CN116661138B - An optical design method for grating spectrometer - Google Patents

Abstract

The present invention relates to the field of optical design technology, and in particular to an optical design method for a grating spectrometer, comprising the steps of: S1, generating an initial parent population; S2, generating a progeny population based on crossover mutation of the initial parent population; the crossover mutation comprises sampling characteristic light rays, and assigning a value to each of the sampled light rays; S3, performing system reconstruction based on the progeny population; the system reconstruction comprises at least one of line distribution reconstruction, record structure reconstruction and grating surface reconstruction; S4, performing optimized iterative design using a genetic algorithm, and determining and selecting a new round of parent populations based on the system reconstruction; the design method of the present invention does not require the construction of a complex aberration expansion model, thereby reducing the manpower work of designers; the entire design process does not have the problem of neglecting high-order expansion, and the design result is more accurate; the design can be carried out for any type of spectrometer, and the design is more universal.

Description

An optical design method for grating spectrometer

Technical Field

The present invention relates to the field of optical design technology, and in particular to an optical design method of a grating spectrometer, a computer device capable of executing the method, and a computer-readable storage medium.

Background technique

Spectrometers can accurately analyze the wavelength-energy ratio of complex light beams and are widely used in metal smelting, biochemical analysis, environmental monitoring, and component identification. Among them, grating spectrometers have the advantages of dispersion linearity and high diffraction efficiency, so they are widely used in various spectrometers as the core spectroscopic element. The types of spectrometers with gratings as the core spectroscopic element mainly include CT and cross-CT structure spectrometers using plane gratings, Rowland circle structures and type III and IV grating spectrometer structures using concave gratings, Offner structure imaging spectrometers using convex gratings, and some free-form surface grating spectrometers used in special fields; the schematic diagrams of several typical spectrometer system structures are shown in Figures 1 to 3.

For the design of spectrometers, the traditional method is to construct an aberration model for the target system. Generally, methods such as vector aberration theory and optical path function aberration theory are used, and the aberration coefficients in the aberration model are optimized with the optimization algorithm. Since these aberration models need to ignore certain expansion items during the construction process, there is an inherent expansion residual error between the model and the real light path, and the final design result is different from the real result, which requires further analysis and verification; at the same time, this design method also puts forward requirements on the design experience of designers. The accumulated design experience of designers can help the design process to obtain design results that meet the design indicators more quickly and efficiently. However, with the requirements for higher spectral resolution indicators proposed by modern spectral analysis and new spectrometer structures with different uses, traditional design methods inevitably have problems such as long design cycle and high design difficulty when solving these new problems. Therefore, an efficient and fast design method for grating spectrometers is very necessary for the development of higher performance spectrometers.

Many studies have been conducted at home and abroad on the design of grating spectrometers, and a variety of solutions have been proposed:

The paper "Analytical representation of spot diagrams and its application to the design of monochromators" presents a typical optical path function aberration theory for the design of concave grating spectrometers. The spectrometer design is achieved by building a system aberration model and optimizing the system parameters. This method selectively ignores the high-order terms when building the aberration expansion model, so the final design result is different from the actual tracking result, and the design accuracy is low.

The paper "Design Ideas for Aberration-Correcting Convex Master Gratings of Flat-Field Concave Gratings for Spectrometers" presents a method for realizing a concave grating spectrometer by designing a recording structure, and also uses an aberration model for optimization.

The paper "Anti-Aberration Design of Variable-Pitch Convex Grating Imaging Spectrometer" proposes a design method based on collective ray tracing analysis of point diagrams combined with the gradient descent method to calculate the line distribution of convex variable-pitch gratings. This tracing method does not have any unfolding residual error; however, it is necessary to select several sampling wavelengths and assign corresponding weight factors, and repeatedly adjust the weights according to the design results during the entire design process, which makes it impossible to achieve optimization of the entire field of view and the entire band.

The paper "Imaging spectrometer with single component of freeform concave grating" proposes a design method for a freeform grating spectrometer, using ray tracing to reconstruct the grating surface shape and line distribution. However, it does not optimize the spectrometer system parameters, and lacks a balanced optimization design strategy for the complete working field of view and performance within the band.

Summary of the invention

In order to solve the above problems, the present invention provides an optical design method of a grating spectrometer using ray tracing as the core, which realizes fast and efficient grating spectrometer design by reversely deducing and reconstructing grating features based on the spectrometer structure.

