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WO2025087668A1 - Systèmes et procédés de prédiction de mode de charge et de paramètres optimaux pour contraste de tension - Google Patents

Systèmes et procédés de prédiction de mode de charge et de paramètres optimaux pour contraste de tension Download PDF

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Publication number
WO2025087668A1
WO2025087668A1 PCT/EP2024/077743 EP2024077743W WO2025087668A1 WO 2025087668 A1 WO2025087668 A1 WO 2025087668A1 EP 2024077743 W EP2024077743 W EP 2024077743W WO 2025087668 A1 WO2025087668 A1 WO 2025087668A1
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Prior art keywords
sample
charging mode
charged particle
range
plot
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Inventor
Mathijs Wouter Henk GARMING
Benoit Herve GAURY
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ASML Netherlands BV
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ASML Netherlands BV
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/02—Details
    • H01J37/026—Means for avoiding or neutralising unwanted electrical charges on tube components
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J2237/00—Discharge tubes exposing object to beam, e.g. for analysis treatment, etching, imaging
    • H01J2237/004—Charge control of objects or beams

Definitions

  • the description herein relates to the field of charged particle systems, and more particularly to methods for predicting charging mode and optimal parameters for voltage contrast in a charged particle system.
  • a charged particle (e.g., electron) beam microscope such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM), capable of resolution down to less than a nanometer, serves as a practicable tool for inspecting IC components having a feature size that is sub- 100 nanometers.
  • SEM scanning electron microscope
  • TEM transmission electron microscope
  • electrons of a single primary electron beam, or electrons of a plurality of primary electron beams can be focused on locations of interest of a wafer under inspection.
  • the primary electrons interact with the wafer and may be backscattered or may cause the wafer to emit secondary electrons.
  • the intensity of the electron beams comprising the backscattered electrons and the secondary electrons may vary based on the properties of the internal and external structures of the wafer, and thereby may indicate whether the wafer has defects.
  • Embodiments of the present disclosure provide systems and methods for predicting charging mode and optimal parameters for voltage contrast in a charged particle system.
  • systems, methods, and non-transitory computer readable mediums may include inputting a plurality of parameters into a model; generating, using the model, a plot for predicting the charging mode of the charged particle system; and predicting, based on the plot, the charging mode of the charged particle system.
  • systems, methods, and non-transitory computer readable mediums may include inputting a plurality of parameters into a model; generating, using the model, a plot for predicting optimal parameters for the target charging mode of the charged particle system; and predicting, based on the plot, the optimal parameters for the target charging mode of the charged particle system.
  • Fig. l is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system, consistent with embodiments of the present disclosure.
  • EBI electron beam inspection
  • Fig. 2A is a schematic diagram illustrating an exemplary multi-beam system that is part of the exemplary charged particle beam inspection system of Fig. 1, consistent with embodiments of the present disclosure.
  • Fig. 2B is a schematic diagram illustrating an exemplary single -beam system that is part of the exemplary charged particle beam inspection system of Fig. 1, consistent with embodiments of the present disclosure.
  • Fig. 3 shows an exemplary graph showing a yield rate of total electrons (secondary electrons and backscattered electrons) relative to landing energy of primary electron beamlets, consistent with embodiments of the present disclosure.
  • Fig. 4 shows a schematic diagram illustrating an exemplary voltage contrast response of a wafer, consistent with embodiments of the present disclosure.
  • Fig. 5 shows a schematic diagram illustrating exemplary SEM images and an exemplary schematic of an inspected sample, consistent with embodiments of the present disclosure.
  • Fig. 6 shows an exemplary graph of scattering range of SEs as a function of landing energy (LE), consistent with embodiments of the present disclosure.
  • Fig. 7 shows an exemplary sample in a charged particle system, consistent with embodiments of the present disclosure.
  • Fig. 8 shows an exemplary graph of primary beam current as a function of electrode potential (potential of a lens component in the charged particle system) and surface voltage, consistent with embodiments of the present disclosure.
  • Fig. 9 shows an exemplary graph of exemplary SEM images at various landing energies and beam densities, consistent with embodiments of the present disclosure.
  • Fig. 10 shows an exemplary graph of surface voltage as a function of landing energy and beam density, consistent with embodiments of the present disclosure.
  • Fig. 11 shows exemplary graphs of surface voltage as a function of landing energy and beam density, consistent with embodiments of the present disclosure.
  • Fig. 12 shows a graph of electric potential and electric fields, consistent with embodiments of the present disclosure.
  • Fig. 13 shows a graph of collection efficiency of SEs released from oxide close to the edge of a grounded contact as function of oxide potential, consistent with embodiments of the present disclosure.
  • Fig. 14 shows exemplary graphs of surface voltage as a function of landing energy and beam density, consistent with embodiments of the present disclosure.
  • Fig. 15 shows an exemplary graph of saturation current as a function of extraction field strength and beam spot size, consistent with embodiments of the present disclosure.
  • Fig. 16 shows an exemplary process for predicting a charging mode of a charged particle system or for predicting parameters for a target charging mode of a charged particle system, consistent with embodiments of the present disclosure.
  • Electronic devices are constructed of circuits formed on a piece of silicon (or other materials such as GaAs) called a substrate. Many circuits may be formed together on the same piece of silicon and are called integrated circuits or ICs. The size of these circuits has decreased dramatically so that many more of them can fit on the substrate. For example, an IC chip in a smart phone can be as small as a thumbnail and yet may include over 2 billion transistors, the size of each transistor being less than l/1000th the size of a human hair.
  • One component of improving yield is monitoring the chip making process to ensure that it is producing a sufficient number of functional ICs.
  • One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. Inspection may be carried out using a scanning electron microscope (SEM). A SEM can be used to image these extremely small structures, in effect, taking a “picture” of the structures of the wafer. The image can be used to determine if the structure was formed properly, and also if it was formed at the proper location. If the structure is defective, then the process can be adjusted so the defect is less likely to recur. Defects may be generated during various stages of semiconductor processing. For the reason stated above, it is important to find defects accurately and efficiently as early as possible.
  • a SEM takes a picture by receiving and recording brightness and colors of light reflected or emitted from people or objects.
  • a SEM takes a “picture” by receiving and recording energies or quantities of electrons reflected or emitted from the structures.
  • an electron beam may be projected onto the structures, and when the electrons are reflected or emitted (“exiting”) from the structures, a detector of the SEM may receive and record the energies or quantities of those electrons to generate an image.
  • some SEMs use a single electron beam (referred to as a “single-beam SEM”), while some SEMs use multiple electron beams (referred to as a “multi-beam SEM”) to take multiple “pictures” of the wafer.
  • the SEM may project more electron beams onto the structures for obtaining these multiple “pictures,” resulting in more electrons exiting from the structures.
  • the detector may receive more exiting electrons simultaneously, and generate images of the structures of the wafer with a higher efficiency and a faster speed.
  • a SEM can be used to image ICs after they are manufactured.
  • a SEM single photoelectron emission computed tomography
  • the whole region that is observed is illuminated with light and the sample is imaged with a lens on a sensor with many pixels. An image is obtained by reading out all the pixels.
  • the detector need not have pixels, as the electron beam is focused onto a tiny (e.g., nanometer-sized) spot that is scanned over the sample.
  • the “picture” made with a SEM is an x-y plot of signal as a function of beam position. In a SEM, there may be no lenses that focus signal from the sample onto the detector.
