Systems and methods for image alignment for inspection and metrology

Abstract

Systems and methods for image alignment for inspection and metrology. Systems and methods may include generating a first boundary mask for a first image of a sample and a second boundary mask that is based on reference data; generating a first cross-correlation map based on the first boundary mask and the second boundary mask; determining a peak value within a range of values based on the first cross-correlation map; and aligning the first and second images based on the peak. Systems and methods may include adding alignment markings to mark designs of a first image and a second image; performing global alignment between the first image and the second image; binarizing the first image and the second image; determining offset between the binarized first image and the binarized second image; and correcting distortion between the first and second images based on the determined offset.

Description

SYSTEMS AND METHODS FOR IMAGE ALIGNMENT FOR INSPECTION AND METROLOGYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of US application 63 / 694,150 which was filed on September 12, 2024 and which is incorporated herein in its entirety by reference.FIELD

[0002] The description herein relates to the field of inspection and metrology in charged particle systems, and more particularly to methods for image alignment for inspection and metrology.BACKGROUND

[0003] In manufacturing processes of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to design and are free of defects. An inspection system utilizing an optical microscope typically has resolution down to a few hundred nanometers; and the resolution is limited by the wavelength of light. As the physical sizes of IC components continue to reduce down to sub- 100 or even sub- 10 nanometers, inspection systems capable of higher resolution than those utilizing optical microscopes are needed.

[0004] A charged particle (e.g., electron) beam microscope, such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM), capable of resolution down to less than a nanometer, serves as a practicable tool for inspecting IC components having a feature size that is sub- 100 nanometers. With a SEM, electrons of a single primary electron beam, or electrons of a plurality of primary electron beams, can be focused on locations of interest of a wafer under inspection. The primary electrons interact with the wafer and may be backscattered or may cause the wafer to emit secondary electrons. The intensity of the electron beams comprising the backscattered electrons and the secondary electrons may vary based on the properties of the internal and external structures of the wafer, and thereby may indicate whether the wafer has defects.SUMMARY

[0005] Embodiments of the present disclosure provide systems and methods for image alignment for inspection and metrology. Embodiments may include generating a first boundary mask for a first image of a sample and a second boundary mask that is based on reference data; generating a first cross-correlation map based on the first boundary mask and the second boundary mask; determining a peak value within a range of values based on the first cross-correlation map; and aligning the first and second images based on the peak.

[0006] Embodiments may include adding alignment markings to mark designs of a first image and a second image; performing global alignment between the first image and the second image; binarizingthe first image and the second image; determining offset between the binarized first image and the binarized second image; and correcting distortion between the first and second images based on the determined offset.

[0007] Embodiments may include determining a first cross-correlation based on a first boundary mask and a second boundary mask; determining a maximum value based on the determined first cross-correlation; and applying an alignment between a first image corresponding to the first boundary mask and reference data corresponding to the second boundary mask based on the maximum value.

[0008] Embodiments may include binarizing a first image and a second image, wherein mark designs of the first image and the second image comprise alignment markings; and applying an alignment between the first and second images based on an offset between the binarized first image and the binarized second image.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Fig. 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system, consistent with embodiments of the present disclosure.

[0010] Fig. 2A is a schematic diagram illustrating an exemplary multi-beam system that is part of the exemplary charged particle beam inspection system of Fig. 1, consistent with embodiments of the present disclosure.

[0011] Fig. 2B is a schematic diagram illustrating an exemplary single-beam system that is part of the exemplary charged particle beam inspection system of Fig. 1, consistent with embodiments of the present disclosure.

[0012] Fig. 3 is an exemplary graph showing a yield rate of secondary electrons relative to landing energy of primary electron beamlets, consistent with embodiments of the present disclosure.

[0013] Fig. 4 is a schematic diagram illustrating an exemplary a voltage contrast response of a wafer, consistent with embodiments of the present disclosure.

[0014] Fig. 5A shows a diagram of an image including a repeating pattern, GDS data, and a crosscorrelation map.

[0015] Fig. 5B shows a diagram of an image including a repeating pattern, GDS data, and a crosscorrelation map.

[0016] Fig. 6 shows a diagram of an image including a repeating pattern, GDS data, and a crosscorrelation map.

[0017] Fig. 7 shows a process of image alignment, consistent with embodiments of the present disclosure.

[0018] Fig. 8 shows an example diagram of experimental results with respect to adjusted parameters of the density function, consistent with embodiments of the present disclosure.

[0019] Fig. 9 shows a process of image alignment for voltage contrast images, consistent with embodiments of the present disclosure.

