step In the third processing step according to the present disclosure, a reference line based on the shape of the bright region of the illumination light is obtained for each bright region.

The third processing step can be the same as that described in the section on the third processing section of the surface texture measuring device above.

6. Fourth Processing Step In the fourth processing step according to the present disclosure, the difference between the point plotted on the graph and the reference line is found for each bright region, and the variation in the difference is quantified for each bright region.

The fourth processing step can be the same as that described in the section on the fourth processing section of the surface texture measuring device mentioned above.

7. Fifth Processing Step The surface texture measuring method disclosed herein may include a fifth processing step S17 of determining the maximum value of light intensity in a bright region and the minimum value of light intensity in a dark region in the light intensity distribution of reflected light on the measurement surface, as shown in FIG. 16 , for example, and calculating image clarity by the following formula (1).
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.)

The fifth processing step can be the same as that described in the section on the fifth processing section of the surface texture measuring device mentioned above.

8. Applications The applications of the surface texture measuring method of the present disclosure are similar to those of the surface texture measuring device described above.

This disclosure is not limited to the above-mentioned embodiments. The above-mentioned embodiments are merely examples, and anything that has substantially the same configuration as the technical ideas described in the claims of this disclosure and provides similar effects is included within the technical scope of this disclosure.

The present disclosure will be explained in detail below with examples and comparative examples.

[Production Example 1]
The glass substrate was Gorilla Glass 3 manufactured by Corning Incorporated. A black PET film was placed on the back surface of the glass substrate. This was used as Sample 1. The surface to be measured of Sample 1 was the front surface of the glass substrate.

[Production Example 2]
The glass substrate was Corning Gorilla Glass 3. A TAC film (FUJIFILM Fuji TAC TG60UL) was placed on one side of the glass substrate via an optically transparent adhesive sheet (OCA, Lintec NCF-D692, thickness 5 μm). A black PET film was placed on the other side of the glass substrate. This was sample 2. The surface to be measured of sample 2 was the surface of the TAC film.

[Production Example 3]
The glass substrate was Corning Gorilla Glass 3. An optically transparent adhesive sheet (OCA, Lintec's NCF-D692, thickness 5 μm) was placed on one side of the glass substrate. A black PET film was placed on the other side of the glass substrate. This was sample 3. The surface to be measured of sample 3 was the surface of the optically transparent adhesive sheet.

[Production Example 4]
The foldable smartphone Galaxy Z Fold2 manufactured by Samsung Electronics was used as sample 4.

[Example]
(1) Configuration of the Surface Texture Measuring Device An EELM-SKY-300-W manufactured by Ecorica was used as the OLED light source. A mask having a metal plate and rectangular and cross-shaped openings penetrating the metal plate was used. The mask had a transmission region consisting of four rectangular openings and a transmission region consisting of three cross-shaped openings. The rectangular transmission region had a length of 70 mm, a line width of 0.5 mm, and a pitch of 1.0 mm. The mask was directly attached to the OLED light source.

The camera used was a Nikon D5600 digital single-lens reflex camera. The lens used was a Nikon AF-P DX NIKKOR 18-55mm f/3.5-5.6G VR. The camera settings were aperture f/22, exposure time 1/8 sec, ISO-100, and focal length 55mm.

The distance between the light source and the surface to be measured was 33 cm, and the angle of incidence of the illumination light emitted from the illumination unit was 60°. The distance between the camera and the surface to be measured was 33 cm, and the angle of reception of the reflected light by the imaging device was 60°.

(2) Illumination and light detection The light source was focused by autofocusing the camera on the cross part of the reflected image of the measured surface. The measured surface was irradiated with light from the light source through a mask, and the camera captured an image of the reflected light from the measured surface.

(3) Processing For the images captured by the camera, a moving average was taken over 50 pixels in length and width, and the images were binarized with a threshold value of maximum value/1.3 to remove noise. A straight line was approximated by the least squares method for the leftmost cell of each row, and the image was rotated so that the slope of the approximated line was 0. This adjusted the angle of the image.