The present invention provides an optical design method for a grating spectrometer, the optical design method comprising the steps of:

S1. Generate an initial parent population by setting the system parameter optimization center and setting the system parameter optimization range;

S2. generating a progeny population based on the crossover mutation of the initial parent population; the crossover mutation includes sampling characteristic light rays, and assigning a random wavelength and a random field of view to each of the sampled light rays;

S3, based on the offspring population, performing system reconstruction; the system reconstruction includes at least one of the following: line distribution reconstruction, record structure reconstruction, and grating surface reconstruction;

S4, using a genetic algorithm to perform an optimized iterative design, and based on the system reconstruction, determine and select a new round of parent populations;

If the parent population does not meet the design goal, the initial parent population is replaced by the parent population, and S2 to S4 are iterated in a loop until the parent population meets the design goal, and then the design result is output;

If the parent population meets the design goal, the iteration is terminated and the design result is output.

In some implementations, the system parameters include working band, working field of view and aperture parameters; the assignment includes assigning a random field of view, an assigned wavelength, an assigned aperture and an assigned weight factor.

In some embodiments, generating an initial parent population includes setting a population size, setting an optimization strategy, and setting an iteration control.

In some implementations, the system reconstruction is a scoreline distribution reconstruction, and the system reconstruction comprises the steps of:

S311, randomly sampling a number of feature points on the incident slit of the grating spectrometer, assigning random wavelengths and feature point positions on the grating surface; and determining the image point coordinates corresponding to the sampled feature points on the image plane according to the ideal object-image relationship;

S312, for the coordinate points of the sampling light obtained in S311 on the light source plane, grating plane and image plane, solve the incident direction, diffraction direction and optical path of the sampling light on the sampling feature point of the grating plane; at the same time, solve the optical path of the light having the same field of view and the same wavelength and passing through the center of the system aperture stop; calculate the ruling function and ruling density distribution corresponding to the sampling feature point of the grating plane according to the diffraction equation;

S313, fitting the data of the line function and the line density distribution obtained in S312 using the least square method, and obtaining the line distribution description of the target grating through the fitting;

In S4, the use of a genetic algorithm for iterative optimization design includes: minimizing the peak-to-valley value or the root mean square value of the fitting residual error of the scribed line data through the genetic algorithm; at the same time, randomly changing the sampling of the characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

In some implementations, the system reconstruction is a record structure reconstruction, and the system reconstruction comprises the steps of:

S321, obtain the coordinate points of the sampling light on the light source plane, grating plane and image plane, solve the incident direction, diffraction direction and optical path of the sampling light on the grating plane sampling point; at the same time, solve the optical path of the light with the same field of view and the same wavelength and passing through the center of the system aperture stop; calculate the ruling function and ruling density distribution of the corresponding grating plane sampling point according to the diffraction equation;

S322, on the premise that the position of a single recorded light source point is known, calculating the optical path and direction information of the light from the known exposure arm to the sampling feature point on the grating surface;

S323, solving the relative optical path difference and light incident direction corresponding to each sampling feature point on the grating surface in the unknown recording arm, statistically analyzing the corresponding recording arm length, and combining the known recording arm angle and recording wavelength with the equivalent grating density at the center to determine the angle parameter;

In S4, the use of a genetic algorithm for iterative optimization design includes: using a genetic algorithm to minimize the peak-to-valley value or root mean square value of the recorded light source point distribution error at the corresponding position of each sampling feature point on the grating surface solved in S323; at the same time, randomly changing the sampling of the characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

In some implementations, the system reconstruction is a grating surface reconstruction, and the system reconstruction comprises the steps of:

S331, determining a series of characteristic points on the slit, corresponding aperture sampling points and wavelengths according to a random sampling method; determining the image point coordinates corresponding to each slit sampling characteristic point on the image plane according to an ideal object-image relationship;

S332, solving the optical path information of the reference light having the same field of view and the same wavelength as that in S331 and passing through the center point of the unknown grating; solving the intersection point of the sampling light and the unknown grating surface in S331, so that the optical path of the sampling light is consistent with the optical path of the corresponding reference light;

S333, solving the corresponding normal direction for the sampling feature points obtained in S331 according to the diffraction equation, and obtaining the surface shape by fitting using the least square method;

