Parameterized inspection image simulation

Abstract

An improved method, apparatus, and system for generating a simulated inspection image is disclosed. According to certain aspects, the method includes acquiring design data including a first pattern, generating a first gray-level profile corresponding to the design data, and rendering an image using the generated first gray-level profile.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application No. 63 / 411,040, filed September 28, 2022, and incorporated herein by reference in its entirety.

[0002] FIELD OF THE INVENTION

[0002] Embodiments provided herein relate to inspection image simulation techniques, and more particularly to generating parameterized simulated inspection images from a layout design. [Background technology]

[0003]

[0003] In the manufacturing process of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure they are manufactured as designed and are free of defects. Inspection systems utilizing optical microscopes such as scanning electron microscopes (SEMs) or charged particle (e.g., electron) beam microscopes may be used. As the physical size of IC components continues to shrink, accuracy and yield in defect detection become more important. Various metrology tools have been developed and are used to verify whether ICs are manufactured correctly. To improve defect inspection performance, it is desirable to verify / quantify such metrology tools using a sufficient number of inspection images with a variety of patterns, sizes, and densities. Summary of the Invention

[0004]

[0004] The embodiments provided herein disclose a particle beam inspection system, and more particularly, an inspection system that uses multiple beams of charged particles.

[0005] Some embodiments provide an apparatus for generating a simulated inspection image, which may include a memory storing a set of instructions and at least one processor configured to execute the set of instructions to cause the apparatus to obtain design data including a first pattern, generate a first gray-level profile corresponding to the design data, and render an image using the generated first gray-level profile.

[0006] Some embodiments provide a non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method including obtaining design data including a first pattern, generating a first gray-level profile corresponding to the design data, and rendering an image using the generated first gray-level profile.

[0007]

[0007] Other advantages of embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings, in which certain embodiments of the present invention are set forth by way of illustration and example.

[0008]

[0008] The above and other aspects of the present disclosure will become more apparent from the description of illustrative embodiments taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram illustrating an exemplary charged particle beam inspection system consistent with an embodiment of the present disclosure. [Figure 2]

[0010] 2 is a schematic diagram illustrating an example multi-beam tool that may be part of the example charged particle beam inspection system of FIG. 1, consistent with an embodiment of the present disclosure. [Figure 3]

[0011] FIG. 1 is a block diagram of an exemplary inspection image simulation system consistent with an embodiment of the present disclosure. [Figure 4A]

[0012] 1 illustrates an exemplary procedure for inspection image simulation, consistent with an embodiment of the present disclosure. [Figure 4B] 1 illustrates an exemplary procedure for inspecting image simulation consistent with an embodiment of the present disclosure. [Figure 4C] 1 illustrates an exemplary procedure for inspecting image simulation consistent with an embodiment of the present disclosure. [Figure 4D] 1 illustrates an exemplary procedure for inspecting image simulation consistent with an embodiment of the present disclosure. [Figure 5]

[0013] FIG. 1 is a block diagram of an exemplary gray level profile generation system consistent with an embodiment of the present disclosure. [Figure 6A]

[0014] 1 illustrates an exemplary procedure for generating a gray-level profile, consistent with an embodiment of the present disclosure. [Figure 6B] 1 illustrates an exemplary procedure for generating a gray level profile, consistent with an embodiment of the present disclosure. [Figure 6C] 1 illustrates an exemplary procedure for generating a gray level profile, consistent with an embodiment of the present disclosure. [Figure 7A]

[0015] 1 illustrates an exemplary performance evaluation of an inspection image simulation system, consistent with an embodiment of the present disclosure. [Figure 7B]

[0016] 10A-10C illustrate exemplary simulated images of various patterns generated using an inspection image simulation system consistent with embodiments of the present disclosure. [Figure 7C] 10A-10C illustrate exemplary simulated images of various patterns generated using an inspection image simulation system consistent with embodiments of the present disclosure. [Figure 8]

[0017] 1 is a process flowchart depicting an exemplary method for simulating an inspection image, consistent with an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010]

[0018] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, in which like numbers in different drawings represent the same or similar elements unless otherwise noted. Implementations described in the following description of exemplary embodiments do not represent all implementations. Instead, they are merely examples of apparatus and methods consistent with aspects related to the disclosed embodiments recited in the appended claims. For example, although some embodiments are described in connection with the use of electron beams, the present disclosure is not so limited. Other types of charged particle beams (e.g., including protons, ions, muons, or any other particles carrying an electric charge) may be similarly applied. Furthermore, other imaging systems, such as optical imaging, photon detection, x-ray detection, ion detection, etc., may also be used.

[0011]

[0019] Electronic devices are composed of circuits formed on a piece of semiconductor material called a substrate. The semiconductor material can include, for example, silicon, gallium arsenide, indium phosphide, or silicon germanium. Many circuits can be formed together on the same piece of silicon and are called integrated circuits, or ICs. The dimensions of these circuits have been dramatically reduced so that many more circuits can fit on a substrate. For example, the IC chip in a smartphone can be as small as a thumbnail, yet contain over 2 billion transistors, each smaller than 1 / 1000 the size of a human hair.

[0012]

[0020] Fabricating ICs with these tiny structures or components is a complex, time-consuming, and expensive process, often involving hundreds of individual steps. An error in just one step can result in a defect in the finished IC, rendering it unusable. Therefore, one of the goals of a manufacturing process is to avoid such defects and maximize the number of functional ICs produced by the process, i.e., to improve the overall yield of the process.

[0013]

[0021] One component of improving yield is monitoring the chip fabrication process to ensure that a sufficient number of functional integrated circuits are being produced. One way to monitor the process is to inspect the chip circuit structures at various stages in their formation. Inspection can be done using a scanning charged particle microscope (SCPM). For example, an SCPM can be a scanning electron microscope (SEM). An SCPM can actually take a "picture" of the structures on the wafer and can be used to image those tiny structures. This image can be used to determine if the structures were properly formed in the correct location. If the structures are defective, the process can be adjusted to make the defect less likely to recur.

[0014]

[0022] As the physical size of IC components continues to shrink, accuracy and yield in defect detection become more important. Metrology tools can be used to determine whether an IC is manufactured correctly by measuring the critical dimensions, curvature, roughness, etc. of structures on a wafer. Such measurements may be based on the contours of structures extracted by a contour extraction tool, which may be part of the metrology tool. To improve defect inspection accuracy, it is important to accurately verify / quantify the metrology tools. Furthermore, various metrology tools have been developed, and the decision to use a metrology tool from among the various metrology tools may be based on its performance, such as accuracy and throughput. Because metrology tool performance can vary depending on the pattern, size, density, etc., it is desirable to test the metrology tool using a sufficient number of inspection images with various patterns, sizes, and densities to accurately verify / quantify the metrology tool. However, acquiring a sufficient number of inspection images with various patterns, sizes, and densities can be time-consuming, costly, or even impossible.

[0015]

[0023] There are several SCPM simulators on the market, such as Hyperlith and eScatter, but these simulators are based on physical modeling of the beam. Such physical model-based simulators are generally time-inefficient and even incapable of generating a sufficient number of simulated SCPM images with a variety of patterns, sizes, and densities. Furthermore, the output of these SCPM simulators is incompatible with some metrology tools.

[0016]

[0024] Embodiments of the present disclosure may provide a parameterized SCPM image simulator. According to some embodiments of the present disclosure, simulated inspection images incorporate metrology-related parameters that can be defined and determined by a user. According to some embodiments of the present disclosure, simulated inspection images may be generated using gray-level profile data extracted from real images (i.e., non-simulated images) or simulated images based on physical models, or using user-defined gray-level profile data. In some embodiments, the gray-level profile data may be derived from user-defined gray-level profile data. According to some embodiments of the present disclosure, simulated inspection images may be controlled using parameters related to edge roughness, gray-level profile, distortion, contrast, etc. According to some embodiments of the present disclosure, inspection images with complex patterns may be simulated that may be unachievable with existing physical model-based simulators. According to some embodiments of the present disclosure, inspection images may be simulated much faster than existing physical model-based simulators.

[0017]

[0025] The relative dimensions of components in the drawings may be exaggerated for clarity. In the following description of the drawings, the same or similar reference numbers refer to the same or similar components or entities, and only the differences with respect to individual embodiments are described. As used herein, unless otherwise stated, the term "or" encompasses all possible combinations unless impracticable. For example, if a component is described as including A or B, the component may include A, or B, or A and B, unless otherwise stated or impracticable. As a second example, if a component is described as including A, B, or C, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A, B, and C, unless otherwise stated or impracticable.

[0018]

[0026] FIG. 1 illustrates an exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure. The EBI system 100 can be used for imaging. As shown in FIG. 1, the EBI system 100 includes a main chamber 101, a load / lock chamber 102, a beam tool 104, and an equipment front-end module (EFEM) 106. The beam tool 104 is disposed within the main chamber 101. The EFEM 106 includes a first load port 106a and a second load port 106b. The EFEM 106 may include additional load ports. The first load port 106a and the second load port 106b receive wafer front-opening unified pods (FOUPs) containing wafers (e.g., semiconductor wafers or wafers made of other materials) or samples (wafers and samples may be used interchangeably) to be inspected. A "lot" is a plurality of wafers that can be loaded for processing as a batch.