  • ICs are made with a substantial number of transistors.
  • Different layers of a sample require different charging modes (e.g., positive mode or negative mode). Therefore, it is important to have control over the mode of charging that occurs, both for pre-charging steps and for inspection.
  • negative mode options to achieve it include increasing the landing energy such that the combined secondary electron (SE) and backscattered electron (BSE) yield drops below unity or increasing the current density in the beam (beam density) until negative mode voltage contrast (VC) is achieved, even at landing energies normally associated with positive mode VC.
  • SE secondary electron
  • BSE backscattered electron
  • VC negative mode voltage contrast
  • Fig. 5 shows an image 510 generated from a sample under positive mode VC charging, an image 520 under negative mode VC charging, and a schematic 540 of image 520, consistent with embodiments of the present disclosure.
  • positive mode VC charging the electron beam charges the sample positively and grounded metal contacts give bright signals compared to their surrounding oxide.
  • negative mode VC charging charging is negative, which leads to contacts appearing dark against a bright surrounding oxide 534, as well as the formation of dark rings 530a or 530b around contact holes 532 (e.g., ground or N+/P-well plug).
  • image 520 may be generated based on electrons 536 that hit the detector.
  • the oxide voltage of oxide 534 must be less than the plug voltage of contact hole 532. With grounded contacts, this condition is satisfied when the oxide has a negative potential. For example, as shown in Fig. 5, electrons 538 may transfer to contact holes 532 to form dark rings 530a.
  • This typical method of achieving beam density induced negative mode suffers from constraints. For example, the trial and error approach to find conditions to achieve negative mode requires tool time and operator time. Therefore, a method to predict whether positive charging or negative charging will occur is needed to increase efficiency.
  • the disclosed embodiments provide systems and methods that address some or all of these disadvantages by predicting charging mode and optimal parameters for voltage contrast in a charged particle system.
  • the disclosed embodiments may include a model that predicts the charging mode or optimal parameters for a target charging mode based on beam and sample parameters, while considering the effect of beam density through the mechanism of space charge.
  • the disclosed embodiments may also consider the effects of pattern density on the surface charging of the sample, as well as VC formation.
  • the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a component may include A or B, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component may include A, B, or C, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.
  • Fig. 1 illustrates an exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure.
  • EBI system 100 may be used for imaging.
  • EBI system 100 includes a main chamber 101, a load/lock chamber 102, an electron beam tool 104, and an equipment front end module (EFEM) 106.
  • EFEM equipment front end module
  • Electron beam tool 104 is located within main chamber 101.
  • EFEM 106 includes a first loading port 106a and a second loading port 106b.
  • EFEM 106 may include additional loading port(s).
  • First loading port 106a and second loading port 106b receive wafer front opening unified pods (FOUPs) that contain wafers (e.g., semiconductor wafers or wafers made of other material(s)) or samples to be inspected (wafers and samples may be used interchangeably).
  • a “lot” is a plurality of wafers that may be loaded for processing as a batch.
  • One or more robotic arms (not shown) in EFEM 106 may transport the wafers to load/lock chamber 102.
  • Load/lock chamber 102 is connected to a load/lock vacuum pump system (not shown) which removes gas molecules in load/lock chamber 102 to reach a first pressure below the atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) may transport the wafer from load/lock chamber 102 to main chamber 101.
  • Main chamber 101 is connected to a main chamber vacuum pump system (not shown) which removes gas molecules in main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is subject to inspection by electron beam tool 104.
  • Electron beam tool 104 may be a single-beam system or a multibeam system.
  • a controller 109 is electronically connected to electron beam tool 104. Controller 109 may be a computer configured to execute various controls of EBI system 100. While controller 109 is shown in Fig. 1 as being outside of the structure that includes main chamber 101, load/lock chamber 102, and EFEM 106, it is appreciated that controller 109 may be a part of the structure.
  • controller 109 may include one or more processors (not shown).
  • a processor may be a generic or specific electronic device capable of manipulating or processing information.
  • the processor may include any combination of any number of a central processing unit (or “CPU”), a graphics processing unit (or “GPU”), an optical processor, a programmable logic controllers, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a Programmable Logic Array (PLA), a Programmable Array Logic (PAL), a Generic Array Logic (GAL), a Complex Programmable Logic Device (CPLD), a Field- Programmable Gate Array (FPGA), a System On Chip (SoC), an Application-Specific Integrated Circuit (ASIC), and any type circuit capable of data processing.
  • the processor may also be a virtual processor that includes one or more processors distributed across multiple machines or devices coupled via a network.
  • controller 109 may further include one or more memories (not shown).
  • a memory may be a generic or specific electronic device capable of storing codes and data accessible by the processor (e.g., via a bus).
  • the memory may include any combination of any number of a random-access memory (RAM), a read-only memory (ROM), an optical disc, a magnetic disk, a hard drive, a solid-state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, or any type of storage device.
  • the codes may include an operating system (OS) and one or more application programs (or “apps”) for specific tasks.
  • the memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices coupled via a network.
  • Embodiments of this disclosure may provide a single charged-particle beam imaging system (“single-beam system”). Compared with a single-beam system, a multiple charged-particle beam imaging system (“multi-beam system”) may be designed to optimize throughput for different scan modes. Embodiments of this disclosure provide a multi-beam system with the capability of optimizing throughput for different scan modes by using beam arrays with different geometries and adapting to different throughputs and resolution requirements.
  • FIG. 2A is a schematic diagram illustrating an exemplary electron beam tool 104 including a multi-beam inspection tool that is part of the EBI system 100 of Fig. 1, consistent with embodiments of the present disclosure.
  • electron beam tool 104 may be operated as a single -beam inspection tool that is part of EBI system 100 of Fig. 1.
  • Multibeam electron beam tool 104 (also referred to herein as apparatus 104) comprises an electron source 201, a Coulomb aperture plate (or “gun aperture plate”) 271, a condenser lens 210, a source conversion unit 220, a primary projection system 230, a motorized stage 209, and a sample holder 207 supported by motorized stage 209 to hold a sample 208 (e.g., a wafer or a photomask) to be inspected.
  • Multi-beam electron beam tool 104 may further comprise a secondary projection system 250 and an electron detection device 240.
  • Primary projection system 230 may comprise an objective lens 231.
  • Electron detection device 240 may comprise a plurality of detection elements 241, 242, and 243.
  • a beam separator 233 and a deflection scanning unit 232 may be positioned inside primary projection system 230.
  • Electron source 201, Coulomb aperture plate 271, condenser lens 210, source conversion unit 220, beam separator 233, deflection scanning unit 232, and primary projection system 230 may be aligned with a primary optical axis 204 of apparatus 104.
  • Secondary projection system 250 and electron detection device 240 may be aligned with a secondary optical axis 251 of apparatus 104.
  • Electron source 201 may comprise a cathode (not shown) and an extractor or anode (not shown), in which, during operation, electron source 201 is configured to emit primary electrons from the cathode and the primary electrons are extracted or accelerated by the extractor and/or the anode to form a primary electron beam 202 that form a primary beam crossover (virtual or real) 203.
  • Primary electron beam 202 may be visualized as being emitted from primary beam crossover 203.
  • Source conversion unit 220 may comprise an image-forming element array (not shown), an aberration compensator array (not shown), a beam-limit aperture array (not shown), and a pre-bending micro-deflector array (not shown).