In S4, the use of a genetic algorithm for iterative optimization design includes: minimizing the peak-to-valley value or the root mean square value of the surface fitting residual error through the genetic algorithm; at the same time, randomly changing the sampling of characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

In some implementations, the residual error of any round of fitting is additionally fitted to the following polynomial:

Among them, F _weight represents the weight function used for subsequent least squares fitting, a _ijmn represents the fitting coefficient result, λ is the wavelength, field is the field of view, x _aperture and z _aperture represent the aperture coordinates; i, j, m, and n are all fitting powers with non-negative integer values, and 0<i+j+m+n≤3.

In some implementations, the polynomial is used as a weight coefficient in the next round of fitting process and participates in the least squares fitting.

The present invention also provides a computer device, comprising:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the optical design method of a grating spectrometer described in the present invention.

The present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the optical design method of a grating spectrometer described in the present invention.

Compared with the prior art, the present invention can achieve the following beneficial effects:

The optical design method of the grating spectrometer provided by the present invention does not need to construct a complex aberration expansion model, thereby reducing the manpower work of designers; the problem of ignoring high-order expansion does not exist in the entire design process, and the design result is more accurate; the design can be carried out for any type of spectrometer, and the design is more universal; the genetic algorithm is used for global optimization, and more suitable design results can be searched; moreover, a variety of different design strategies can be freely selected, and selecting different design strategies according to different design requirements can further reduce the design difficulty and reduce the design time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of an Offner convex grating imaging spectrometer in the prior art;

FIG2 is a schematic diagram of the structure of a type III concave grating spectrometer containing a grating recording structure in the prior art;

FIG3 is a schematic diagram of the structure of an echelle grating spectrometer using a free-form surface grating as a second-order dispersion element in the prior art;

4 is a schematic diagram of the composition structure division of a grating spectrometer and a schematic diagram of a grating recording structure in a specific embodiment of the present invention;

5 is a schematic diagram of a process of a first system reconstruction method in a specific implementation mode of the present invention;

6 is a schematic diagram of a process of a second system reconstruction method in a specific implementation manner of the present invention;

7 is a schematic diagram of a process of a third system reconstruction method in a specific implementation manner of the present invention;

8 is a schematic flow chart of an optical design method of a grating spectrometer in a specific embodiment of the present invention;

FIG. 9 is a block diagram of an exemplary computer device suitable for implementing embodiments of the present invention in accordance with a specific embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

Usually, in a grating spectrometer system, the light emitted from the light source surface passes through several optical elements (or directly) and irradiates on the grating surface. After passing through the grating, the light of different wavelengths is diffracted to different directions in space and converges on the image plane of the system through several subsequent elements (or directly). As shown in FIG4 , in a specific embodiment of the present invention, the entire grating spectrometer system is divided into a front part, a grating part and a rear part, wherein the front part is from the light source to the part in front of the grating, and the rear part includes the part from the back of the grating to the image plane.

As shown in FIG4 , for a light ray emitted from any point A on the light source surface, it passes through a point P on the grating and intersects the phase plane at point B, where C and D represent the positions of two exposure light sources, and N represents the surface normal direction at point P on the grating. FIG4 is used to characterize the grating surface ray tracing calculation combined with the exposure system, and its optical path function can be expressed as shown in formula (1):

F＝AP+PB+ _nPmλ (1)

Where _nP represents the value of the line function at point P of the grating, λ represents the wavelength of the calculated light, and m is the diffraction order of the grating. According to the Fermat principle, the propagation path of the light is the optical path extreme value, that is, the partial derivative of the optical path function F with respect to the coordinates of point P should be 0, as shown in equations (2) and (3).

In addition, in the double spherical wave holographic grating recording structure shown in FIG4 , the grating line function and line density function can be expressed as shown in equations (4) to (6):

λ ₀ n _P =(CP-DP)-(CO-DO) (4)

In the above formula, CP and DP represent the optical path from two light source points C and D to any point P on the grating surface, CO and DO represent the optical path from the light source point to the center of the grating, δ represents the sign of the partial derivative to be solved; α, β, γ represent the direction cosines of the corresponding index light, x, y, z represent the spatial coordinates of the corresponding index, and λ ₀ represents the recording wavelength of the holographic grating; in other specific implementations, when other grating recording structures are used, formula (5) and formula (6) are also applicable without any changes.