[0019]

[0027] One or more robot arms (not shown) in the EFEM 106 can transfer wafers to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pumping system (not shown), which removes gas molecules from the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After the first pressure is reached, one or more robot arms (not shown) can transfer the wafers from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pumping system (not shown), which removes gas molecules from the main chamber 101 to reach a second pressure lower than the first pressure. After the second pressure is reached, the wafers are subjected to inspection by the beam tool 104. The beam tool 104 can be a single beam system or a multi-beam system.

[0020]

[0028] A controller 109 is electronically connected to the beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. While the controller 109 is illustrated in Figure 1 as being external to the structure including the main chamber 101, the load / lock chamber 102, and the EFEM 106, it will be understood that the controller 109 may be part of the structure.

[0021]

[0029] In some embodiments, the controller 109 may include one or more processors (not shown). A processor may be a general-purpose or specialized electronic device capable of manipulating or processing information. For example, a processor may include any number of central processing units (i.e., "CPUs"), graphics processing units (i.e., "GPUs"), optical processors, programmable logic controllers, microcontrollers, microprocessors, digital signal processors, intellectual property (IP) cores, programmable logic arrays (PLAs), programmable array logic (PALs), general-purpose array logic (GALs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), and any combination of any type of circuitry capable of processing data. A processor may also be a virtual processor, including one or more processors distributed across multiple machines or devices coupled via a network.

[0022]

[0030] In some embodiments, the controller 109 may further include one or more memories (not shown). Memory may be a general-purpose or specialized electronic device capable of storing code and data accessible to a processor (e.g., via a bus). For example, memory may include any number of random access memories (RAMs), read-only memories (ROMs), optical disks, magnetic disks, hard drives, solid-state drives, flash drives, security digital (SD) cards, memory sticks, compact flash (CF) cards, or any combination of any type of storage device. The code and data may include an operating system (OS) and one or more application programs (i.e., "apps") for specific tasks. Memory may also be a virtual memory, which includes one or more memories distributed across multiple machines or devices coupled via a network.

[0023]

[0031] FIG. 2 shows a schematic diagram of an exemplary multi-beam tool 104 (also referred to herein as apparatus 104) and image processing system 290 that may be configured for use in EBI system 100 (FIG. 1) consistent with an embodiment of the present disclosure.

[0024]

[0032] The beam tool 104 includes a charged particle source 202, a gun aperture 204, a condenser lens 206, a primary charged particle beam 210 emitted from the charged particle source 202, a source conversion unit 212, multiple beamlets 214, 216, and 218 of the primary charged particle beam 210, a primary projection optics 220, a motorized wafer stage 280, a wafer holder 282, multiple secondary charged particle beams 236, 238, and 240, a secondary optics 242, and a charged particle detection device 244. The primary projection optics 220 may include a beam separator 222, a deflection scanning unit 226, and an objective lens 228. The charged particle detection device 244 may include detection subregions 246, 248, and 250.

[0025]

[0033] The charged particle source 202, the gun aperture 204, the condenser lens 206, the source conversion unit 212, the beam separator 222, the deflection scanning unit 226, and the objective lens 228 may be aligned with a primary optical axis 260 of the apparatus 104. The secondary optics 242 and the charged particle detection device 244 may be aligned with a secondary optical axis 252 of the apparatus 104.

[0026]

[0034] The charged particle source 202 can emit one or more charged particles, such as electrons, protons, ions, muons, or any other particles that carry an electric charge. In some embodiments, the charged particle source 202 can be an electron emitter. For example, the charged particle source 202 can include a cathode, an extractor, or an anode, and primary electrons can be emitted from the cathode and extracted or accelerated to form a primary charged particle beam 210 (in this case, a primary electron beam) with a (virtual or real) crossover 208. For ease of explanation and to avoid ambiguity, some of the descriptions herein use electrons as an example. However, it should be noted that any charged particle, not limited to electrons, can be used in any embodiment of the present disclosure. The primary charged particle beam 210 can be visualized as it is being emitted from the crossover 208. The gun aperture 204 can block charged particles surrounding the primary charged particle beam 210 to reduce the Coulomb effect, which can cause an increase in the size of the probe spot.

[0027]

[0035] The source conversion unit 212 may include an array of image-forming elements and an array of beam-limiting apertures. The array of image-forming elements may include an array of micro-deflectors or micro-lenses. The array of image-forming elements may form multiple (virtual or real) parallel images of the crossover 208 with the multiple beamlets 214, 216, and 218 of the primary charged particle beam 210. The array of beam-limiting apertures may limit the multiple beamlets 214, 216, and 218. Although three beamlets 214, 216, and 218 are shown in FIG. 2 , embodiments of the present disclosure are not limited thereto. For example, in some embodiments, the apparatus 104 may be configured to generate a first number of beamlets. In some embodiments, the first number of beamlets may be in the range of 1 to 1000. In some embodiments, the first number of beamlets may be in the range of 200 to 500. In an exemplary embodiment, the apparatus 104 may generate 400 beamlets.

[0028]

[0036] The condenser lens 206 can focus the primary charged particle beam 210. The currents of the beamlets 214, 216, and 218 downstream of the source conversion unit 212 can be varied by adjusting the focusing power of the condenser lens 206 or by changing the radius of the corresponding beam-limiting apertures in the array of beam-limiting apertures. The objective lens 228 can focus the beamlets 214, 216, and 218 onto a wafer 230 for imaging, and can form multiple probe spots 270, 272, and 274 on the surface of the wafer 230.

[0029]

[0037] The beam separator 222 may be a Wien filter-type beam separator that generates an electrostatic dipole field and a magnetic dipole field. In some embodiments, when applied, the force that the electrostatic dipole field exerts on the charged particles (e.g., electrons) in the beamlets 214, 216, and 218 may be substantially equal in magnitude and opposite in direction to the force that the magnetic dipole field exerts on the charged particles. Thus, the beamlets 214, 216, and 218 may pass through the beam separator 222 in a straight line with a zero deflection angle. However, the total dispersion of the beamlets 214, 216, and 218 generated by the beam separator 222 may also be non-zero. The beam separator 222 may separate the secondary charged particle beams 236, 238, and 240 from the beamlets 214, 216, and 218 and direct the secondary charged particle beams 236, 238, and 240 to the secondary optics 242.

[0030]

[0038] The deflection scanning unit 226 can deflect the beamlets 214, 216, and 218 to scan probe spots 270, 272, and 274 on the surface area of ​​the wafer 230. In response to the beamlets 214, 216, and 218 impinging on the probe spots 270, 272, and 274, secondary charged particle beams 236, 238, and 240 can be emitted from the wafer 230. The secondary charged particle beams 236, 238, and 240 can include charged particles (e.g., electrons) having a distribution of energies. For example, the secondary charged particle beams 236, 238, and 240 can be secondary electron beams including secondary electrons (energy ≦50 eV) and backscattered electrons (energy between 50 eV and the landing energy of the beamlets 214, 216, and 218). The secondary optics 242 can focus the secondary charged particle beams 236, 238, and 240 onto detection subregions 246, 248, and 250 of the charged particle detection device 244. The detection subregions 246, 248, and 250 can be configured to detect the corresponding secondary charged particle beams 236, 238, and 240 and generate corresponding signals (e.g., voltages, currents, etc.) that are used to reconstruct SCPM images of structures on or below the surface area of ​​the wafer 230.

[0031]

[0039] The signals generated may represent the intensities of the secondary charged particle beams 236, 238, and 240 and may be provided to an image processing system 290 in communication with the charged particle detection device 244, the primary projection optics 220, and the motorized wafer stage 280. The speed of movement of the motorized wafer stage 280 may be synchronized and adjusted with the beam deflection controlled by the deflection scanning unit 226 so that the movement of the scanning probe spots (e.g., scanning probe spots 270, 272, and 274) systematically covers the area of ​​interest on the wafer 230. Such synchronization and adjustment parameters may be adjusted to accommodate various materials of the wafer 230. For example, different materials of the wafer 230 may have different resistance-capacitance characteristics that may cause different signal sensitivities to the movement of the scanning probe spots.

[0032]

[0040] The intensities of the secondary charged particle beams 236, 238, and 240 may vary depending on the external or internal structure of the wafer 230 and may therefore indicate whether the wafer 230 contains defects. Furthermore, as described above, the beamlets 214, 216, and 218 may be projected onto different locations on the top surface of the wafer 230 or onto different sides of a local structure of the wafer 230 to generate secondary charged particle beams 236, 238, and 240 that may have different intensities. Thus, by mapping the intensities of the secondary charged particle beams 236, 238, and 240 with areas of the wafer 230, the image processing system 290 can reconstruct an image that reflects the characteristics of the internal or external structure of the wafer 230.