  • the pre-bending micro-deflector array deflects a plurality of primary beamlets 211, 212, 213 of primary electron beam 202 to normally enter the beam-limit aperture array, the image-forming element array, and an aberration compensator array.
  • apparatus 104 may be operated as a single-beam system such that a single primary beamlet is generated.
  • condenser lens 210 is designed to focus primary electron beam 202 to become a parallel beam and be normally incident onto source conversion unit 220.
  • the image-forming element array may comprise a plurality of micro-deflectors or micro-lenses to influence the plurality of primary beamlets 211, 212, 213 of primary electron beam 202 and to form a plurality of parallel images (virtual or real) of primary beam crossover 203, one for each of the primary beamlets 211, 212, and 213.
  • the aberration compensator array may comprise a field curvature compensator array (not shown) and an astigmatism compensator array (not shown).
  • the field curvature compensator array may comprise a plurality of micro-lenses to compensate field curvature aberrations of the primary beamlets 211, 212, and 213.
  • the astigmatism compensator array may comprise a plurality of micro-stigmators to compensate astigmatism aberrations of the primary beamlets 211, 212, and 213.
  • the beam-limit aperture array may be configured to limit diameters of individual primary beamlets 211, 212, and 213.
  • Fig. 2A shows three primary beamlets 211, 212, and 213 as an example, and it is appreciated that source conversion unit 220 may be configured to form any number of primary beamlets.
  • Controller 109 may be connected to various parts of EBI system 100 of Fig. 1, such as source conversion unit 220, electon detection device 240, primary projection system 230, or motorized stage 209. In some embodiments, as explained in further details below, controller 109 may perform various image and signal processing functions. Controller 109 may also generate various control signals to govern operations of the charged particle beam inspection system.
  • Condenser lens 210 is configured to focus primary electron beam 202. Condenser lens 210 may further be configured to adjust electric currents of primary beamlets 211, 212, and 213 downstream of source conversion unit 220 by varying the focusing power of condenser lens 210. Alternatively, the electric currents may be changed by altering the radial sizes of beam-limit apertures within the beamlimit aperture array corresponding to the individual primary beamlets. The electric currents may be changed by both altering the radial sizes of beam-limit apertures and the focusing power of condenser lens 210. Condenser lens 210 may be an adjustable condenser lens that may be configured so that the position of its first principal plane is movable.
  • the adjustable condenser lens may be configured to be magnetic, which may result in off-axis beamlets 212 and 213 illuminating source conversion unit 220 with rotation angles. The rotation angles change with the focusing power or the position of the first principal plane of the adjustable condenser lens.
  • Condenser lens 210 may be an anti-rotation condenser lens that may be configured to keep the rotation angles unchanged while the focusing power of condenser lens 210 is changed.
  • condenser lens 210 may be an adjustable antirotation condenser lens, in which the rotation angles do not change when its focusing power and the position of its first principal plane are varied.
  • Objective lens 231 may be configured to focus beamlets 211, 212, and 213 onto a sample 208 for inspection and may form, in the current embodiments, three probe spots 221, 222, and 223 on the surface of sample 208.
  • Coulomb aperture plate 271 in operation, is configured to block off peripheral electrons of primary electron beam 202 to reduce Coulomb effect. The Coulomb effect may enlarge the size of each of probe spots 221, 222, and 223 of primary beamlets 211, 212, 213, and therefore deteriorate inspection resolution.
  • Beam separator 233 may, for example, be a Wien filter comprising an electrostatic deflector generating an electrostatic dipole field and a magnetic dipole field (not shown in Fig. 2A).
  • beam separator 233 may be configured to exert an electrostatic force by electrostatic dipole field on individual electrons of primary beamlets 211, 212, and 213.
  • the electrostatic force is equal in magnitude but opposite in direction to the magnetic force exerted by magnetic dipole field of beam separator 233 on the individual electrons.
  • Primary beamlets 211, 212, and 213 may therefore pass at least substantially straight through beam separator 233 with at least substantially zero deflection angles.
  • Deflection scanning unit 232 in operation, is configured to deflect primary beamlets 211, 212, and 213 to scan probe spots 221, 222, and 223 across individual scanning areas in a section of the surface of sample 208.
  • primary beamlets 211, 212, and 213 or probe spots 221, 222, and 223 on sample 208 electrons emerge from sample 208 and generate three secondary electron beams 261, 262, and 263.
  • Each of secondary electron beams 261, 262, and 263 typically comprise secondary electrons (having electron energy ⁇ 50eV) and backscattered electrons (having electron energy between 50eV and the landing energy of primary beamlets 211, 212, and 213).
  • Beam separator 233 is configured to deflect secondary electron beams 261, 262, and 263 towards secondary projection system 250.
  • Secondary projection system 250 subsequently focuses secondary electron beams 261, 262, and 263 onto detection elements 241, 242, and 243 of electron detection device 240.
  • Detection elements 241, 242, and 243 are arranged to detect corresponding secondary electron beams 261, 262, and 263 and generate corresponding signals which are sent to controller 109 or a signal processing system (not shown), e.g., to construct images of the corresponding scanned areas of sample 208.
  • detection elements 241, 242, and 243 detect corresponding secondary electron beams 261, 262, and 263, respectively, and generate corresponding intensity signal outputs (not shown) to an image processing system (e.g., controller 109).
  • each detection element 241, 242, and 243 may comprise one or more pixels.
  • the intensity signal output of a detection element may be a sum of signals generated by all the pixels within the detection element.
  • controller 109 may comprise image processing system that includes an image acquirer (not shown), a storage (not shown).
  • the image acquirer may comprise one or more processors.
  • the image acquirer may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combination thereof.
  • the image acquirer may be communicatively coupled to electron detection device 240 of apparatus 104 through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, among others, or a combination thereof.
  • the image acquirer may receive a signal from electron detection device 240 and may construct an image. The image acquirer may thus acquire images of sample 208.
  • the image acquirer may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, and the like.
  • the image acquirer may be configured to perform adjustments of brightness and contrast, etc. of acquired images.
  • the storage may be a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), other types of computer readable memory, and the like.
  • the storage may be coupled with the image acquirer and may be used for saving scanned raw image data as original images, and postprocessed images.
  • the image acquirer may acquire one or more images of a sample based on an imaging signal received from electron detection device 240.
  • An imaging signal may correspond to a scanning operation for conducting charged particle imaging.
  • An acquired image may be a single image comprising a plurality of imaging areas.
  • the single image may be stored in the storage.
  • the single image may be an original image that may be divided into a plurality of regions. Each of the regions may comprise one imaging area containing a feature of sample 208.
  • the acquired images may comprise multiple images of a single imaging area of sample 208 sampled multiple times over a time sequence.
  • the multiple images may be stored in the storage.
  • controller 109 may be configured to perform image processing steps with the multiple images of the same location of sample 208.
  • controller 109 may include measurement circuitries (e.g., analog-to- digital converters) to obtain a distribution of the detected secondary electrons.
  • the electron distribution data collected during a detection time window in combination with corresponding scan path data of each of primary beamlets 211, 212, and 213 incident on the wafer surface, can be used to reconstruct images of the wafer structures under inspection.
  • the reconstructed images can be used to reveal various features of the internal or external structures of sample 208, and thereby can be used to reveal any defects that may exist in the wafer.