In addition, 1100 pixels were cut out in the longitudinal direction of the linear bright area and 500 pixels in the lateral direction of the linear bright area from the center of the image captured by the camera. The longitudinal direction of the linear bright area was set as the column direction, and the lateral direction of the linear bright area was set as the row direction, and the light intensity was calculated for each pixel. In the light intensity distribution of each column, the light intensity value of 50% of the peak value (maximum value) of the light intensity of the bright area was set as the light intensity threshold for each linear bright area and for each left light intensity distribution and right light intensity distribution. In the light intensity distribution of each column, the measurement point that was equal to or greater than the light intensity threshold and closest to the light intensity threshold for each linear bright area and for each left light intensity distribution and right light intensity distribution was set as the selection point. The position of the pixel indicating the selection point was identified. For each linear bright area and for each left light intensity distribution and right light intensity distribution, a graph was created in which the position of the pixel indicating the selection point was plotted with the position of the pixel in the longitudinal direction of the linear bright area as the horizontal axis and the position of the pixel in the lateral direction of the linear bright area as the vertical axis. The points plotted on the graph were linearly approximated using the least squares method, and the linear approximation equation was used as the reference line. The difference between the points plotted on the graph and the reference line was calculated for each linear bright area and for each left light intensity distribution and right light intensity distribution. Then, the standard deviation of the above differences was calculated for each linear bright area and for each left light intensity distribution and right light intensity distribution. Next, the arithmetic mean value of the standard deviation of the above differences for each linear bright area and for each left light intensity distribution and right light intensity distribution was calculated. This arithmetic mean value was used as the distortion of the reflected image of the measured surface.

The results for samples 1 to 4 are shown in Table 1.

[Comparative Example]
(1) Configuration of Surface Texture Measuring Apparatus The configuration of the surface texture measuring apparatus was the same as in Example 1.

(2) Illumination and Light Detection Illumination and light detection were performed in the same manner as in Example 1.

(3) Processing From the center of the image captured by the camera, 1100 pixels were cut in the longitudinal direction of the linear bright area corresponding to the longitudinal direction of the bright area of the illumination light, and 500 pixels were cut in the lateral direction of the linear bright area. The longitudinal direction of the linear bright area was set as the column direction, and the lateral direction of the linear bright area was set as the row direction, and the light intensity was calculated for each pixel. The pitch of the bright area was calculated for each column. In this case, the peak value of the light intensity of the bright area was calculated, and the distance between the peak values of the light intensity of adjacent bright areas was defined as the pitch of the bright area. The standard deviation δ of the data of three points was calculated for each column. Then, the arithmetic average value of the standard deviation δ of the pitch of each column was calculated.

The comparative example mimics the method using the flatness measuring device described in Patent Document 1.

[Reference example]
Instead of using a camera, the reflected light on the surface to be measured was observed visually and evaluated according to the following criteria.
A: No distortion of the reflected image B: Medium distortion of the reflected image C: Large distortion of the reflected image

In the comparative example, the evaluation of sample 3 was the same as that of sample 1, and the distortion of the reflected image could not be adequately evaluated numerically. On the other hand, in the working example, similar to the reference example, the evaluations of samples 2 to 4 all differed from that of sample 1, and the distortion of the reflected image could be evaluated numerically. Sample 3 has long-period distortion. From these results, it was confirmed that the method disclosed herein can also numerically evaluate long-period distortion.