[0033]

[0041] In some embodiments, the image processing system 290 may include an image acquirer 292, storage 294, and a controller 296. The image acquirer 292 may include one or more processors. For example, the image acquirer 292 may include a computer, a server, a mainframe host, a terminal, a personal computer, any type of mobile computing device, etc., or a combination thereof. The image acquirer 292 may be communicatively coupled to the charged particle detection device 244 of the beam tool 104 via a medium such as an electrical conductor, a fiber optic cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, wireless radio, or a combination thereof. In some embodiments, the image acquirer 292 may receive signals from the charged particle detection device 244 and construct an image. In this manner, the image acquirer 292 may acquire an SCPM image of the wafer 230. The image acquirer 292 may also perform various post-processing functions, such as generating contours and overlaying indicators on the acquired image. The image acquirer 292 may be configured to adjust the brightness and contrast of the acquired image, etc. In some embodiments, storage 294 may be a storage medium such as a hard disk, a flash drive, cloud storage, random access memory (RAM), or other types of computer-readable memory. Storage 294 may be coupled to image acquirer 292 and may be used to store scanned raw image data as original images and post-processed images. Image acquirer 292 and storage 294 may be connected to controller 296. In some embodiments, image acquirer 292, storage 294, and controller 296 may be integrated into one control unit.

[0034]

[0042] In some embodiments, the image acquirer 292 may acquire one or more SCPM images of the wafer based on the imaging signal received from the charged particle detection device 244. The imaging signal may correspond to a scanning motion for performing charged particle imaging. The acquired image may be a single image including multiple imaging areas. The single image may be stored in the storage 294. The single image may be an original image that may be divided into multiple regions. Each of these regions may include an imaging area that includes a feature of the wafer 230. The acquired image may include multiple images of a single imaging area of ​​the wafer 230 sampled multiple times over a temporal sequence. The multiple images may be stored in the storage 294. In some embodiments, the image processing system 290 may be configured to perform image processing steps using multiple images of the same location on the wafer 230.

[0035]

[0043] In some embodiments, image processing system 290 may include measurement circuitry (e.g., an analog-to-digital converter) to obtain a distribution of detected secondary charged particles (e.g., secondary electrons). The charged particle distribution data collected during the detection time window may be used in combination with corresponding scan path data of beamlets 214, 216, and 218 incident on the wafer surface to reconstruct an image of the wafer structure under inspection. The reconstructed image may be used to reveal various features of the internal or external structure of wafer 230, and thereby reveal any defects that may be present in the wafer.

[0036]

[0044] In some embodiments, the charged particles may be electrons. When electrons of the primary charged particle beam 210 are projected onto the surface of the wafer 230 (e.g., probe spots 270, 272, and 274), the electrons of the primary charged particle beam 210 may interact with particles of the wafer 230 and penetrate the surface of the wafer 230 to a certain depth. Some electrons of the primary charged particle beam 210 may interact with the material of the wafer 230 elastically (e.g., in the form of elastic scattering or collisions) and be reflected or bounced off the surface of the wafer 230. The elastic interaction preserves the total kinetic energy of the interacting objects (e.g., electrons of the primary charged particle beam 210), and the kinetic energy of the interacting objects is not converted into other forms of energy (e.g., heat, electromagnetic energy, etc.). The reflected electrons resulting from such elastic interactions may be referred to as backscattered electrons (BSE). Some electrons of the primary charged particle beam 210 may interact with the material of the wafer 230 inelastically (e.g., in the form of inelastic scattering or collisions). Inelastic interactions do not conserve the total kinetic energy of interacting objects, but rather convert some or all of the kinetic energy of the interacting objects into other forms of energy. For example, inelastic interactions can cause the kinetic energy of some electrons in the primary charged particle beam 210 to cause electronic excitation and transitions in atoms of a material. Such inelastic interactions can also produce electrons that leave the surface of the wafer 230, which may be called secondary electrons (SEs). The yield or emission rate of BSEs and SEs depends, among other things, on the material under inspection and the irradiation energy of the electrons of the primary charged particle beam 210 that are irradiated onto the surface of the material. The energy of the electrons of the primary charged particle beam 210 can be imparted in part by their acceleration voltage (e.g., the acceleration voltage between the anode and cathode of the charged particle source 202 in FIG. 2 ). The amount of BSEs and SEs can be greater or less than (and even the same as) the injected electrons of the primary charged particle beam 210.

[0037]

[0045] Images generated by SCPM can be used for defect inspection. For example, a generated image capturing a test device area of ​​a wafer can be compared to a reference image capturing the same test device area. The reference image may be predetermined (e.g., by simulation) and may not contain known defects. If the difference between the generated image and the reference image exceeds an acceptable level, a potential defect can be identified. As another example, SCPM can scan multiple areas of a wafer, each containing identically designed test device areas, and generate multiple images capturing those test device areas as manufactured. The multiple images can be compared to each other. If the difference between the multiple images exceeds an acceptable level, a potential defect can be identified.

[0038]

[0046] Reference is now made to FIG. 3 , which is a block diagram of an exemplary inspection image simulation system consistent with embodiments of the present disclosure. The inspection image simulation system 300 (also referred to as “apparatus 300”) may include one or more computers, servers, mainframe hosts, terminals, personal computers, any type of mobile computing device, etc., or combinations thereof. It is understood that in various embodiments, the inspection image simulation system 300 may be part of or separate from a charged particle beam inspection system (e.g., EBI system 100 of FIG. 1 ). It is also understood that the inspection image simulation system 300 may be separate from the charged particle beam inspection system and include one or more components or modules communicatively coupled to the charged particle beam inspection system. In some embodiments, the inspection image simulation system 300 may include one or more components (e.g., software modules) implementable by the controller 109 or system 290, as discussed herein. As shown in FIG. 3 , the inspection image simulation system 300 may include a design data acquirer 310, a design data processor 320, a pattern information estimator 330, and an image renderer 340. According to some embodiments, the inspection image simulation system 300 may further include a parameter applicator 360 .

[0039]

[0047] According to some embodiments of the present disclosure, the design data acquirer 310 can acquire design data having a specific pattern. The design data can be a golden image, a layout file for a wafer design, or a Graphics Database System (GDS) format, a Graphics Database System II (GDS II) format, an Open Artwork Systems Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), or the like. The wafer design can include a pattern or structure for inclusion on the wafer. The pattern or structure can be a mask pattern used to transfer features from a photolithography mask or reticle to the wafer. In some embodiments, a layout in a GDS or OASIS format, among others, can include feature information stored in a binary file format representing planar geometry, text, and other information related to the wafer design. FIG. 4A illustrates design data 410. As shown in FIG. 4A, the design data 410 includes a pattern 411. In some embodiments, a user can generate the design data 410 to include a pattern having a specified shape, size, density, etc. In some embodiments, a user can select a particular portion of the design data 410 that has a pattern with a specified shape, size, density, etc.

[0040]

[0048] Referring again to FIG. 3 , the design data processor 320 can perform image processing operations on the design data 410 acquired by the design data acquirer 310. In some embodiments, the design data processor 320 can convert the design data 410 into a binary image. In some embodiments, the design data processor 320 can further perform corner rounding on the binary image. FIG. 4A shows a binary image 420 obtained after performing corner rounding on the binary image converted from the design data 410. In some embodiments, a corner rounding operation can be performed to emulate a pattern formed on a wafer. In FIG. 4A , the binary image 420 includes a pattern 421 that corresponds to the pattern 411 in the design data 410. As shown in FIG. 4A , the corners of the pattern 421 on the binary image 420 are rounded compared to the corners of the pattern 411 on the design data 410. While corner rounding is illustrated as an image processing operation, it will be understood that any image processing operation can be performed on the design data 410 to mimic a pattern formed on a wafer. For example, pattern merging or pattern trimming can be performed on the binary image 420 .

[0041]

[0049] According to some embodiments of the present disclosure, one or more parameters can be applied by the parameter applicator 360 to incorporate characteristics of a real SCPM image. According to some embodiments of the present disclosure, the inspection image simulation system 300 can consider parameters to emulate an SCPM image, including specific metrology-related characteristics such as roughness, charging effects, distortion, gray level profile, and voltage contrast. In some embodiments, the parameter applicator 360 can apply a charging effect to the binary image 420. The charging effect can cause image distortion when the wafer structure includes insulating materials. An image distortion model 360-1 representing the charging effect on the binary image 420 can be applied to the binary image 420 by the parameter applicator 360. In some embodiments, the charging effect can be applied by adjusting distortion parameters of the image distortion model 360-1 corresponding to the charging effect. In some embodiments, the image distortion model 360-1 representing the distortion map can be adjusted by changing parameters related to the degree of rotation, scale, shift, etc. At this stage, the charging effect can be applied for each field of view (FOV) of the processed binary image 425. In some embodiments, the distortion model 360-1 may be established based on observing actual SCPM images, structures on the wafer, materials comprising the structures, inspection conditions, etc. In some embodiments, the image distortion model 360-1 may represent a distortion map caused by any reason other than charging effects. FIG. 4A shows a processed binary image 425 obtained after applying the image distortion model 360-1 to the binary image 420. In FIG. 4A, the processed binary image 425 includes a pattern 426 corresponding to the pattern 421 in the binary image 420. As shown in FIG. 4A, due to the introduction of distortion representing charging effects, the shape or position of the pattern 426 on the processed binary image 425 may differ from that of the pattern 421.The subsequent processes performed by the inspection image simulation system 300 are illustrated using the processed binary image 425, although it will be understood that the subsequent processes may be performed on the binary image 420 when the distortion model 360-1 is not applied to the binary image 420.