  • controller 109 may control motorized stage 209 to move sample 208 during inspection of sample 208. In some embodiments, controller 109 may enable motorized stage 209 to move sample 208 in a direction continuously at a constant speed. In other embodiments, controller 109 may enable motorized stage 209 to change the speed of the movement of sample 208 over time depending on the steps of scanning process.
  • apparatus 104 may use one, two, or more number of primary electron beams.
  • apparatus 104 may be a SEM used for lithography.
  • electron beam tool 104 may be a single-beam system or a multi-beam system.
  • an electron beam tool 100B (also referred to herein as apparatus 100B) may be a single -beam inspection tool that is used in EBI system 100, consistent with embodiments of the present disclosure.
  • Apparatus 100B includes a wafer holder 136 supported by motorized stage 134 to hold a wafer 150 to be inspected.
  • Electron beam tool 100B includes an electron emitter, which may comprise a cathode 103, an anode 121, and a gun aperture 122.
  • Electron beam tool 100B further includes a beam limit aperture 125, a condenser lens 126, a column aperture 135, an objective lens assembly 132, and a detector 144.
  • Objective lens assembly 132 in some embodiments, may be a modified SORIL lens, which includes a pole piece 132a, a control electrode 132b, a deflector 132c, and an exciting coil 132d.
  • an electron beam 161 emanating from the tip of cathode 103 may be accelerated by anode 121 voltage, pass through gun aperture 122, beam limit aperture 125, condenser lens 126, and be focused into a probe spot 170 by the modified SORIL lens and impinge onto the surface of wafer 150.
  • Probe spot 170 may be scanned across the surface of wafer 150 by a deflector, such as deflector 132c or other deflectors in the SORIL lens.
  • Secondary or scattered primary particles, such as secondary electrons or scattered primary electons emanated from the wafer surface may be collected by detector 144 to determine intensity of the beam and so that an image of an area of interest on wafer 150 may be reconstructed.
  • Image acquirer 120 may comprise one or more processors.
  • image acquirer 120 may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combination thereof.
  • Image acquirer 120 may connect with detector 144 of electron beam tool 100B through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or a combination thereof.
  • Image acquirer 120 may receive a signal from detector 144 and may construct an image. Image acquirer 120 may thus acquire images of wafer 150.
  • Image acquirer 120 may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, and the like. Image acquirer 120 may be configured to perform adjustments of brightness and contrast, etc. of acquired images.
  • Storage 130 may be a storage medium such as a hard disk, random access memory (RAM), cloud storage, other types of computer readable memory, and the like. Storage 130 may be coupled with image acquirer 120 and may be used for saving scanned raw image data as original images, and post-processed images.
  • Image acquirer 120 and storage 130 may be connected to controller 109. In some embodiments, image acquirer 120, storage 130, and controller 109 may be integrated together as one electronic control unit.
  • image acquirer 120 may acquire one or more images of a sample based on an imaging signal received from detector 144.
  • An imaging signal may correspond to a scanning operation for conducting charged particle imaging.
  • An acquired image may be a single image comprising a plurality of imaging areas that may contain various features of wafer 150.
  • the single image may be stored in storage 130. Imaging may be performed on the basis of imaging frames.
  • the condenser and illumination optics of the electron beam tool may comprise or be supplemented by electromagnetic quadrupole electron lenses.
  • electron beam tool 100B may comprise a first quadrupole lens 148 and a second quadrupole lens 158.
  • the quadrupole lenses are used for controlling the electon beam.
  • first quadrupole lens 148 can be controlled to adjust the beam current
  • second quadrupole lens 158 can be controlled to adjust the beam spot size and beam shape.
  • a machine learning system may be operated in association with, e.g., controller 109, image processing system 199, image acquirer 120, or storage 130 of FIGs. 1, 2A, 2B.
  • machine learning may be employed in the process for predicting a charging mode of a charged particle system or for predicting parameters for a target charging mode of a charged particle system, e.g., process 1600 of FIG. 16.
  • a machine learning system may comprise a discriminative model.
  • a machine learning system may include a generative model.
  • learning can feature two types of mechanisms: discriminative learning that may be used to create classification and detection algorithms, and generative learning that may be used to actually create models that, in the extreme, can render images, graphs, or plots.
  • a generative model may be configured for generating graphs (e.g., graph 800 of Fig. 8, graph 1000 of Fig. 10, graphs 1110, 1120, 1130, 1140 of Fig. 11, graph 1200 of Fig. 12, graph 1300 of Fig. 13, graphs 1410, 1420 of Fig. 14, graph 1500 of Fig. 15) used for predicting a charging mode of a charged particle system or for predicting parameters for a target charging mode of a charged particle system.
  • graphs e.g., graph 800 of Fig. 8, graph 1000 of Fig. 10, graphs 1110, 1120, 1130, 1140 of Fig. 11, graph 1200 of Fig. 12, graph 1300 of Fig. 13, graphs 1410, 1420 of Fig. 14, graph 1500 of Fig. 15
  • This may be performed by 1) inputting parameters of the charged particle system or sample (e.g., primary beam current, electrode potential, surface voltage, landing energy, beam density, electric fields, collection efficiency of secondary electrons released from oxide of a sample, oxide potential, extraction field strength, beam spot size, saturation current) or actual SEM images into the generative model; and 2) using the model in inference mode to feed the model parameters of the charged particle system or sample or actual SEM images corresponding to different charging VC modes.
  • the model may be used to generate graphs or plots, which in turn may be used to predict a charging mode of a charged particle system or to predict parameters for a target charging mode of a charged particle system.
  • the model may predict a charging mode of a charged particle system or predict parameters for a target charging mode of a charged particle system without generating graphs or plots.
  • Such simulated parameters and associated charging VC modes can be used as reference images in, e.g., die-to-database inspection.
  • the discriminative model(s) may have any suitable architecture or configuration known in the art.
  • Discriminative models also called conditional models, are a class of models used in machine learning for modeling the dependence of an unobserved variable “y” on an observed variable “x.” Within a probabilistic framework, this may be done by modeling a conditional probability distribution P(ylx), which can be used for predicting y based on x.
  • Discriminative models as opposed to generative models, may not allow one to generate samples from the joint distribution of x and y.
  • Generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks.
  • most discriminative models are inherently supervised and cannot easily be extended to unsupervised learning. Application specific details ultimately dictate the suitability of selecting a discriminative versus generative model.
  • a generative model can be generally defined as a model that is probabilistic in nature.
  • a “generative” model is not one that performs forward simulation or rule-based approaches and, as such, it may not be necessary to model the physics of the processes involved in generating an actual image or output (for which a simulated image or output is being generated). Instead, the generative model can be learned (in that its parameters can be learned) based on a suitable training set of data.
  • Such generative models may have a number of advantages for the embodiments described herein.
  • the generative model may be configured to have a deep learning architecture in that the generative model may include multiple layers, which may perform a number of algorithms or transformations. The number of layers included in the generative model may depend on the particular use case. For practical purposes, a suitable range of layers is from 2 layers to a few tens of layers.
  • Deep learning is a type of machine learning.
  • Machine learning can be generally defined as a type of artificial intelligence (Al) that provides computers with the ability to learn without being explicitly programmed.
  • Al artificial intelligence
  • Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
  • Machine learning explores the study and construction of algorithms that can learn from and make predictions on data — such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.