In a specific implementation of the present invention, according to the above calculation process, the parameters mainly used to determine the propagation path of light on the grating of the computational spectrometer can be divided into the following three categories: the incident direction and diffraction direction of the light, the grating surface type and the grating line distribution (or the recording structure of the grating). When any two of these three types of quantities are known, the third group of quantities can be solved through the above derivation. Based on this core idea, the present invention proposes the following specific implementation scheme.

As shown in FIG8 , it is a schematic flow chart of an optical design method of a grating spectrometer in a specific embodiment of the present invention. As can be seen from the figure, in a specific embodiment of the present invention, an optical design method of a grating spectrometer is provided, and the optical design method comprises the steps of:

S1. Generate an initial parent population by setting a system parameter optimization center and a system parameter optimization range; the system parameters include a working band, a working field and an aperture parameter; generating the initial parent population includes setting a population size, setting an optimization strategy and setting an iterative control;

S2. Based on the crossover mutation of the initial parent population, a progeny population is generated; the crossover mutation includes sampling characteristic light rays, and assigning a random wavelength and field of view to each of the sampled light rays; the assignment includes assigning a random field of view, a random wavelength, an assignment caliber, and an assignment weight factor;

S3, based on the offspring population, performing system reconstruction; the system reconstruction includes reconstruction of grating distribution, reconstruction of recording structure and reconstruction of grating surface type;

S4, using a genetic algorithm to perform an optimized iterative design, and based on the system reconstruction, determine and select a new round of parent populations;

If the parent population does not meet the design goal, the initial parent population is replaced by the parent population, and S2 to S4 are iterated in a loop until the parent population meets the design goal, and then the design result is output;

If the parent population meets the design goal, the iteration is terminated and the design result is output.

In a specific implementation manner, the design result is output through the iterative convergence of the genetic algorithm; specifically, two control strategies are included. When any one of the conditions is met, it is considered that the calculation result has converged and the iteration can be terminated; in the first way, the number of iteration rounds reaches the set maximum number of iteration rounds, and the maximum number of iteration rounds can be adjusted manually, preferably 20 rounds can be selected; in the second way, the iteration judgment function value reaches the preset minimum result. Specifically, this result will have different results according to the system reconstruction strategies corresponding to the three different implementation modes involved in the present invention.

The optical design method of the grating spectrometer provided in the specific embodiment of the present invention is a grating spectrometer design method based on the ray tracing method, which can avoid the problems of poor design accuracy and long design cycle caused by the traditional aberration model expansion method; by using the correlation between the light direction, grating surface shape and grating line distribution in the grating diffraction process, the spectrometer design can be realized by solving the position parameters when any two types of data are known.

The optical design method of the grating spectrometer provided in the specific embodiment of the present invention uses a random field of view, wavelength and aperture sampling method when optimizing the design process using a genetic algorithm, and uses completely random sampling results in each round of iteration process to ensure that the design result has a balanced high resolution of a continuous field of view and a continuous band; at the same time, a fitting method is used to solve unknown design parameters, and the peak-to-valley value or the root mean square value of the fitting residual error is used as a judgment; in the iterative design process, the polynomial of the fitting residual error of the previous round of design goals relative to the field of view, wavelength and sampling aperture is used as the weight factor of the next round of design to guide the next round of design process; when fitting the line distribution and the surface shape, the line function value (or the surface shape vector height value) and the line function density value (or the surface shape partial derivative value) are used for fitting at the same time to ensure that the fitting result is closer to the design goal; by changing the starting point (i.e., the image point) of the reverse tracing in the spectrometer system, a design with aberration tolerance can be achieved; and this process is automatically changed through the tracing results to reduce human participation.