[0042]

[0050] In some embodiments, one or more image processes, including application of distortion model 360-1, can be applied to binary image 425 to incorporate one or more parameters into the simulated inspection image. In this example, processed binary image 425 in FIG. 4A shows the resulting processed binary image obtained by applying roughness to a contour and applying image distortion model 360-1 to binary image 420. In some embodiments, roughness to a contour can be modeled to be applied by parameter applicator 360. In some embodiments, roughness can be modeled using a power spectral density (PSD) function. In some embodiments, roughness can be applied by adjusting parameters of the roughness model according to a desired level of roughness. For example, the roughness model can be adjusted by changing parameters of the PSD function, such as the standard deviation, longitudinal correlation coefficient, or slope coefficient. It should be noted that any model representing roughness relative to a contour can be utilized in this disclosure. In this disclosure, processed binary image 425 can refer to the resulting image after performing one or more image processes on binary image 420.

[0043]

[0051] Referring again to FIG. 3 , the pattern information estimator 330 can estimate pattern information from the processed binary image 425. In some embodiments, the pattern information estimator 330 can estimate distance information of the pattern 426. In some embodiments, the distance information of the pattern 426 can be estimated by performing a distance transform operation on the processed binary image 425. The distance transform converts the processed binary image 425, which is composed of feature pixels and non-feature pixels, into an image in which all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. In some embodiments, pixels that make up the outline of the pattern 426 can be recognized as feature pixels. FIG. 4B shows a distance image 430-1 estimated from the processed binary image 425. In FIG. 4B, the distance image 430-1 includes a section 431 that corresponds to the section 427 containing the pattern 426 in the processed binary image 425 of FIG. 4A. In FIG. 4B, the distance image 430-1 becomes brighter as the distance from the nearest feature pixel (i.e., the outline of the pattern 426) decreases. Distance image 430-1 darkens as the distance from the nearest feature pixel (i.e., the contour of pattern 426) increases. Thus, as shown in FIG. 4B , distance image 430-1 brightens along the circular contour of pattern 426 and darkens as the distance from the contour increases. According to some embodiments, distance image 430-1 can be used to determine the distance of a particular pixel in section 431 from the contour of pattern 426. For example, the location of all pixels in section 431 can be defined by their distance from the contour of pattern 426. In some embodiments, distance image 430-1 can indicate whether a particular pixel in section 431 is located inside or outside the contour of pattern 426. For example, distance image 430-1 can use a different color for pixels located inside the contour of pattern 426 than the color used for pixels located outside the contour. In some embodiments, the brightness indicates the magnitude of the distance of a particular pixel, while the color can indicate whether the pixel is located inside or outside the contour of the pattern.In some embodiments, a negative sign (-) may be used if a particular pixel is located inside the contours of pattern 426, and a positive sign (+) may be used if a particular pixel is located outside the contours of pattern 426. While obtaining distance information has been described with respect to one pattern (e.g., 426), it will be appreciated that distance information may be obtained for any or all patterns on processed binary image 425 in a similar manner.

[0044]

[0052] In some embodiments, the pattern information estimator 330 can estimate frequency information of the pattern 426 from the distance image 430-1 of FIG. 4B . In some embodiments, the frequency information of the pattern 426 can be estimated by performing a gradient operation on the distance image 430-1. In some embodiments, performing a gradient operation on the distance image 430-1 can obtain the direction of the greatest change on the distance image 430-1. FIG. 4B shows a gradient image 430-2 obtained by performing a gradient operation on the distance image 430-1. As shown in FIG. 4B , the gradient image 430-2 includes a section 433 corresponding to the section 431 of the distance image 430-1. As shown in FIG. 4B , the gradient image 430-2 indicates the direction of the greatest change on the distance image 430-1. Because the distance image 430-1 has pixel values ​​representing the distance from the contour of the pattern 426, the direction of the greatest change on the distance image 430-1 can be perpendicular to the contour of the pattern 426. As shown by direction lines 432 and 434 in gradient image 430-2, the direction of maximum change in distance image 430-1 may be radial in this example. While gradient image 430-2 shows two direction lines 432 and 434, it will be understood that gradient image 430-2 may have any number of direction lines indicating the direction of maximum change in distance image 430-1. In some embodiments, the center of rotation for direction lines 432 and 434 may be determined based on gradient image 430-2. In this example, the center of rotation for direction lines 432 and 434 is the center of section 433. In some embodiments, a reference line extending from the center of rotation may be set based on gradient image 430-2 to determine frequency information for each pixel in section 433. In this example, direction line 434 may be used as the reference line defining 0°. According to some embodiments, frequency information for a particular pixel in section 433 may be determined by the pixel's frequency from a reference line, e.g., reference line 434. For example, the frequency of a particular pixel may be determined by the angle between a line from the center to the corresponding pixel and a reference line.4B shows the direction lines ranging from 0° to 360° (i.e., a power range of 360°), it will be understood that the power range may vary depending on the pattern shape, gradient image 430-2, etc. For example, a particular pattern may have a power range of less than 360°.

[0045]

[0053] According to some embodiments of the present disclosure, the position of each pixel within section 433 may be determined according to distance information and degree information for section 433. For example, the position of a pixel may be specified as a distance from the contour of pattern 426 and a number of degrees from a reference line. While some embodiments of the present disclosure are illustrated using a circular pattern (e.g., pattern 426), it will be understood that the present disclosure may be applied to any shaped pattern having a closed-loop pattern. For example, pixels within a section having any closed-loop pattern may be specified by defining the position of the pixel within the section using the distance from the contour of the pattern and the number of degrees from a reference line. In the present disclosure, a closed-loop pattern may include any polygonal pattern, such as a rectangular pattern, a star pattern, etc. In some embodiments, a closed-loop pattern may also include a line pattern, since a line pattern also has a width as well as a length.

[0046]

[0054] Referring again to FIG. 3 , the image renderer 340 may render a gray level image corresponding to the processed binary image 425. According to some embodiments of the present disclosure, the image renderer 340 may render an image using gray level profile data corresponding to the processed binary image 425. FIG. 4C illustrates a gray level image 440 rendered using gray level profile data 340-1. The gray level profile data 340-1 illustrated in FIG. 4C is an example gray level profile along a line 442 in a section 441. In FIG. 4C , the line 442 is 45° away from a reference line 443, and the gray level profile data 340-1 represents the gray levels of pixels located along the line 442. In the gray level profile data 340-1 of Figure 4C, the x-axis represents distance from the contour of the pattern 426, with a distance of 0 representing the contour of the pattern 426, a distance with a negative sign (-d) representing distance d from the contour of the pattern inside the pattern 426, and a distance with a positive sign (+d) representing distance d from the contour of the pattern outside the pattern 426. While Figure 4C shows gray profile data 340-1 along a single 45° line 442, it will be understood that gray profile data along multiple lines of various degrees may be used to generate the gray level image of section 441. It will also be understood that other sections of the gray level image 440 may be rendered in a similar manner to generate section 441.

[0047]

[0055] According to some embodiments of the present disclosure, the gray level profile data 340-1 may be created from a real SCPM image, a simulated image from a physics-based simulator, or user-defined gray level profile data. How gray level profile data is created is described later in this disclosure with reference to FIG. 5 . In some embodiments, the gray level profile data 340-1 may be modified from gray level profile data extracted from a real SCPM image or a simulated image from a physics-based simulator, or user-defined gray level profile data. When modifying existing gray level profile data, a user can change the gray level profile to reflect characteristics the user wants to observe from an inspection image. In some embodiments, existing gray level profile data created from an SCPM image having a different pattern, size, or density than that of the design data 410 can be used to simulate an inspection image corresponding to the design data 410. In this case, when rendering an image corresponding to the design data 410, the existing gray level profile data may be modified according to differences between the design data 410 and the SCPM image from which the existing gray level profile data was extracted. According to some embodiments of the present disclosure, the gray level profile data 340-1 may be obtained by modifying existing gray level profile data of a non-simulated or simulated image having a pattern type, size, or density similar to that of the design data 410. Thus, according to some embodiments of the present disclosure, inspection images having various patterns, sizes, densities, etc. may be simulated.

[0048]

[0056] According to some embodiments of the present disclosure, a user can determine which gray-level profile data to apply when rendering a gray-level image. FIG. 4D illustrates how the gray-level profile data affects the rendered gray-level image. FIG. 4D illustrates design data 460 in a binary image format that corresponds to design data 410 of FIG. 4A but has a different pattern than design data 410. In FIG. 4D, three gray-level images 440-2, 440-3, and 440-4 are shown that are rendered by applying three different gray-level profile data 340-2, 340-3, and 340-4 to design data 460, respectively. For example, gray-level image 440-2 is obtained by applying gray-level profile data 340-2 to design data 460, and so on. Note that, similar to the gray level profile data 340-1 in FIG. 4C , the three gray level profile data 340-2, 340-3, and 340-4 in FIG. 4D also show gray level profile data along only one line of a specific frequency for one pattern in the design data 460. As shown in FIG. 4D , the resulting three gray level images 440-2, 440-3, and 440-4 are different from each other. According to some embodiments of the present disclosure, a user can obtain a desired gray level image by adjusting the gray level profile data applied to the design data. Note that, although not shown, the rendered gray level images 440-2, 440-3, and 440-4 are obtained by applying the gray level profile data 340-2, 340-3, and 340-4 to the design data 460 after one or more processes have been performed on the design data 460.