  • the machine learning described herein may be further performed as described in “Introduction to Statistical Machine Learning,” by Sugiyama, Morgan Kaufmann, 2016, 534 pages; “Discriminative, Generative, and Imitative Learning,” Jebara, MIT Thesis, 2002, 212 pages; and “Principles of Data Mining (Adaptive Computation and Machine Learning)” Hand et al., MIT Press, 2001, 578 pages; which are incorporated by reference as if fully set forth herein.
  • the embodiments described herein may be further configured as described in these references.
  • a machine learning system may comprise a neural network.
  • a model may be a deep neural network with a set of weights that model the world according to the data that it has been fed to train it.
  • Neural networks can be generally defined as a computational approach which is based on a relatively large collection of neural units loosely modeling the way a biological brain solves problems with relatively large clusters of biological neurons connected by axons. Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units.
  • Neural networks typically consist of multiple layers, and the signal path traverses from front to back.
  • the goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract.
  • Modern neural network projects typically work with a few thousand to a few million neural units and millions of connections.
  • the neural network may have any suitable architecture or configuration known in the art.
  • a model may comprise convolutional and deconvolution neural network.
  • the embodiments described herein can take advantage of learning concepts such as a convolution and deconvolution neural network to solve the normally intractable representation conversion problem (e.g., rendering).
  • the model may have any convolution and deconvolution neural network configuration or architecture known in the art.
  • Fig. 3 illustrates an exemplary graph showing a yield rate of total electrons (secondary electrons (SEs) and backscattered electrons) relative to landing energy of primary electron beamlets, consistent with embodiments of the present disclosure.
  • the graph illustrates the relationship of the landing energy of a plurality of beamlets of a primary electron beam (e.g., plurality of beamlets 211, 212, or 213 of primary electron beam 202 of Fig. 2A) and the yield rate of secondary electron beams (e.g., secondary electron beams 261 , 262, or 263 of Fig. 2A).
  • the yield rate indicates the number of secondary electrons that are produced in response to the impact of the primary electrons.
  • a yield rate greater than 1.0 indicates that more secondary electrons may be produced than the number of primary electrons that have landed on the wafer.
  • a yield rate of less than 1.0 indicates that less secondary electrons may be produced in response to the impact of the primary electrons.
  • An electron beam tool (e.g., electron beam tool 104 of Fig. 2) may generate a darker voltage contrast image of a device structure with a more positive surface potential since a detection device (e.g., detection device 240 of Fig. 2) may receive less secondary electrons (see Fig. 4).
  • BSE backscattered electrons
  • An electron beam tool (e.g., electron beam tool 104 of Fig. 2) may generate a brighter voltage contrast image of a device structure with a more negative surface potential a detection device (e.g., detection device 240 of Fig. 2) may receive more secondary electrons (see Fig. 4).
  • the landing energy of the primary electron beams may be controlled by the total bias between the electron source and the wafer.
  • Fig. 4 illustrates a schematic diagram of a voltage contrast response of a wafer, consistent with embodiments of the present disclosure.
  • physical and electrical defects in a wafer e.g., resistive shorts and opens, defects in deep trench capacitors, back end of line (BEOL) defects, etc.
  • BEOL back end of line
  • Defect detection using voltage contrast images may use a pre-scanning process (i.e., a charging, flooding, neutralization, or prepping process), where charged particles are applied to an area of the wafer (e.g., sample 208 of Fig. 2) to be inspected before conducting the inspection.
  • a pre-scanning process i.e., a charging, flooding, neutralization, or prepping process
  • an electron beam tool (e.g., electron beam tool 104 of Fig. 2) may be used to detect defects in internal or external structures of a wafer by illuminating the wafer with a plurality of beamlets of a primary electron beam (e.g., plurality of beamlets 211, 212, or 213 of primary electron beam 202 of Fig. 2) and measuring a voltage contrast response of the wafer to the illumination.
  • the wafer may comprise a test device region 420 that is developed on a substrate 410.
  • test device region 420 may include multiple device structures 430 and 440 separated by insulating material 450.
  • device structure 430 is connected to substrate 410.
  • device structure 440 is separated from substrate 410 by insulating material 450 such that a thin insulator structure 470 (e.g., thin oxide) exists between device structure 440 and substrate 410.
  • the electron beam tool may generate secondary electrons (e.g., secondary electron beams 261, 262, or 263 of Fig. 2) from the surface of test device region 420 by scanning the surface of test device region 420 with a plurality of beamlets of a primary electron beam.
  • secondary electrons e.g., secondary electron beams 261, 262, or 263 of Fig. 2
  • the landing energy of the primary electrons is between Ei and E2 (i.e., the yield rate is greater than 1.0 in Fig. 3)
  • more electrons may leave the surface of the wafer than land on the surface, thereby resulting in a positive electrical potential at the surface of the wafer.
  • a positive electrical potential may build-up at the surface of a wafer.
  • device structure 440 may retain more positive charges because device structure 440 is not connected to an electrical ground in substrate 410, thereby resulting in a positive electrical potential at the surface of device structure 440.
  • primary electrons with the same landing energy (i.e., the same yield rate) applied to device structure 430 may result in less positive charges retained in device structure 430 since positive charges may be neutralized by electrons supplied by the connection to substrate 410.
  • An image processing system e.g., controller 109 of Fig. 2 of an electron beam tool may generate voltage contrast images 435 and 445 of corresponding device structures 430 and 440, respectively.
  • device structure 430 is shorted to the ground and may not retain built-up positive charges. Accordingly, when primary electron beamlets land on the surface of the wafer during inspection, device structure 430 may repel more secondary electrons thereby resulting in a brighter voltage contrast image.
  • device structure 440 may retain a build-up of positive charges. This build-up of positive charges may cause device structure 440 to repel less secondary electrons during inspection, thereby resulting in a darker voltage contrast image.
  • An electron beam tool may pre-scan the surface of a wafer by supplying electrons to build up the electrical potential on the surface of the wafer. After pre-scanning the wafer, the electron beam tool may obtain images of multiple dies within the wafer. In some embodiments, defects may be detected by comparing the differences in voltage contrast images from multiple dies. For example, if non-uniform charging is applied to the wafer and the voltage contrast level of one image associated with a first node is the same as the voltage contrast level of an image associated with a second node, the die corresponding to the two voltage contrast levels may have an electrical short circuit defect. Pre-scanning is applied to the wafer under the assumption that the electrical surface potential built-up on the surface of the wafer during pre-scanning will be retained during inspection and will remain above the detection threshold of the electron beam tool.
  • the built-up surface potential level may change during inspection due to the effects of electrical breakdown or tunneling, thereby resulting in failure to detect defects.
  • a high voltage is applied to a high resistance thin device structure (e.g., thin oxide), such as an insulator structure 470
  • leakage current may flow through the high resistance structure, thereby preventing the structure from functioning as a perfect insulator. This may affect circuit functionality and result in a device defect.
  • a similar effect of leakage current may also occur in a structure with improperly formed materials or a high resistance metal layer, for example a cobalt silicide (e.g., CoSi, CoSil. Co ⁇ Si. Co;Si. etc.) layer between a tungsten plug and a source or drain area of a field-effect transistor (FET).
  • FET field-effect transistor
  • a defective etching process may leave a thin oxide resulting in unwanted electrical blockage (e.g., open circuit) between two structures (e.g., device structure 440 and substrate 410) intended to be electrically connected.