REFERENCE SIGNS LIST 1: surface to be measured 2: illumination unit 3: light source 4: mask 5: light detection unit 6: first processing unit 7: second processing unit 8: third processing unit 9: fourth processing unit 10: surface texture measuring device

Claims (18)

an illumination unit having a light source and configured to illuminate a surface to be measured with illumination light having one or more bright and dark regions;
a light detection unit that focuses the light source through the measurement surface, receives reflected light that is reflected by the measurement surface and has one or more bright and dark regions corresponding to the bright and dark regions of the illumination light, and detects a light intensity distribution of the reflected light on the measurement surface;
a first processing unit that determines, for each bright region in the light intensity distribution of the reflected light on the measurement surface, a plurality of selection points located on a line connecting measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifies positions on the measurement surface indicating the selection points;
a second processing unit that creates a graph in which positions on the measurement surface indicating the selected points are plotted, for each of the bright regions, with a first direction on the measurement surface as a horizontal axis and a second direction on the measurement surface perpendicular to the first direction as a vertical axis; and
a third processing unit that determines, for each of the bright regions, a reference line based on a shape of the bright region of the illumination light;
a fourth processing unit that calculates a difference between a point plotted on the graph and the reference line for each of the bright regions and quantifies a variation in the difference for each of the bright regions;
A surface texture measuring device comprising: The surface texture measuring device according to claim 1, wherein the light detection unit is an imaging device. The surface texture measuring device according to claim 1 or 2, wherein the illumination light has a linear bright region as the bright region, and the reflected light has a linear bright region as the bright region that corresponds to the linear bright region of the illumination light. the light detection unit divides the measurement surface into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects a light intensity distribution of the reflected light on the measurement surface;
The first processing unit divides a light intensity distribution of the reflected light on the measurement surface into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and extracts a light intensity distribution of the reflected light for each of the divided regions;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a measurement point in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions that is equal to or greater than the light intensity threshold and closest to the light intensity threshold, or selecting a measurement point in each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions that is equal to or greater than the light intensity threshold and closest to the light intensity threshold;
The surface texture measuring device according to claim 3 , wherein the measurement point is the selected point. the light detection unit divides the measurement surface into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects a light intensity distribution of the reflected light on the measurement surface;
The first processing unit divides a light intensity distribution of the reflected light on the measurement surface into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and extracts a light intensity distribution of the reflected light for each of the divided regions;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a measurement point showing a light intensity closest to the light intensity threshold in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions, or selecting a measurement point showing a light intensity closest to the light intensity threshold for each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions;
The surface texture measuring device according to claim 3 , wherein the measurement point is the selected point. the light detection unit divides the measurement surface into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, detects the light intensity of the reflected light for each measurement region, and detects a light intensity distribution of the reflected light on the measurement surface;
The first processing unit divides a light intensity distribution of the reflected light on the measurement surface into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and extracts a light intensity distribution of the reflected light for each of the divided regions;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or more than the light intensity threshold and is closest to the light intensity threshold, in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions, or selecting a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or more than the light intensity threshold and is closest to the light intensity threshold, in each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions,
The surface texture measuring device according to claim 3 , wherein the selected point is a point that indicates the light intensity threshold value. The surface texture measuring device according to any one of claims 1 to 6, wherein the third processing unit determines an approximation from the points plotted on the graph, and the approximation is used as the reference line. The surface texture measuring device according to any one of claims 1 to 7, wherein the fourth processing unit calculates a standard deviation of the difference between the points plotted on the graph and the reference line. 9. The surface texture measuring device according to claim 1, further comprising a fifth processing unit that obtains a maximum value of light intensity in the bright region and a minimum value of light intensity in the dark region in a light intensity distribution of the reflected light on the measured surface, and calculates image clarity by the following formula (1):
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.) an illumination step of irradiating the measurement surface with illumination light having one or more bright and dark regions using a light source;
a light detection step of focusing the light source through the measurement surface, receiving reflected light reflected by the measurement surface and having one or more bright and dark regions corresponding to the bright and dark regions of the illumination light, and detecting a light intensity distribution of the reflected light on the measurement surface;