[0049]

[0057] Referring again to FIG. 3 , in some embodiments, parameter applicator 360 can apply parameters that a user wants to consider in the simulated inspection image. In some embodiments, a charging effect can be applied to each section 441 on gray level image 440. In some embodiments, a model representing the charging effect can be applied to gray level image 440. In some embodiments, the charging effect of insulating or low-conductivity materials irradiated with an electron beam can affect the resulting SCPM image. The charging effect on the SCPM image can result in a specific voltage contrast pattern on the SCPM image. In some embodiments, the charging effect can result in a darker or brighter voltage contrast on the SCPM image. In some embodiments, a model representing the charging effect can be generated depending on the material forming the structure on the wafer, the pattern shape, the intensity of the irradiating beam, the scanning direction, etc. In some embodiments, parameter applicator 360 can apply a model representing the charging effect to each section 441 on gray level image 440. In some embodiments, the model representing the charging effect can be adjusted by adjusting parameters related to the charging direction, tail length, contrast value, gray level value, pattern contour, etc. 4C shows the resulting gray level image 450 after the charging effect has been applied. As shown in FIG. 4C, the resulting gray level image 450 differs from the gray level image 440 according to the charging effect applied to the gray level image 440. For example, the resulting gray level image 450 differs from the gray level image 440 in various aspects, such as contrast, pattern contours, gray levels, etc.

[0050]

[0058] In some embodiments, the resulting gray level image 450 may be output as output data 350 of the system 300. In some embodiments, one or more parameters may be applied to the resulting gray level image 450, and the output data therefrom may be output as output data 350 of the system 300. In some embodiments, the output data 350 may be packed in a specific image format that includes identifying information such as pattern shape, size, density, etc. In some embodiments, the output data 350 may be in any other format that can be used in subsequent processing, for example, by a metrology tool.

[0051]

[0059] FIG. 5 is a block diagram of an exemplary gray level profile extraction system 500 consistent with embodiments of the present disclosure. The gray level profile extraction system 500 (also referred to as “apparatus 500”) may include one or more computers, servers, mainframe hosts, terminals, personal computers, any type of mobile computing device, etc., or combinations thereof. It is understood that in various embodiments, the gray level profile extraction system 500 may be part of or separate from a charged particle beam inspection system (e.g., EBI system 100 of FIG. 1 ). It is also understood that the gray level profile extraction system 500 may be separate from the charged particle beam inspection system and include one or more components or modules communicatively coupled to the charged particle beam inspection system. In some embodiments, the gray level profile extraction system 500 may include one or more components (e.g., software modules) implementable by the controller 109 or system 290, as discussed herein. It is understood that in various embodiments, the gray level profile extraction system 500 may be part of or separate from the inspection image simulation system 300 of FIG. 3 . As shown in FIG. 5, the gray level profile extraction system 500 may include an image acquirer 510 , a contour extractor 520 , a pattern information estimator 530 , and a gray level profile generator 540 .

[0052]

[0060] According to some embodiments of the present disclosure, the image acquirer 510 can acquire an inspection image as an input image. In some embodiments, the inspection image is an SCPM image of a sample or wafer. In some embodiments, the inspection image can be an inspection image generated by, for example, the EBI system 100 of FIG. 1 or the electron beam tool 104 of FIG. 2. In some embodiments, the image acquirer 510 can acquire the inspection image from a storage device or system that stores the inspection image. FIG. 6A illustrates an example inspection image 610 that includes a pattern 611. As shown in FIG. 6A, the inspection image 610 can include a pattern 611 having a particular shape, size, and density.

[0053]

[0061] Referring again to FIG. 5 , contour extractor 520 can extract contour information of a pattern on inspection image 610. In some embodiments, the contour information of pattern 611 may include information about the boundary of pattern 611. In some embodiments, the boundary of the pattern may be a line for determining the outer shape of the pattern, a line for determining the inner shape of the pattern, a boundary between different textures within the pattern, or any other type of line that can be used to recognize the pattern. FIG. 6A shows an example contour extracted image 620 of inspection image 610. As shown in FIG. 6A , a contour 621 of pattern 611 is shown in contour extracted image 620.

[0054]

[0062] Referring again to FIG. 5 , pattern information estimator 530 can estimate pattern information from contour-extracted image 620. In some embodiments, pattern information estimator 630 can estimate distance information of pattern 611. In some embodiments, distance information of pattern 611 can be estimated by performing a distance transform operation on contour-extracted image 620. The distance transform converts contour-extracted image 620, which is composed of feature pixels and non-feature pixels, into an image in which all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. In some embodiments, pixels that make up contour 621 of pattern 611 can be recognized as feature pixels. FIG. 6A shows distance image 630-1 estimated from contour-extracted image 620. In FIG. 6A , distance image 630-1 includes section 631 that corresponds to section 622 that includes contour 621 in contour-extracted image 620. In FIG. 6A , distance image 630-1 becomes brighter as the distance from contour 621 decreases. Distance image 630-1 becomes darker as the distance from contour 621 increases. 6A , distance image 630-1 is brighter along circular contour 621 and darker as the distance from contour 621 increases. According to some embodiments, distance image 630-1 can be used to determine the distance of a particular pixel within section 631 from contour 621 of pattern 611. For example, the location of all pixels within section 631 can be defined by their distance from contour 621 of pattern 611. In some embodiments, distance image 630-1 can indicate whether a particular pixel within section 631 is located inside or outside contour 621. For example, distance image 630-1 can use a different color for pixels located inside contour 621 than the color used for pixels located outside contour 621. In some embodiments, the brightness indicates the magnitude of the distance of a particular pixel, while the color can indicate whether the pixel is located inside or outside the contour of the pattern.In some embodiments, a negative sign (-) may be used if a particular pixel is located inside contour 621, and a positive sign (+) may be used if a particular pixel is located outside contour 621. While obtaining distance information has been described with respect to one pattern (e.g., 611), it will be appreciated that distance information may be obtained for any or all patterns on contour extracted image 620 in a similar manner.

[0055]

[0063] In some embodiments, the pattern information estimator 530 can estimate frequency information of the pattern 611 from the distance image 630-1 of FIG. 6A . In some embodiments, the frequency information of the pattern 611 can be estimated by performing a gradient operation on the distance image 630-1. In some embodiments, performing a gradient operation on the distance image 630-1 can obtain the direction of the greatest change on the distance image 630-1. FIG. 6A shows a gradient image 630-2 obtained by performing a gradient operation on the distance image 630-1. As shown in FIG. 6A , the gradient image 630-2 includes a section 633 corresponding to the section 631 of the distance image 630-1. As shown in FIG. 6A , the gradient image 630-2 indicates the direction of the greatest change on the distance image 630-1. Because the distance image 630-1 has pixel values ​​representing the distance from the contour 621, the direction of the greatest change on the distance image 630-1 can be perpendicular to the contour 621 of the pattern 611. As shown by direction line 634 in gradient image 630-2, the direction of maximum change in distance image 630-1 may be radial in this example. While gradient image 630-2 shows one direction line 634, it will be understood that direction image 630-2 may have any number of direction lines indicating the direction of maximum change in distance image 630-1. In some embodiments, the center of rotation for direction line 634 may be determined based on gradient image 630-2. In this example, the center of rotation for direction line 634 is the center of section 633. In some embodiments, a reference line extending from the center of rotation may be set based on gradient image 630-2 to determine frequency information for each pixel in section 633. In this example, direction line 634 may be used as the reference line defining 0°. According to some embodiments, frequency information for a particular pixel in section 633 may be determined by the pixel's frequency from a reference line, such as reference line 634. For example, the frequency of a particular pixel may be determined by the angle between the reference line and a line from the center to the corresponding pixel. Although the gradient image 630-2 in FIG. 6A shows that the direction lines range from 0° to 360° (i.e., a power range of 360°), it will be understood that the power range may vary depending on the pattern shape, the gradient image 630-2, etc.For example, a particular pattern may have a power range of less than 360°.

[0056]

[0064] According to some embodiments of the present disclosure, the position of each pixel within section 633 may be determined according to distance information and frequency information for section 633. For example, the position of a pixel may be specified as a distance from the pattern's contour 621 and a frequency from a reference line. While some embodiments of the present disclosure are illustrated using a circular pattern (e.g., pattern 611), it will be understood that the present disclosure may be applied to any shaped pattern having a closed-loop pattern. For example, pixels within a section having any closed-loop pattern may be specified by defining the pixel's position within the section using the distance from the pattern's contour and a frequency from a reference line. In the present disclosure, a closed-loop pattern may include any polygonal pattern, such as a rectangular pattern, a star pattern, etc. In some embodiments, a closed-loop pattern may also include a line pattern, since a line pattern also has a width as well as a length.