  • device structures 430 and 440 may be designed to make contact with substrate 410 and function identically, but due to manufacturing errors, insulator structure 470 may exist in device structure 440. In this case, insulator structure 470 may represent a defect susceptible to a breakdown effect.
  • VC voltage contrast
  • SE secondary electron
  • BSE backscattered electron
  • FIG. 5 shows image 510 generated from a sample under positive mode VC charging, image 520 under negative mode VC charging, and schematic 540 of image 520, in positive mode VC charging, the electron beam charges the sample positively and grounded metal contacts give bright signals compared to their surrounding oxide.
  • negative mode VC charging charging is negative, which leads to contacts appearing dark against a bright surrounding oxide 534, as well as the formation of dark rings 530a or 530b around contact holes 532 (e.g., ground or N+/P-well plug).
  • image 520 may be generated based on electrons 536 that hit the detector.
  • the oxide voltage of oxide 534 must be less than the plug voltage of contact hole 532. With grounded contacts, this condition is satisfied when the oxide has a negative potential.
  • electrons 538 may transfer to contact holes 532 to form dark rings 530a.
  • the disclosed embodiments of the present disclosure include methods to predict whether positive charging or negative charging will occur under certain conditions and methods to predict conditions under which positive charging or negative charging will occur.
  • the disclosed embodiments may include a model that predicts the charging mode or optimal parameters for a target charging mode based on beam and sample parameters, while considering the effect of beam density through the mechanism of space charge.
  • the disclosed embodiments may also consider the effects of pattern density on the surface charging of the sample, as well as VC formation.
  • Space charge refers to the electric field collectively generated by secondary electrons that have already been emitted and are in the vacuum above the sample. Primary and backscattered electrons contribute less to the field and are also less affected by it due to their high energy. The secondary electrons near the sample surface experience a downward force, which competes with the applied extraction field. Higher extracted SE current leads to stronger space charge fields, and when the space charge field is equal to the electrode (lens component of microscope) extraction field (field of sample a microscope), there is no net extraction field anymore. This is referred to as the space charge limit or saturation current. Because of this limitation on the secondary electron current, a reduced electron yield can be expected, and even a transition from positive to negative charging is possible.
  • the space charge limited current I sat for secondary electron (SE) current (e.g., maximum amount of SEs that can be extracted) may be described by the following example equation: (Equation 1), where r SE FM/50 is the emission radius of the SEs, obtained by convoluting the primary beam spot size, and the scattering range of SEs. m e is electron mass and q is the electron charge.
  • Fig. 6 shows an exemplary graph 600 of scattering range of SEs 602 as a function of landing energy (LE) (voltage difference between electron source and sample), consistent with embodiments of the present disclosure.
  • LE landing energy
  • Graph 600 may be a simulation for a 0 nm primary beam radius, SiCL material, and incident beam radius 0 nm. Scattering range of SEs 602 may be used to determine the emission radius of the SEs r SE FW50 .
  • the electric field strength E No5 is the extraction field applied with electrode (lens component of charged particle system). This field is surface voltage dependent in this case as it is equal to the sum of the applied field that follows from LE and electrode potential settings, and an extra contribution that depends on the surface voltage V sur f and the distance between the sample and electrode d No5 , which may be described by the following example equation: (Equation 2).
  • FIG. 7 shows an exemplary sample 702 in a charged particle system 700, consistent with embodiments of the present disclosure.
  • I SE may be the minimum of a “normal” SE current and a space charge limited SE current.
  • “Normal” SE current may be expressed as where I p is the primary beam current, 6 0 is the base SE yield (which depends on landing energy (LE)), q is the electron charge, Heaviside is a step function, i s a surface potential of a sample, and is a plug material work function.
  • Space charge limited SE current may be expressed where 6 0 is vacuum permittivity, q is the electron charge, m e is electron mass, E no5 is extraction field strength, V sur f is a surface potential of a sample, d no 5 is a distance between an electrode and a sample, and GE.FWSO is SE emission spot radius (which depends on the primary beam size and LE).
  • leakage current may be through a diode or other junction or tunneling mechanism, which may have non-linear conductance and are typically not characterized with a resistance.
  • Fig. 8 shows an exemplary graph 800 of primary beam current 802 as a function of electrode potential (potential of a lens component in the charged particle system) 804 and surface voltage 806, consistent with embodiments of the present disclosure.
  • Graph 800 shows the model solution for a 20 nm electron beam spot diameter, leakage resistance of 10 GOhm, SE and BSE yields of 1.5 and 0.2, and extraction fields calculated for an exemplary charged particle system.
  • Line 820 indicates a surface potential of zero.
  • low beam current and high extraction results in a positive surface potential (V sur f > 0).
  • High beam current and low extraction results in a negative surface potential (V sur f ⁇ 0).
  • Fig. 9 shows an exemplary graph 900 of exemplary generated SEM images 906 at various landing energies (LE) 902 and beam densities 904 (may be obtained from current focus or beam spot size), consistent with embodiments of the present disclosure.
  • Line 920 indicates an estimate of the transition between positive VC mode and negative VC mode.
  • Graph 900 may include experimental data (e.g., generated SEM images 906), region 908 where the sample in the generated images has a positive surface potential (V sur f > 0, positive VC mode), and region 910 where the sample in the generated images has a negative surface potential (V sur f ⁇ 0, negative VC mode).
  • experimental data e.g., generated SEM images 906
  • region 908 where the sample in the generated images has a positive surface potential (V sur f > 0, positive VC mode)
  • region 910 where the sample in the generated images has a negative surface potential (V sur f ⁇ 0, negative VC mode).
  • Fig. 10 shows an exemplary graph 1000 of surface voltage 1010 as a function of landing energy (LE) 1002 and beam density 1004, consistent with embodiments of the present disclosure.
  • Line 1020 indicates a surface potential of zero.
  • Graph 1000 shows the model solution for an electrode potential of zero in an exemplary charged particle system.
  • Graph 1000 may include region 1008, where the model predicts a sample has a positive surface potential (V sur f > 0), and region 1006 where the model predicts a sample has a negative surface potential (V sur f ⁇ 0).
  • FIG. 9-10 shows conditions and parameters at which negative mode VC charging (V sur f ⁇ 0) or positive mode VC charging (V sur f > 0) may be achieved.
  • negative mode VC charging may be achieved by increasing the landing energy (LE) and increasing the beam density to the space charge limit.
  • Positive mode VC charging may be achieved by decreasing the LE and decreasing the beam density.
  • Fig. 11 shows exemplary graphs 1110, 1120, 1130, and 1140 of surface voltage as a function of landing energy (LE) and beam density, consistent with embodiments of the present disclosure.
  • Lines 1112, 1122, 1132, and 1142 indicate a surface potential of zero.
  • Electrodes 1110, 1120, 1130, and 1140 show the model solutions for an electrode potential of -500 V, zero V, +500 V, and +1000 V, respectively, in an exemplary charged particle system.
  • electrode potential is another charged particle system parameter (e.g., SEM parameter) that may be included in the model.
  • Fig. 12 shows a graph 1200 of electric potential 1202 (gradient) and electric fields 1204 (arrows), consistent with embodiments of the present disclosure.
  • Pattern density or pattern fill factor is a determining factor for the formation of negative mode VC charging. It is more difficult to achieve negative mode VC charging with samples with denser patterns (lower pitches or distances between features) than with sparse patterns (higher pitches or distances between features).
  • the model of embodiments of the present disclosure incorporates the effect of pattern density on surface voltage through the consideration of local fields between oxide and plugs.