In a first specific implementation manner, the system reconstruction may be a scribe line distribution reconstruction, and the system reconstruction includes the steps of:

S311, randomly sampling a number of feature points on the incident slit of the grating spectrometer, assigning random wavelengths and feature point positions on the grating surface; and determining the image point coordinates corresponding to the sampled feature points on the image plane according to the ideal object-image relationship;

S312, for the coordinate points of the sampling light obtained in S311 on the light source plane, grating plane and image plane, solve the incident direction, diffraction direction and optical path of the sampling light on the sampling feature point of the grating plane; at the same time, solve the optical path of the light having the same field of view and the same wavelength and passing through the center of the system aperture stop; calculate the ruling function and ruling density distribution corresponding to the sampling feature point of the grating plane according to the diffraction equation;

S313, fitting the data of the line function and the line density distribution obtained in S312 using the least square method, and obtaining the line distribution description of the target grating through the fitting;

In S4, the use of a genetic algorithm for iterative optimization design includes: minimizing the peak-to-valley value or the root mean square value of the fitting residual error of the scribed line data through the genetic algorithm; at the same time, randomly changing the sampling of the characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

Specifically, as shown in FIG5 , it is a schematic diagram of the process of the first system reconstruction method in a specific implementation mode of the present invention. Taking the convex aberration-free grating Offner imaging spectrometer shown in FIG1 as an example, the purpose of system reconstruction is to obtain a target grating line distribution model that satisfies the current structure, that is, the line function coefficients n ₀ , n ₁ , n ₂ and n ₃ in formula (7),

n＝n ₀ +n ₁ z +n ₂ z ² +n ₃ z ³ (7)

Formula (7) is a fourth-order polynomial form to obtain the target grating line density distribution model; the specific design process includes the following steps:

(0) Assume that the positions and postures of all components in the system (including the incident slit, 1 and 3 reflectors, convex grating and phase plane as shown in FIG1 ) have been determined, and the surface shapes of all optical components have also been determined;

(1) Randomly sample a number of feature points on the incident slit and assign random wavelengths and feature point positions on the grating surface; determine the image point coordinates corresponding to these feature points on the image plane based on the ideal object-image relationship;

(2) for the coordinate points of the sampling light rays on the light source plane, grating plane and image plane in step (1), the incident direction, diffraction direction and optical path of these sampling light rays on the sampling points on the grating plane are solved according to the law of reflection, and the optical path of the light rays having the same field of view and wavelength but passing through the center of the system aperture stop is solved at the same time; the ruling function and ruling density distribution of the sampling points on the grating plane are calculated according to the diffraction equation;

(3) fitting the line function and line density data obtained in step (2) using the least squares method, wherein the fitting goal is to obtain a distribution representation of the target grating line;

(4) The above process is optimized and solved using a genetic algorithm. The solution goal is to minimize the peak-to-valley value or root mean square of the fitting residual error of the grating data. At the same time, the sampling of the characteristic light is randomly changed during the iteration of the genetic algorithm. That is, each round of iteration uses different light source plane coordinates, grating plane coordinates and corresponding wavelengths to ensure that the design result has balanced resolution in a continuous field of view and continuous band.

In a second specific implementation manner, the system reconstruction is a record structure reconstruction, and the system reconstruction includes the steps of:

S321, obtain the coordinate points of the sampling light on the light source plane, grating plane and image plane, solve the incident direction, diffraction direction and optical path of the sampling light on the grating plane sampling point; at the same time, solve the optical path of the light with the same field of view and the same wavelength and passing through the center of the system aperture stop; calculate the ruling function and ruling density distribution of the corresponding grating plane sampling point according to the diffraction equation;

S322, on the premise that the position of a single recorded light source point is known, calculating the optical path and direction information of the light from the known exposure arm to the sampling feature point on the grating surface;

S323, solving the relative optical path difference and light incident direction corresponding to each sampling feature point on the grating surface in the unknown recording arm, statistically analyzing the corresponding recording arm length, and combining the known recording arm angle and recording wavelength with the equivalent grating density at the center to determine the angle parameter;

In S4, the use of a genetic algorithm for iterative optimization design includes: using a genetic algorithm to minimize the peak-to-valley value or root mean square value of the recorded light source point distribution error at the corresponding position of each sampling feature point on the grating surface solved in S323; at the same time, randomly changing the sampling of the characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

Specifically, as shown in FIG6 , it is a schematic diagram of the process of the second system reconstruction method in a specific implementation mode of the present invention. Taking the type III concave grating flat-field spectrometer shown in FIG2 as an example, the specific steps include:

(0) Assuming that the spectrometer structure (as shown in FIG. 2 , including the positions of the incident slit, grating and image plane, and the curvature radius of the grating base) is known, a series of characteristic points on the slit, as well as the corresponding grating sampling points and wavelengths are determined according to the random sampling method, and the image point corresponding to each slit sampling point on the image plane is determined according to the ideal object-image relationship; at the same time, assuming that the position of any light source in the grating recording structure (assuming it is C) is known;

(1) Obtain the scoreline function value and scoreline function density value of the sampling point on the grating surface according to the same logic as that in solving the scoreline distribution;

(2) Under the premise of knowing the position of a single recorded light source point, calculate the optical path and direction information of the light from the known exposure arm to the sampling feature point on the grating surface;

(3) According to the aforementioned formulas (5) and (6), the relative optical path difference and light incident direction corresponding to each sampling feature point on the grating surface in the unknown recording arm are solved, and the corresponding recording arm length is statistically analyzed. The angle parameter can be determined by the equivalent grating density at the center combined with the known recording arm angle and recording wavelength:

(4) A genetic algorithm is used to optimize the design of all known parameters assumed in the above process. The optimization goal is to minimize the peak-to-valley value or root mean square value of the light source point distribution error recorded at the corresponding position of each grating sampling point solved in step (3); at the same time, the sampling of characteristic light rays is randomly changed during the iteration process of the genetic algorithm, that is, each round of iteration uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths to ensure that the design result has balanced resolution within a continuous field of view and a continuous band.

In a third specific implementation manner, the system reconstruction is grating surface reconstruction, and the system reconstruction comprises the steps of:

S331, determining a series of characteristic points on the slit, corresponding aperture sampling points and wavelengths according to a random sampling method; determining the image point coordinates corresponding to each slit sampling characteristic point on the image plane according to an ideal object-image relationship;

S332, solving the optical path information of the reference light having the same field of view and the same wavelength as that in S331 and passing through the center point of the unknown grating; solving the intersection point of the sampling light and the unknown grating surface in S331, so that the optical path of the sampling light is consistent with the optical path of the corresponding reference light;

S333, solving the corresponding normal direction for the sampling feature points obtained in S331 according to the diffraction equation, and obtaining the surface shape by fitting using the least square method;

In S4, the use of a genetic algorithm for iterative optimization design includes: minimizing the peak-to-valley value or the root mean square value of the surface fitting residual error through the genetic algorithm; at the same time, randomly changing the sampling of characteristic light during the iterative process of the genetic algorithm, and each round of iterative process uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths.

Specifically, as shown in FIG7 , it is a schematic diagram of the process of the third system reconstruction method in a specific implementation mode of the present invention. Taking the free-form surface grating spectrometer shown in FIG3 as an example, the base surface type of the free-form surface grating is solved. Although the first system reconstruction method and the second system reconstruction method can still be used in the design of this type of grating spectrometer, converting the solution target from the ruled line distribution to the grating surface type can effectively reduce the design difficulty; the specific steps are:

(0) Assume that all parameters of the entire spectrometer system are known except for the target grating surface shape, including the position of each component, the surface shape of other components, and the grating line distribution model; determine a series of characteristic points on the slits, as well as the corresponding aperture sampling points and wavelengths according to the random sampling method, and determine the image point corresponding to each slit sampling point on the image plane according to the ideal object-image relationship;

(1) Solve the optical path information of the reference light beam having the same field of view and wavelength sampling as in step (0) but passing through the center point of the unknown grating; and use this as a reference to solve the intersection point of the sampling characteristic light beam in step (0) and the unknown grating surface, so that the optical path of these sampling light beams is consistent with the optical path of the corresponding reference light beam;

(2) solving the corresponding normal direction for the feature points obtained in step (1) according to the diffraction equation, and obtaining the surface shape by fitting using the least squares method;

(3) Genetic algorithms are used to optimize the design of all unknown parameters assumed to be known in the above process. The optimization goal is to minimize the peak-to-valley value or root mean square value of the surface fitting residual error. At the same time, the sampling of characteristic light rays is randomly changed during the iteration process of the genetic algorithm. That is, each round of iteration uses different light source surface coordinates, grating surface coordinates and corresponding wavelengths to ensure that the design result has balanced resolution in a continuous field of view and continuous band.