[0057]

[0065] Referring again to FIG. 5 , the gray level profile generator 540 can generate gray level profile data corresponding to the inspection image 610. According to some embodiments of the present disclosure, the gray level profile generator 540 can extract the gray level profile data of the inspection image 610 according to the pattern information estimated by the pattern information estimator 530. In some embodiments of the present disclosure, the gray level profile generator 540 can extract the gray level profile data according to the distance information and frequency information of each pattern obtained by the pattern information estimator 530. FIG. 6B shows a gray level distribution 640 corresponding to a section 612 including a pattern 611 in the inspection image 610. As shown in FIG. 6B , the gray level profile data of the section 612 can be extracted along a direction line 643 from a rotation center 641 at a specific degree θ from a reference line 642 within the frequency range (e.g., 360°) estimated by the pattern information estimator 530. According to some embodiments of the present disclosure, the gray level profile data of the section 612 can be extracted along multiple direction lines 643 at various degrees θ from the reference line 642. For example, the gray level profile data of section 612 may be extracted along multiple directional lines 643 rotated by equal angles.

[0058]

[0066] FIG. 6C shows gray level profile data 645 extracted from gray level distribution 640 corresponding to section 612 containing pattern 611 in inspection image 610. In FIG. 6C, the x-axis represents distance from contour 621 of pattern 611, with distance 0 representing contour 621 of pattern 611, distances with negative signs (-) representing distances inside the pattern from contour 621 of pattern 611, and distances with positive signs (+) representing distances outside the pattern from contour 621 of pattern 611. In FIG. 6C, the y-axis represents gray level values. In FIG. 6C, gray level values ​​are sampled along direction line 643 every 10° of rotation angle. For example, the gray level value of the direction line 643 when the degree θ is equal to 0° is shown as a gray scale mark next to the number "0", the gray level value of the direction line 643 when the degree θ is equal to 10° is shown as a gray scale mark next to the number "1", and similarly, the gray level value of the direction line 643 when the degree θ is equal to 350° is shown as a gray scale mark next to the number "35".

[0059]

[0067] As shown in FIG. 6C , the gray level values ​​of each directional line 643 may be modeled as a gray level profile along the corresponding directional line 643. According to some embodiments of the present disclosure, the gray level profile of each directional line 643 may be modeled by the mean and standard deviation of the gray level values ​​of the pixels located along the directional line 643. In this example, each section 612 may have 36 gray level profiles along the 36 directional lines 643. According to some embodiments of the present disclosure, the gray level profile of the section 612 may be modeled by the mean and standard deviation of the gray level values ​​of the pixels located along the 36 directional lines 643. In this example, the gray level profiles may be generated as two-dimensional data. While the present disclosure illustrates extracting gray level profile data of the section 612 of the inspection image 610 along the 36 directional lines 643, it will be understood that the gray level profile data of the inspection image may be extracted along any number of lines of any shape depending on the embodiment, pattern shape, target accuracy, etc.

[0060]

[0068] According to some embodiments of the present disclosure, a gray level profile may be modeled for each pixel on the pattern 611. In some embodiments, the gray level profile of the same pattern may be assumed to follow a Gaussian distribution. In some embodiments, gray level values ​​of pixels on multiple identical patterns may be extracted from the corresponding gray level distributions. For example, the inspection image 610 includes multiple repeating patterns 611, e.g., N patterns 611, and the gray level values ​​of pixels on the N patterns 611 may be extracted. In some embodiments, the gray level values ​​of the N pixels at corresponding positions on the N patterns 611 are assumed to follow a Gaussian distribution. As described with reference to FIG. 6A , the position of each pixel on each pattern 611 may be specified by the distance from the pattern's contour and the number of degrees from the reference line. Therefore, N gray level values ​​may be extracted from the N patterns 611 for each relative pixel position on the pattern 611. In some embodiments, the gray level profile for each relative pixel position on the pattern 611 may be modeled by fitting a Gaussian distribution model to the N extracted gray level values. For example, a Gaussian distribution model that can be obtained by fitting to the extracted gray level values ​​can be expressed by equation (1).

number

[0061]

[0069] In equation (1), x represents the pixel location on pattern 611, μ represents the mean of the Gaussian distribution model, and σ represents the standard deviation of the Gaussian distribution model. The location x may be represented by a distance from the pattern's contour and a frequency from a reference line. The mean μ and standard deviation σ may be obtained by fitting a Gaussian distribution to the N extracted gray level values ​​at location x. Similarly, gray level profiles may be modeled for the remaining pixel locations on pattern 611. According to some embodiments of the present disclosure, each pixel location on pattern 611 may have a corresponding gray level profile that follows a Gaussian distribution. In some embodiments, each pixel location on pattern 611 may be modeled by a Gaussian distribution with an associated mean μ or standard deviation σ. In some embodiments, Gaussian distributions representing gray level profiles for different pixel locations may have different means μ or standard deviations σ. While obtaining gray level profiles for pixels on pattern 611 has been described, it will be appreciated that in some embodiments, gray level profiles for pixels on an area including pattern 611 (e.g., section 612) and the surrounding area may be obtained. While modeling the gray level profile of pattern 611 based on multiple patterns on one image, it will be appreciated that the gray level profile of a pattern may be modeled based on multiple patterns from multiple images.

[0062]

[0070] According to some embodiments of the present disclosure, similar or identical patterns are assumed to have similar or identical gray level profiles. According to some embodiments of the present disclosure, the gray level profile created for one pattern (e.g., pattern 611) may be utilized to simulate an inspection image corresponding to design data (e.g., design data 410) having a similar or identical pattern in pattern shape, size, or density. When applying the gray level profile data to simulate an inspection image having a similar or identical pattern, the gray level values ​​for each pixel on pattern 611 may be randomly selected from a corresponding Gaussian distribution model based on probability, system requirements, etc. For example, when simulating an inspection image including 100 pixels, 100 gray level values ​​may be selected from 100 corresponding Gaussian distribution models for the pattern (e.g., pattern 611).

[0063]

[0071] According to some embodiments of the present disclosure, gray level profile data can also be obtained based on simulated images from physics-based simulators, such as Hyperlith and eScatter. In some embodiments, the gray level profile data can be user-defined gray level profile data using, for example, the Fraser model. In some embodiments, existing gray level profile data created from an SCPM image having a different pattern, size, or density than that of the design data 410 can be used to simulate an inspection image corresponding to the design data 410. In this case, when rendering an image corresponding to the design data 410, the existing gray level profile data can be modified according to differences between the design data 410 and the SCPM image from which the existing gray level profile data was extracted. Thus, according to some embodiments of the present disclosure, inspection images having various patterns, sizes, densities, etc. can be simulated.

[0064]

[0072] FIG. 7A illustrates an exemplary performance evaluation of an inspection image simulation system consistent with embodiments of the present disclosure. In FIG. 7A, a first image is a real SCPM image 710, a second image is a simulated image 720, and a third image is a residual image 730 obtained by subtracting the simulated image 720 from the real SCPM image 710. In this example, the simulated image 720 is generated by the inspection image simulation system 300 of FIG. 3 to incorporate parameters of the SCPM image 710 (e.g., distortion, voltage contrast pattern, gray level profile, etc.). As shown in FIG. 7A, the residual image 730 does not include pattern-related fingerprint features. It will be appreciated that pattern-related features, such as the pattern's contour, critical dimension, roughness, etc., can be accurately captured from the simulated image 720 generated by the inspection image simulation system 300 according to embodiments of the present disclosure.

[0065]

[0073] 7B-7C show exemplary simulated images of various patterns generated using an inspection image simulation system consistent with embodiments of the present disclosure. In FIG. 7B, the images on the left are design data 741, 743, and 745 in binary format, having various patterns and densities. The images on the right are simulated images 742, 744, and 746 generated by the inspection image simulation system 300 of FIG. 3 based on the corresponding design data 741, 743, and 745 on the left, respectively. Similarly, FIG. 7C shows design data 751 and its corresponding simulated image 752 generated by the inspection image simulation system 300 of FIG. 3. FIG. 7C also shows an enlarged image 753 of a portion of simulated image 752. As further shown in FIGS. 7B-7C, it should be noted that the inspection image simulation techniques of the present disclosure may be applied to various patterns and densities, including, but not limited to, line patterns (e.g., design pattern 745), complex circuit patterns (e.g., design pattern 752), etc.

[0066]

[0074] 8 is a process flow diagram illustrating an exemplary method for simulating an inspection image consistent with an embodiment of the present disclosure. For illustrative purposes, the method for simulating an inspection image is described with reference to the inspection image simulation system 300 of FIG.

[0067]

[0075] In step S810, design data can be acquired. Step S810 can be performed by, for example, design data acquirer 310, among others. In some embodiments, the design data can be a golden image, a layout file for a wafer design, or a Graphics Database System (GDS) format, a Graphics Database System II (GDS II) format, an Open Artwork Systems Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), or the like. The wafer design can include patterns or structures for inclusion on the wafer. The patterns or structures can be mask patterns used to transfer features from a photolithography mask or reticle to the wafer. In some embodiments, a GDS or OASIS format layout, among others, can include feature information stored in a binary file format representing planar geometry, text, and other information related to the wafer design. As shown in FIG. 4A , design data 410 includes pattern 411. In some embodiments, design data 410 can be generated to include patterns having specified shapes, sizes, densities, etc. In some embodiments, a specific portion of design data 410 can be selected to have patterns having specified shapes, sizes, densities, etc.