  • Graph 1200 includes two grounded plugs 1208 and 1210 surrounded by oxide at -IV. SEs are influenced by ‘global’ fields 1212 (smaller arrows) mainly set by electrode and local extraction fields 1214 (larger arrows) governed by the potential difference between plugs and oxide. Close to the sample, where the space charge interaction takes place, local fields are the strongest between local fields and global fields.
  • the local fields are stronger for denser patterns.
  • the formation of a negative surface voltage now increases the extraction field, thereby increasing the saturation current, and thus limiting the accumulation of more negative voltage.
  • the local fields are stronger in denser patterns, the negative voltage accumulated on dense patterns is lower than that on sparser patterns.
  • Fig. 13 shows a graph 1300 of collection efficiency 1302 of SEs released from oxide close to the edge of a grounded contact as a function of oxide potential 1304, consistent with embodiments of the present disclosure.
  • a second effect of pattern density is related to the formation of the dark rings associated with negative mode VC charging (see, e.g., Fig. 5). Visibility of these dark rings requires a drop in collection efficiency for electrons released close to the edge of a plug. This collection efficiency not only depends on voltage, but also on pitch between contacts (see legend 1306), as shown in Fig. 13. More negative surface voltages are required for negative mode VC charging on denser patterns (e.g., smaller pitch) compared to sparser patterns (e.g., higher pitch).
  • both a sufficiently negative oxide voltage and sufficiently low pattern density are required.
  • a detection threshold 1308 dark rings are not generated in images due to the brightness of signal in these regions not being sufficiently lower than the brightness signals of the rest of the sample.
  • dark rings in generated images become clearer. That is, in areas of the sample where the dark rings could be generated in the images, less SEs are collected/detected by the detector, thereby resulting in a lower collection efficiency and darker/clearer rings.
  • With lower (more negative) oxide voltages more electrons transfer away from the oxide, such that the ring area is left with a more positive potential and is darker in generated images.
  • Fig. 14 shows exemplary graphs 1410 and 1420 of surface voltage as a function of landing energy (LE) and beam density, consistent with embodiments of the present disclosure.
  • Lines 1412 and 1422 indicate a surface potential of zero.
  • Lines 1414 and 1424 indicate a surface potential at which dark rings in negative mode VC charging may be visible (as discussed in Fig. 13).
  • Graphs 1410 and 1420 show the model solutions for an electrode potential of zero V and 500 nm pitch and zero V and 1500 nm pitch, respectively, in an exemplary charged particle system.
  • Graphs 1410 and 1420 show ranges of parameters at which certain charging modes may be achieved. For example, graph 1410 shows region 1416 where positive mode VC charging may be achieved, region 1417 where negative mode VC charging may be achieved (without visible dark rings in the generated images), and region 1418 where negative mode VC charging may be achieved with visible dark rings in the generated images.
  • Fig. 15 shows an exemplary graph 1500 of saturation current 1506 as a function of extraction field strength 1502 and beam spot size 1504 at a landing energy (LE) of 1000 eV, consistent with embodiments of the present disclosure.
  • embodiments of the present disclosure may include models for the following applications: A recipe generator software solution that determines beam settings for positive/negative mode VC charging, reference material for engineers in manual condition tuning, determining requirements for future tools in terms of current (density) and extraction field (e.g., Fig. 15), and validation of the existing methods of beam density induced positive/negative mode VC charging.
  • Fig. 16 shows an exemplary process 1600 for predicting a charging mode of a charged particle system or for predicting parameters for a target charging mode of a charged particle system, consistent with embodiments of the present disclosure.
  • a system may input a plurality of parameters into a model.
  • the plurality of parameters may include charged particle system parameters (see, e.g., Fig. 6 and corresponding description), a surface potential of a sample (see, e.g., Figs. 7-11 and corresponding descriptions), a pattern density of the sample (see, e.g., Fig. 12 and corresponding description), a collection efficiency of secondary electrons (SEs) from an oxide of the sample (see, e.g., Fig. 13 and corresponding description); and a potential of the oxide (see, e.g., Fig. 13 and corresponding description).
  • charged particle system parameters see, e.g., Fig. 6 and corresponding description
  • a surface potential of a sample see, e.g., Figs. 7-11 and corresponding descriptions
  • a pattern density of the sample see, e.g., Fig. 12 and corresponding description
  • SEs secondary electrons
  • the charged particle system parameters may include a space charge limited current, where the space charge limited current is a maximum quantity of SEs that may be extracted from an environment of the charged particle system.
  • determining the space charge limited current may include determining an electric field strength in the charged particle system and determining an emission radius of SEs.
  • determining the electric field may include determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the applied electric field strength may be based on a landing energy (LE) in the charged particle system and parameters associated with the lens component.
  • determining the emission radius of SEs may be based on a primary beam spot size and a scattering range of SEs.
  • determining the surface potential of the sample may be based on a primary electron beam current, a backscattered electron (BSE) current, a SE current, a stage current, a base SE yield, and a resistance of the sample.
  • the stage current may be a leakage current through a stage holding the sample.
  • determining the base SE yield is based on a LE.
  • the pattern density of the sample may be based on distances between features on a sample.
  • the features may include contacts.
  • the features may include plugs.
  • the system may generate, using the model, a plot for predicting the charging mode of the charged particle system or optimal parameters for the target charging mode of the charged particle system.
  • a lower pattern density may result in predicting a negative charging mode (see, e.g., Fig. 13 and corresponding description).
  • a lower collection efficiency of SEs from the oxide of the sample may result in predicting a negative charging mode.
  • the plot may include a plot of a range of primary beam currents, a range of lens component potentials, and a range of surface potentials of the sample (see, e.g., Fig. 8 and corresponding description).
  • the plot may include a plot of a range of landing energies (LEs), a range of beam densities, and a range of surface potentials of the sample (see, e.g., Figs.
  • the plot may include a plot of a range of LEs, a range of beam densities, a range of surface potentials of the sample, and a range of threshold values associated with visibility of ring-shaped regions in generated images (see, e.g., Fig. 14 and corresponding description).
  • the range of threshold values may be determined based on the collection efficiency of SEs from an oxide of the sample and a potential of the oxide (see, e.g., Figs. 13-14 and corresponding description).
  • the ring-shaped regions may be associated with regions between features of the sample and oxide of the sample (see, e.g., Fig. 5 and corresponding description).
  • the absence of the ring-shaped regions in generated images may be associated with defects of the sample.
  • the plot may include a plot of a range of extraction field strengths, a range of beam spot sizes, and a range of space charge limited currents (see, e.g., Fig. 15 and corresponding description).
  • the system may predict, based on the plot, the charging mode of the charged particle system or the optimal parameters for the target charging mode of the charged particle system.
  • the predicted charging mode may be a negative charging mode or a positive charging mode depending on the results at step 1604.
  • the predicted optimal parameters may include one or more of primary beam current (see, e.g., Fig. 8 and corresponding description), lens component potential (see, e.g., Fig. 8 and corresponding description), landing energy (LE) (see, e.g., Figs. 9-10 and corresponding description), beam density (see, e.g., Figs. 9-10 and corresponding description), extraction field strength (see, e.g., Fig. 15 and corresponding description), or beam spot size (see, e.g., Fig. 15 and corresponding description).