In a specific implementation manner, the first system reconstruction method and the third system reconstruction method involve a fitting process, and the residual error of any round of fitting is additionally fitted as shown in the following polynomial (7), which is specifically a polynomial corresponding to the field of view, aperture and wavelength:

Among them, F _weight represents the weight function used for the subsequent least squares fitting, a _ijmn represents the fitting coefficient result, λ is the wavelength, field is the field of view, x _aperture and z _aperture represent the aperture coordinates; i, j, m, n are all fitting powers with non-negative integer values, and 0<i+j+m+n≤3. This polynomial can be used as the weight coefficient of the next round of fitting process and participate in the least squares fitting; this additional weight adjustment step can ensure the entire design result of the present invention and achieve balanced high resolution in the complete working band and working field of view.

In other embodiments, the above-mentioned holographic grating recording structure can be replaced by a design in which a non-planar reflector is added to the exposure arm. In this case, the method of the present invention can be replaced by a reconstruction process for an auxiliary reflector in the design.

The optical design method of the grating spectrometer provided by the present invention does not need to construct a complex aberration expansion model, thereby reducing the manpower work of designers; the problem of ignoring high-order expansion does not exist in the entire design process, and the design result is more accurate; the design can be carried out for any type of spectrometer, and the design is more universal; the genetic algorithm is used for global optimization, and more suitable design results can be searched; moreover, a variety of different design strategies can be freely selected, and selecting different design strategies according to different design requirements can further reduce the design difficulty and reduce the design time.

Accordingly, according to an embodiment of the present invention, the present invention also provides a computer device, a readable storage medium and a computer program product.

FIG9 is a schematic diagram of the structure of a computer device 12 provided in an embodiment of the present invention. FIG9 shows a block diagram of an exemplary computer device 12 suitable for implementing an embodiment of the present invention. The computer device 12 shown in FIG9 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present invention.

As shown in Figure 9, computer equipment 12 is in the form of a general-purpose computing device. Computer equipment 12 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described and/or required herein.

Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16 , a system memory 28 , and a bus 18 that connects various system components including system memory 28 and processing unit 16 .

Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 9 , commonly referred to as a “hard drive”). Although not shown in FIG. 9 , a disk drive for reading and writing to a removable non-volatile disk (e.g., a “floppy disk”) and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data media interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and/or methods of the embodiments described herein.

The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input/output (I/O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and/or public network, such as the Internet) through a network adapter 20.

9, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be understood that, although not shown in the figure, other hardware and/or software modules can be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the optical design method of the grating spectrometer provided in the embodiment of the present invention.

An embodiment of the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein when the program is executed by a processor, it is an optical design method of a grating spectrometer provided in all the inventive embodiments of the present application.

The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages or their combinations, and the programming language includes object-oriented programming languages such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect to the Internet).

An embodiment of the present invention further provides a computer program product, including a computer program, which implements the optical design method of the grating spectrometer described above when executed by a processor.

It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A method of optical design for a grating spectrometer, the method comprising the steps of:

S1, generating an initial parent population by setting a system parameter optimizing center and a system parameter optimizing range;

S2, generating a child population based on the initial parent population cross variation; the cross variation comprises sampling characteristic rays, and assigning a random wavelength and a random view field to each sampling characteristic ray;

S3, performing system reconstruction based on the offspring population; the system reconstruction comprises at least one of reticle distribution reconstruction, record structure reconstruction and grating surface type reconstruction; the system reconstruction is a reticle distribution reconstruction, and the system reconstruction comprises the following steps:

S311, randomly sampling a plurality of characteristic points on an incident slit of the grating spectrometer, and assigning random wavelengths and characteristic point positions on a grating surface; determining corresponding image point coordinates of the sampling feature points on the image plane according to the ideal object-image relationship;

S312, solving the incidence direction, the diffraction direction and the optical path of the sampling characteristic light rays on the sampling characteristic points of the grating surface according to the coordinate points of the sampling characteristic light rays on the light source surface, the grating surface and the image surface obtained in the S311; meanwhile, solving the optical path of the light which has the same field of view and the same wavelength and passes through the center of the aperture diaphragm of the system; calculating a line function and line density distribution of the sampling feature points of the corresponding grating surface according to a diffraction equation;

s313, fitting the data of the line function and the line density distribution obtained in the S312 by using a least square method, and obtaining a line distribution expression of the target grating through the fitting;

the system reconstruction is a recording structure reconstruction, the system reconstruction comprises the steps of:

S321, obtaining coordinate points of sampling characteristic light on a light source surface, a grating surface and an image surface, and solving the incidence direction, the diffraction direction and the optical path of the sampling characteristic light on the grating surface sampling point; meanwhile, solving the optical path of the light which has the same field of view and the same wavelength and passes through the center of the aperture diaphragm of the system; calculating a line-scribing function and line-scribing density distribution of a sampling point of a corresponding grating surface according to a diffraction equation;

s322, on the premise that the position of a single recording light source point is known, calculating the light path length and direction information from a known exposure arm to a grating surface sampling characteristic point;

s323, solving the corresponding relative optical path difference and the light incidence direction of each sampling characteristic point on the grating surface in an unknown recording arm, statistically analyzing the corresponding recording arm length, and determining an angle parameter by combining the angle and the recording wavelength of the known recording arm with the equivalent grating line density at the center;

the system reconstruction is a grating surface type reconstruction, and the system reconstruction comprises the following steps:

S331, determining a series of characteristic points, corresponding caliber sampling points and wavelengths on the slit according to a random sampling method; according to the ideal object-image relationship, determining the corresponding image point coordinates of each slit sampling characteristic point on the image plane;

S332, solving the optical path information of the reference light which has the same field of view and the same wavelength as those in S331 and passes through the center point of the unknown grating; solving the intersection point of the sampling characteristic light and the unknown grating surface in the S331, so that the optical path of the sampling characteristic light is consistent with the optical path of the corresponding reference light;

S333, solving the corresponding normal direction according to the diffraction equation aiming at the sampling characteristic points obtained in the S331, and obtaining a surface shape by using least square fitting;

S4, carrying out optimization iterative design by using a genetic algorithm, and judging and selecting a new round of parent population based on the system reconstruction;

If the parent population does not meet the design target, replacing the initial parent population with the parent population, and circularly executing S2-S4 for iteration until the parent population meets the design target, and outputting a design result;

Ending iteration if the parent population meets the design target, and outputting a design result;

In the step S4, performing the optimization iterative design using the genetic algorithm includes: the peak-valley value or root mean square value of the fitting residual error of the line data is minimized through a genetic algorithm; simultaneously, the sampling of the characteristic light is changed immediately in the iterative process of the genetic algorithm, and different light source plane coordinates, grating plane coordinates and corresponding wavelengths are used in each round of iterative process;

In the step S4, performing the optimization iterative design using the genetic algorithm includes: minimizing the peak-valley value or root mean square value of the surface fitting residual error through a genetic algorithm; simultaneously, the sampling of the characteristic light is changed immediately in the iterative process of the genetic algorithm, and different light source plane coordinates, grating plane coordinates and corresponding wavelengths are used in each round of iterative process;

in the step S4, performing the optimization iterative design using the genetic algorithm includes: the peak-valley value or root mean square value of the distribution error of the recorded light source points at the corresponding position obtained by solving each sampling characteristic point in the S323 is minimized through a genetic algorithm; meanwhile, the sampling of the characteristic light is changed immediately in the iterative process of the genetic algorithm, and different light source plane coordinates, grating plane coordinates and corresponding wavelengths are used in each round of iterative process.

2. The method of optical design of a grating spectrometer according to claim 1, wherein the system parameters include operating band, operating field of view, and caliber parameters; the assigning includes assigning a random field of view, assigning a random wavelength, assigning a caliber, and assigning a weight factor.

3. The method of optical design of a grating spectrometer of claim 1, wherein generating the initial parent population comprises setting a population number, setting an optimization strategy, and setting an iterative control.

4. The optical design method of a grating spectrometer according to claim 1, wherein the fitting residual error of any round is additionally fitted to a polynomial as follows:

Wherein F _weight represents a weight function for subsequent least squares fitting, a _ijmn represents a fitting coefficient result, lambda is a wavelength, field is a field of view, and x _aperture and z _aperture represent caliber coordinates; i. j, m and n are fitting power numbers with non-negative integers, and 0< i+j+m+n is less than or equal to 3.

5. The optical design method of grating spectrometer according to claim 4, wherein the polynomial is used as the weight coefficient of the next round of fitting process to participate in the least square fitting.

6. A computer device, comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the optical design method of the grating spectrometer of any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the optical design method of the grating spectrometer of any one of claims 1 to 5.