[0068]

[0076] In step S820, the design data can be processed. Step S820 can be performed by, for example, design data processor 310, among others. In some embodiments, design data 410 can be converted into a binary image. In some embodiments, corner rounding can be performed on the binary image. FIG. 4A shows binary image 420 obtained after corner rounding is performed on the binary image converted from design data 410. In some embodiments, a corner rounding operation can be performed to emulate a pattern formed on a wafer. In some embodiments, pattern merging or pattern trimming can further be performed on binary image 420.

[0069]

[0077] According to some embodiments of the present disclosure, one or more parameters may be applied to incorporate characteristics of a real SCPM image. According to some embodiments of the present disclosure, method 800 may consider parameters to emulate an SCPM image, including certain metrology-related characteristics such as roughness, charging effects, distortion, gray-level profile, and voltage contrast. Method 800 may optionally include step S860-1. In step S860-1, one or more parameters may be applied to binary image 420. Step S820 may be performed, for example, by parameter applicator 360, among others. In step S860-1, charging effects may be applied to binary image 420. Charging effects may cause image distortion when wafer structures include insulating materials. An image distortion model 360-1 representing charging effects on binary image 420 may be applied to binary image 420. In some embodiments, image distortion model 360-1 representing the distortion map may be adjusted by changing parameters related to rotation, scale, shift, etc. FIG. 4A shows a processed binary image 425 obtained after applying image distortion model 360-1 to binary image 420.

[0070]

[0078] In step S830, pattern information may be estimated from the processed binary image 425. Step S830 may be performed, among other things, by, for example, the pattern information estimator 330. In some embodiments, in step S830, distance information and frequency information of the pattern 426 may be estimated. Because estimating the distance information and frequency information has been described with respect to FIG. 4B , a detailed description for estimating the distance information and frequency information will be omitted here for simplicity and brevity. According to some embodiments of the present disclosure, the position of each pixel in section 433 may be determined according to the distance information and frequency information of section 433. For example, the pixel position may be specified as a distance from the contour of the pattern 426 and a frequency from a reference line.

[0071]

[0079] In step S840, an image can be rendered using the gray-level profile data. Step S840 can be performed, for example, by the image renderer 340, among others. In some embodiments, the gray-level profile data corresponding to the processed binary image 420 can be used to render a gray-level image corresponding to the processed binary image 425. Rendering an image has been described with respect to FIG. 4C , and therefore a detailed description of rendering an image corresponding to the processed binary image 420 will be omitted here for simplicity and brevity. According to some embodiments of the present disclosure, the gray-level profile data 340-1 can be created from a real SCPM image, a simulated image from a physics-based simulator, or user-defined gray-level profile data. How the gray-level profile data is created has been described in this disclosure with reference to FIG. 5 , and therefore a detailed description thereof will be omitted here for clarity and simplicity. In some embodiments, the gray-level profile data 340-1 can be modified from gray-level profile data extracted from a real SCPM image or a simulated image from a physics-based simulator, or user-defined gray-level profile data. When modifying existing gray-level profile data, a user can change the gray-level profile to reflect characteristics the user wants to observe from the inspection image. In some embodiments, existing gray-level profile data created from an SCPM image having a different pattern, size, or density than that of design data 410 can be used to simulate an inspection image corresponding to design data 410. In this case, when rendering an image corresponding to design data 410, the existing gray-level profile data can be modified according to differences between design data 410 and the SCPM image from which the existing gray-level profile data was extracted.

[0072]

[0080] Method 800 may optionally include step S860-2. In step S860-2, one or more parameters may be applied to gray level image 440. Step S860-2 may be performed, among other things, by parameter applicator 360, for example. In step S860-2, a charging effect may be applied to each section 441 on gray level image 440. According to some embodiments, a model representing the charging effect may be applied to gray level image 440. In some embodiments, the charging effect may result in a darker or brighter voltage contrast on the SCPIM image. In some embodiments, the model representing the charging effect may be generated depending on the material forming the structure on the wafer, the pattern shape, the intensity of the illumination beam, the scanning direction, etc. In some embodiments, parameter applicator 360 may apply the model representing the charging effect to each section 441 on gray level image 440. In some embodiments, the model representing the charging effect may be adjusted by adjusting parameters related to the charging direction, tail length, etc. FIG. 4C shows the resulting gray level image 450 after the charging effect has been applied.

[0073]

[0081] In some embodiments, the resulting gray level image 450 may be output as output data 350 of the system 300. In some embodiments, one or more parameters may be applied to the resulting gray level image 450, and the output data therefrom may be output as output data 350 of the system 300. In some embodiments, the output data 350 may be packed in a specific image format that includes identifying information such as pattern shape, size, density, etc. In some embodiments, the output data 350 may be in any other format that can be used in subsequent processing, for example, by a metrology tool.

[0074]

[0082] 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 ) to, among other things, perform image inspection, image acquisition, stage positioning, beam focusing, field adjustment, beam bending, condenser lens adjustment, charged particle source activation, beam deflection, and execute method 800. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage medium, compact disk read-only memory (CD-ROM), any other optical data storage medium, any physical medium with a pattern of holes, random access memory (RAM), programmable ROM (PROM) and erasable programmable ROM (EPROM), FLASH-EPROM or any other flash memory, non-volatile random access memory (NVRAM), cache, registers, any other memory chip or cartridge, and networked versions thereof.

[0075]