  • primary beam current see, e.g., Fig. 8 and corresponding description
  • lens component potential see, e.g., Fig. 8 and corresponding description
  • landing energy (LE) see, e.g., Figs. 9-10 and corresponding description
  • beam density see, e.g., Figs. 9-10
  • the predicted optimal parameters may cause an increase in surface potential of the sample (see, e.g., Fig. 13 and corresponding description).
  • the predicted optimal parameters may cause a decrease in potential of the oxide of the sample (see, e.g., Fig. 13 and corresponding description).
  • a higher primary beam current may result in a lower (more negative) surface potential of the sample (see, e.g., Fig. 8 and corresponding description).
  • a lower lens component potential may result in a lower (more negative) surface potential of the sample (see, e.g., Fig. 8 and corresponding description).
  • a higher beam density may result in a lower (more negative) surface potential of the sample (see, e.g., Figs. 9-10 and corresponding description).
  • a higher LE may result in a lower (more negative) surface potential of the sample (see, e.g., Figs. 9-10 and corresponding description).
  • a higher lens component potential may result in a positive surface potential of the sample over an increased range of beam densities and LEs (see, e.g., Fig. 11 and corresponding description).
  • a lower extraction field strength may result in a lower space charge limited current (see, e.g., Figs. 6 and 15 and corresponding descriptions).
  • a lower beam spot size may result in a lower space charge limited current (see, e.g., Figs. 6 and 15 and corresponding descriptions).
  • a non-transitory computer readable medium may be provided that stores instructions for a processor of a controller (e.g., controller 109 of Fig. 1) for controlling the electron beam tool or other systems of other systems and servers, or components thereof, consistent with embodiments in the present disclosure. These instructions may allow the one or more processors to carry out image processing, data processing, beamlet scanning, graphical display, operations of a charged particle beam apparatus, or another imaging device, or the like for providing operations consistent with those described above for Figs. 6-15.
  • a processor of a controller e.g., controller 109 of Fig. 1
  • These instructions may allow the one or more processors to carry out image processing, data processing, beamlet scanning, graphical display, operations of a charged particle beam apparatus, or another imaging device, or the like for providing operations consistent with those described above for Figs. 6-15.
  • non-transitory computer readable medium may be provided that stores instructions for a processor to perform the steps of process 1600.
  • Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a Compact Disc Read Only Memory (CD-ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read Only Memory (PROM), and Erasable Programmable Read Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non- Volatile Random Access Memory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same.
  • NVRAM Non- Volatile Random Access Memory
  • a method of predicting a charging mode of a charged particle system comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting the charging mode of the charged particle system; and predicting, based on the plot, the charging mode of the charged particle system.
  • the plurality of parameters comprise: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system. 7. The method of clause 6, wherein determining the applied electric field strength is based on a landing energy (LE) in the charged particle system and parameters associated with the lens component.
  • LE landing energy
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • stage current comprises a leakage current through a stage holding the sample.
  • the plot includes a plot of a range of LEs, a range of beam densities, a range of surface potentials of the sample, and a range of threshold values associated with visibility of ring-shaped regions in generated images.
  • the predicted optimal parameters comprise one or more of primary beam current, lens component potential, landing energy (LE), beam density, extraction field strength, or beam spot size.
  • a method of predicting parameters for a target charging mode of a charged particle system comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting optimal parameters for the target charging mode of the charged particle system; and predicting, based on the plot, the optimal parameters for the target charging mode of the charged particle system.
  • the plurality of parameters comprising: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method of predicting a charging mode of a charged particle system, the method comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting the charging mode of the charged particle system; and predicting, based on the plot, the charging mode of the charged particle system.
  • the plurality of parameters comprise: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the applied electric field strength is based on a landing energy (LE) in the charged particle system and parameters associated with the lens component.
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • stage current comprises a leakage current through a stage holding the sample.
  • the predicted optimal parameters comprise one or more of primary beam current, lens component potential, landing energy (LE), beam density, extraction field strength, or beam spot size.
  • a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method of predicting parameters for a target charging mode of a charged particle system, the method comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting optimal parameters for the target charging mode of the charged particle system; and predicting, based on the plot, the optimal parameters for the target charging mode of the charged particle system.
  • the plurality of parameters comprising: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • stage current comprises a leakage current through a stage holding the sample.
  • a system to predict a charging mode of a charged particle system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting the charging mode of the charged particle system; and predicting, based on the plot, the charging mode of the charged particle system.
  • the plurality of parameters comprise: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the applied electric field strength is based on a landing energy (LE) in the charged particle system and parameters associated with the lens component.
  • LE landing energy
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • stage current comprises a leakage current through a stage holding the sample.
  • the predicted optimal parameters comprise one or more of primary beam current, lens component potential, landing energy (LE), beam density, extraction field strength, or beam spot size.
  • a system to predict parameters for a target charging mode of a charged particle system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising: inputting a plurality of parameters into a model; generating, using the model, a plot for predicting optimal parameters for the target charging mode of the charged particle system; and predicting, based on the plot, the optimal parameters for the target charging mode of the charged particle system.
  • the plurality of parameters comprising: charged particle system parameters; a surface potential of a sample; a pattern density of the sample; a collection efficiency of secondary electrons (SEs) from an oxide of the sample; and a potential of the oxide.
  • SEs secondary electrons
  • determining the electric field comprises determining an applied electric field strength in the charged particle system, the surface potential of the sample, and a distance between the sample and a lens component of the charged particle system.
  • determining the applied electric field strength is based on a landing energy (LE) in the charged particle system and parameters associated with the lens component.
  • determining the surface potential of the sample is based on at least one of a primary electron beam current, backscattered electron current, SE current, a stage current, a base SE yield, and a resistance of the sample.
  • stage current comprises a leakage current through a stage holding the sample.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)

Abstract

Systèmes et procédés de prédiction d'un mode de charge d'un système de particules chargées. Les procédés peuvent comprendre l'entrée d'une pluralité de paramètres dans un modèle; la génération, à l'aide du modèle, d'un tracé pour prédire le mode de charge du système de particules chargées; et la prédiction, sur la base du tracé, du mode de charge du système de particules chargées.
PCT/EP2024/077743 2023-10-25 2024-10-02 Systèmes et procédés de prédiction de mode de charge et de paramètres optimaux pour contraste de tension Pending WO2025087668A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3783636A1 (fr) * 2019-08-19 2021-02-24 ASML Netherlands B.V. Compensation numérique de charge induite par microscope électronique a balyage, la compensation utilisante un modèle basé sur diffusion
EP3961473A1 (fr) * 2020-08-29 2022-03-02 ASML Netherlands B.V. Modélisation physique de gravure multi-échelle et procédés associés

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3783636A1 (fr) * 2019-08-19 2021-02-24 ASML Netherlands B.V. Compensation numérique de charge induite par microscope électronique a balyage, la compensation utilisante un modèle basé sur diffusion
EP3961473A1 (fr) * 2020-08-29 2022-03-02 ASML Netherlands B.V. Modélisation physique de gravure multi-échelle et procédés associés

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HAND ET AL.: "Principles of Data Mining (Adaptive Computation and Machine Learning", 2001, MIT PRESS, pages: 578
JEBARA: "Discriminative, Generative, and Imitative Learning", MIT THESIS, 2002, pages 212
SUGIYAMAMORGAN KAUFMANN, INTRODUCTION TO STATISTICAL MACHINE LEARNING, 2016, pages 534

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