a first processing step of determining, for each bright region in the light intensity distribution of the reflected light on the measurement surface, a plurality of selection points located on a line connecting measurement points of the light intensity of the reflected light using a predetermined light intensity threshold, and identifying positions on the measurement surface indicating the selection points;
a second processing step of creating a graph for each of the bright regions, the graph being a plot of positions on the measurement surface that indicate the selected points, with a first direction on the measurement surface as the horizontal axis and a second direction on the measurement surface that is perpendicular to the first direction as the vertical axis;
a third processing step of determining, for each bright region, a reference line based on a shape of the bright region of the illumination light;
a fourth processing step of calculating a difference between the points plotted on the graph and the reference line for each of the bright regions, and quantifying the variation in the difference for each of the bright regions;
The surface texture measuring method according to claim 1, The surface texture measurement method according to claim 10, wherein the light detection step uses an imaging device. The surface texture measurement method according to claim 10 or 11, wherein the illumination light has a linear bright region as the bright region, and the reflected light has a linear bright region as the bright region corresponding to the linear bright region of the illumination light. In the light detection step, the measurement surface is divided into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light on the measurement surface is detected by detecting a light intensity of the reflected light for each measurement region;
In the first processing step, a light intensity distribution of the reflected light on the measurement surface is divided into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light for each divided region is extracted;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a measurement point in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions that is equal to or greater than the light intensity threshold and closest to the light intensity threshold, or selecting a measurement point in each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions that is equal to or greater than the light intensity threshold and closest to the light intensity threshold;
The surface texture measuring method according to claim 12 , wherein the measurement point is the selected point. In the light detection step, the measurement surface is divided into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light on the measurement surface is detected by detecting a light intensity of the reflected light for each measurement region;
In the first processing step, a light intensity distribution of the reflected light on the measurement surface is divided into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light for each divided region is extracted;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a measurement point showing a light intensity closest to the light intensity threshold in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions, or selecting a measurement point showing a light intensity closest to the light intensity threshold for each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions;
The surface texture measuring method according to claim 12 , wherein the measurement point is the selected point. In the light detection step, the measurement surface is divided into M×N measurement regions, N of which are in a longitudinal direction of the linear bright region of the reflected light and M of which are in a lateral direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light on the measurement surface is detected by detecting a light intensity of the reflected light for each measurement region;
In the first processing step, a light intensity distribution of the reflected light on the measurement surface is divided into n divided regions in a longitudinal direction of the linear bright region of the reflected light, and a light intensity distribution of the reflected light for each divided region is extracted;
dividing, for each linear bright region, the light intensity distribution of the reflected light in each of the divided regions into a left light intensity distribution and a right light intensity distribution with a peak value of the light intensity of the linear bright region as a boundary;
selecting a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or more than the light intensity threshold and is closest to the light intensity threshold, in either one of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions, or selecting a point that is located on a line connecting a measurement point that indicates a light intensity that is equal to or less than the light intensity threshold and is closest to the light intensity threshold, and a measurement point that indicates a light intensity that is equal to or more than the light intensity threshold and is closest to the light intensity threshold, in each of the left light intensity distribution and the right light intensity distribution of each of the linear bright regions,
The method of measuring surface texture according to claim 12 , wherein the selected point is a point that indicates the light intensity threshold value. The surface texture measurement method according to any one of claims 10 to 15, wherein in the third processing step, an approximation formula is found from the points plotted on the graph, and the approximation formula is used as the reference line. The surface texture measurement method according to any one of claims 10 to 16, wherein the fourth processing step calculates the standard deviation of the difference between the points plotted on the graph and the reference line. 18. A surface texture measuring method according to claim 10, further comprising a fifth processing step of determining a maximum value of light intensity in the bright region and a minimum value of light intensity in the dark region in a light intensity distribution of the reflected light on the measured surface, and calculating image clarity by the following formula (1):
DOI=(M-m)/(M+m)×100 (1)
(In the above formula, DOI is image clarity, M is the maximum value of light intensity in the bright area, and m is the minimum value of light intensity in the dark area.)