[0083] The embodiments may be further described using the following clauses: 1. A method for generating a simulated inspection image, comprising: acquiring design data including a first pattern; generating a first gray-level profile corresponding to the design data; Rendering an image using the generated first gray level profile; A method comprising: 2. The method of clause 1, wherein the first gray level profile is created from a non-simulated inspection image, a simulated image generated by a simulator based on a physical model, or a user-defined gray level profile. 3. Generating a first gray level profile includes: acquiring a non-simulation test image having a second pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a second gray level profile corresponding to the non-simulated test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 3. The method according to clause 1 or 2, comprising: 4. The method of clause 3, wherein the first pattern and the second pattern differ in size, shape, or density. 5. Generating a second gray level profile modeling a gray level profile of pixels on the second pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the second pattern; 5. The method according to clause 3 or 4, comprising: 6. The method of any one of clauses 3 to 5, wherein the pattern information includes distance information and power information of the second pattern. 7. The method of clause 6, wherein the distance information and the power information are used to determine the location of pixels on the second pattern. 8. Generating a first gray level profile includes: generating a second gray level profile corresponding to the second pattern; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 3. The method according to clause 1 or 2, comprising: 9. Incorporating user-defined parameters into images 9. The method of any one of clauses 1 to 8, further comprising: 10. Incorporating user-defined parameters performing corner rounding on design data including a first pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 9. The method of claim 9, comprising: 11. A method for generating a simulated inspection image, comprising: acquiring a non-simulation test image having a first pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a first gray-level profile corresponding to the non-simulation test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile; A method comprising: 12. Generating a first gray level profile includes: modeling a gray level profile of pixels on the first pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the first pattern; 12. The method according to clause 11, comprising: 13. The method of clause 11 or 12, wherein the pattern information includes distance information and power information of the first pattern. 14. The method of clause 13, wherein the distance information and the power information are used to determine the location of pixels on the first pattern. 15. Obtaining design data including a second pattern; Rendering an image using the generated first gray level profile; 14. The method of any one of clauses 11 to 13, further comprising: 16. The method of clause 15, wherein the first pattern and the second pattern differ in size, shape, or density. 17. Incorporating user-defined parameters into images 17. The method of any one of clauses 11 to 16, further comprising: 18. Incorporating user-defined parameters performing corner rounding on the design data including the second pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 18. The method according to clause 17, comprising: 19. An apparatus for generating a simulated inspection image, comprising: a memory for storing a set of instructions; at least one processor executing a set of instructions to acquiring design data including a first pattern; generating a first gray-level profile corresponding to the design data; Rendering an image using the generated first gray level profile; at least one processor configured to cause the apparatus to An apparatus comprising: 20. The apparatus of clause 19, wherein the first gray level profile is created from a non-simulated inspection image, a simulated image generated by a simulator based on a physical model, or a user-defined gray level profile. 21. When generating the first gray level profile, at least one processor executes a set of instructions to: acquiring a non-simulation test image having a second pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a second gray level profile corresponding to the non-simulated test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 21. The apparatus of clause 19 or 20, further configured to cause the apparatus to: 22. The device of clause 21, wherein the first pattern and the second pattern differ in size, shape, or density. 23. When generating the second gray level profile, at least one processor executes a set of instructions to: modeling a gray level profile of pixels on the second pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the second pattern; 23. The apparatus of clause 21 or 22, configured to cause the apparatus to 24. The device of any one of clauses 21 to 23, wherein the pattern information includes distance information and power information of the second pattern. 25. The apparatus of clause 24, wherein the distance information and the power information are used to determine the location of pixels on the second pattern. 26. When generating the first gray level profile, at least one processor executes a set of instructions to: generating a second gray level profile corresponding to the second pattern; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 21. The apparatus of clause 19 or 20, further configured to cause the apparatus to: At least one processor executes a set of instructions to Incorporating user-defined parameters into images 27. The apparatus of any one of clauses 19 to 26, further configured to cause the apparatus to: 28. When incorporating user-defined parameters, at least one processor executes a set of instructions to: performing corner rounding on design data including a first pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 28. The apparatus of clause 27, configured to cause the apparatus to: 29. An apparatus for generating a simulated inspection image, comprising: a memory for storing a set of instructions; at least one processor executing a set of instructions to acquiring a non-simulation test image having a first pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a first gray-level profile corresponding to the non-simulation test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile; at least one processor configured to cause the apparatus to An apparatus comprising: 30. When generating the first gray level profile, at least one processor executes a set of instructions to: modeling a gray level profile of pixels on the first pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the first pattern; 29. The apparatus of claim 29, configured to cause the apparatus to: 31. The apparatus of clause 29 or 30, wherein the pattern information includes distance information and power information of the first pattern. 32. The apparatus of clause 31, wherein the distance information and the power information are used to determine the location of pixels on the first pattern. 33. At least one processor executes a set of instructions to acquiring design data including a second pattern; Rendering an image using the generated first gray level profile; 33. The apparatus of any one of clauses 29 to 32, further configured to cause the apparatus to: 34. The apparatus of clause 33, wherein the first pattern and the second pattern differ in size, shape, or density. 35. At least one processor executes a set of instructions to Incorporating user-defined parameters into images 35. The apparatus of any one of clauses 29 to 34, further configured to cause the apparatus to perform: 36. When incorporating user-defined parameters, at least one processor executes a set of instructions to: performing corner rounding on the design data including the second pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 36. The apparatus of clause 35, configured to cause the apparatus to: 37. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising: acquiring design data including a first pattern; generating a first gray-level profile corresponding to the design data; Rendering an image using the generated first gray level profile; 1. A non-transitory computer-readable medium comprising: 38. The computer-readable medium of clause 37, wherein the first gray level profile is created from a non-simulated inspection image, a simulated image generated by a simulator based on a physical model, or a user-defined gray level profile. 39. In generating a first gray level profile, a set of instructions executable by at least one processor of a computing device comprises: acquiring a non-simulation test image having a second pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a second gray level profile corresponding to the non-simulated test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 39. The computer-readable medium of clause 37 or 38, causing a computing device to perform 40. The computer-readable medium of clause 39, wherein the first pattern and the second pattern differ in size, shape, or density. 41. In generating a second gray level profile, a set of instructions executable by at least one processor of a computing device comprises: modeling a gray level profile of pixels on the second pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the second pattern; 41. The computer-readable medium of clause 39 or 40, causing a computing device to perform 42. The computer-readable medium of any one of clauses 39 to 41, wherein the pattern information includes distance information and power information of the second pattern. 43. The computer-readable medium of clause 42, wherein the distance information and the frequency information are used to determine the location of pixels on the second pattern. 44. In generating a first gray-level profile, a set of instructions executable by at least one processor of a computing device comprises: generating a second gray level profile corresponding to the second pattern; generating a first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; 39. The computer-readable medium of clause 37 or 38, causing a computing device to perform 45. A set of instructions executable by at least one processor of a computing device comprises: Incorporating user-defined parameters into images 45. The computer-readable medium of any one of clauses 37 to 44, further causing a computing device to perform: 46. ​​A set of instructions executable by at least one processor of a computing device when incorporating user-defined parameters: performing corner rounding on design data including a first pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 46. ​​The computer-readable medium of claim 45, which causes a computing device to: 47. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising: acquiring a non-simulation test image having a first pattern; Extracting a contour of a pattern from the non-simulated inspection image; estimating pattern information of the contour of the extracted pattern; generating a first gray-level profile corresponding to the non-simulation test image based on the estimated pattern information; generating a first gray level profile by modifying the second gray level profile; 1. A non-transitory computer-readable medium comprising: 48. In generating a first gray level profile, a set of instructions executable by at least one processor of a computing device comprises: modeling a gray level profile of pixels on the first pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the first pattern; 48. The computer-readable medium of clause 47, which causes a computing device to: 49. The computer-readable medium of clause 47 or 48, wherein the pattern information includes distance information and power information of the first pattern. 50. The computer-readable medium of clause 49, wherein the distance information and the frequency information are used to determine the location of pixels on the first pattern. 51. A set of instructions executable by at least one processor of a computing device comprises: acquiring design data including a second pattern; Rendering an image using the generated first gray level profile; 51. The computer-readable medium of any one of clauses 47 to 50, further causing a computing device to perform: 52. The computer-readable medium of clause 51, wherein the first pattern and the second pattern differ in size, shape, or density. 53. A set of instructions executable by at least one processor of a computing device comprises: Incorporating user-defined parameters into images 53. The computer-readable medium of any one of clauses 47 to 52, further causing a computing device to perform: 54. A set of instructions executable by at least one processor of a computing device when incorporating user-defined parameters: performing corner rounding on the design data including the second pattern; Applying image distortion to the design data; or Applying a charging effect to the rendered image 54. The computer-readable medium of claim 53, which causes a computing device to:

[0076]

[0084] The block diagrams in the figures may illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware or software products according to various exemplary embodiments of the present disclosure. In this regard, each block in the schematic diagrams may represent a specific arithmetic or logical operation that may be implemented using hardware, such as electronic circuits. The blocks may also represent modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical function. It should be understood that in some alternative implementations, the functions shown in the blocks may occur in an order different from that shown in the figures. For example, depending on the functionality involved, two blocks shown in succession may be executed or implemented substantially simultaneously, or the two blocks may be executed in the reverse order. Some blocks may also be omitted. It should also be understood that each block and combination of blocks in the block diagrams may be implemented by a dedicated hardware-based system that performs the specified function or act, or a combination of dedicated hardware and computer instructions.

[0077]

[0085] It is to be understood that the embodiments of the present disclosure are not limited to the exact configurations described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope of the present invention. While the present disclosure has been described in connection with various embodiments, other embodiments of the present invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims

1. 1. An apparatus for generating a simulated inspection image, comprising: a memory for storing a set of instructions; at least one processor executing the set of instructions to acquiring design data including a first pattern; generating a first gray level profile corresponding to the design data; Rendering an image using the generated first gray-level profile; and at least one processor that causes the device to perform An apparatus comprising:

2. The apparatus of claim 1 , wherein the first gray-level profile is created from a non-simulated inspection image, a simulated image generated by a simulator based on a physical model, or a user-defined gray-level profile.

3. In generating the first gray level profile, the at least one processor executes the set of instructions to: acquiring a non-simulation inspection image having a second pattern; extracting a pattern contour from the non-simulation inspection image; estimating pattern information of the contour of the extracted pattern; generating a second gray-level profile corresponding to the non-simulation inspection image based on the estimated pattern information; and generating the first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; The apparatus of claim 1 , further comprising:

4. The apparatus of claim 3 , wherein the first pattern and the second pattern differ in size, shape, or density.

5. In generating the second gray-level profile, the at least one processor executes the set of instructions to: modeling a gray level profile of pixels on the second pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the second pattern; The apparatus of claim 3 , wherein the apparatus performs the following:

6. The apparatus of claim 3 , wherein the pattern information includes distance information and power information of the second pattern.

7. The apparatus of claim 6 , wherein the distance information and the power information are used to determine pixel locations on the second pattern.

8. In generating the first gray level profile, the at least one processor executes the set of instructions to: generating a second gray level profile corresponding to the second pattern; generating the first gray level profile by modifying the second gray level profile based on differences between the first pattern and the second pattern; The apparatus of claim 1 , further comprising:

9. The at least one processor executes the set of instructions to Incorporating user-defined parameters into said image. The apparatus of claim 1 , further comprising:

10. When incorporating the user-defined parameters, the at least one processor executes the set of instructions to: performing corner rounding on the design data including the first pattern; applying an image distortion to the design data; or applying a charging effect to the rendered image; The apparatus of claim 9 , wherein the apparatus performs the following:

11. 1. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising: acquiring design data including a first pattern; generating a first gray level profile corresponding to the design data; Rendering an image using the generated first gray-level profile; and 1. A non-transitory computer-readable medium comprising:

12. 12. The computer-readable medium of claim 11, wherein the first gray level profile is created from a non-simulated inspection image, a simulated image generated by a simulator based on a physical model, or a user-defined gray level profile.

13. The set of instructions executable by at least one processor of the computing device when generating the first gray-level profile comprises: acquiring a non-simulation inspection image having a second pattern; extracting a pattern contour from the non-simulation inspection image; estimating pattern information of the contour of the extracted pattern; generating a second gray-level profile corresponding to the non-simulation inspection image based on the estimated pattern information; and generating the first gray level profile by modifying the second gray level profile based on a difference between the first pattern and the second pattern; The computer-readable medium of claim 11 , which causes the computing device to:

14. The computer-readable medium of claim 13 , wherein the first pattern and the second pattern differ in size, shape, or density.

15. The set of instructions executable by at least one processor of the computing device when generating the second gray-level profile comprises: modeling a gray level profile of pixels on the second pattern using a Gaussian distribution model based on gray level values ​​of pixels at corresponding locations on a plurality of patterns having the same pattern as the second pattern; The computer-readable medium of claim 13 , which causes the computing device to:

Images