[0020] Fig. 10 shows an example diagram of experimental results with respect to voltage contrast images (e.g., Fig. 9), consistent with embodiments of the present disclosure.DETAILED DESCRIPTION

[0021] 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 the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the disclosure. Instead, they are merely examples of apparatuses and methods consistent with aspects related to the subject matter recited in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the disclosure is not so limited. Other types of charged particle beams may be similarly applied. Furthermore, other imaging systems may be used, such as optical imaging, photodetection, x-ray detection, extreme ultraviolet inspection, deep ultraviolet inspection, or the like, in which they generate corresponding types of images.

[0022] Electronic devices are constructed of circuits formed on a piece of silicon called a substrate. Many circuits may be formed together on the same piece of silicon and are called integrated circuits or ICs. The size of these circuits has decreased dramatically so that many more of them can fit on the substrate. For example, an IC chip in a smart phone can be as small as a thumbnail and yet may include over 2 billion transistors, the size of each transistor being less than 1 / 1000th the size of a human hair.

[0023] Making these extremely small ICs is a complex, time-consuming, and expensive process, often involving hundreds of individual steps. Errors in even one step have the potential to result in defects in the finished IC rendering it useless. Thus, one goal of the manufacturing process is to avoid such defects to maximize the number of functional ICs made in the process, that is, to improve the overall yield of the process.

[0024] One component of improving yield is monitoring the chip making process to ensure that it is producing a sufficient number of functional ICs. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. Inspection may be carried out using a scanning electron microscope (SEM). A SEM can be used to image these extremely small structures, in effect, taking a “picture” of the structures of the wafer. The image can be used to determine if the structure was formed properly, and also if it was formed at the proper location. If the structure is defective, then the process can be adjusted so the defect is less likely to recur. Defects may be generated during various stages of semiconductor processing. For the reason stated above, it is important to find defects accurately and efficiently as early as possible.

[0025] The working principle of a SEM is similar to a camera. A camera takes a picture by receiving and recording brightness and colors of light reflected or emitted from people or objects. A SEM takes a “picture” by receiving and recording energies or quantities of electrons reflected or emitted from the structures. Before taking such a “picture,” an electron beam may be projected onto the structures, and when the electrons are reflected or emitted (“exiting”) from the structures, a detector of the SEM may receive and record the energies or quantities of those electrons to generate an image. To take such a “picture,” some SEMs use a single electron beam (referred to as a “single-beam SEM”), while some SEMs use multiple electron beams (referred to as a “multi-beam SEM”) to take multiple “pictures” of the wafer. By using multiple electron beams, the SEM may project more electron beams onto the structures for obtaining these multiple “pictures,” resulting in more electrons exiting from the structures. Accordingly, the detector may receive more exiting electrons simultaneously, and generate images of the structures of the wafer with a higher efficiency and a faster speed.

[0026] Alignment of image data with reference data may be performed during inspection or metrology for each image of a sample to improve inspection and metrology results. For example, after a SEM generates an image, alignment may be performed to align the generated image with reference data (e.g., layout / GDS data) to determine the location of each polygon of a pattern in the image. After alignment, defect inspection or metrology analysis of the image may be performed.

[0027] Typical SEM image alignment methods involve finding a maximum similarity metric at an aligned location by determining a cross-correlation between a SEM image and its reference image or reference layout. Determining the cross-correlation involves determining a Fast Fourier Transform (FFT), which reduces computational complexity of global alignment from O(N2) to O(NlogN), where N is the number of pixels.

[0028] Existing SEM image alignment methods include scale adaptive alignment for die to database (D2DB), which are cross-correlation based. Scale adaptive alignment includes the following steps: Searching a scale parameter for dilation or erosion and matching pattern sizes to solve the different pattern size problem between a SEM image and its layout GDS; extracting dark, bright, or edge pixels as alignment signals; and determining the cross-correlation between the SEM image and its reference image or reference layout and outputting a final alignment solution.

[0029] The above-described alignment method may be used for voltage contrast (VC) images. Compared to SEM images, VC images typically have larger distortions due to higher current, larger pixel sizes, and mixed bits grey level. These fundamental image condition differences contribute to GDS alignment challenges with respect to VC images. Mapping the failure bit to the design structure accurately is important for VC defect data and device failure analysis.

[0030] Typical image alignment methods, however, suffer from constraints. For example, existing scale adaptive alignment methods cannot solve the pitch jump misalignment problem for replicated array patterns where the misalignment is greater than one pitch. The existing scale adaptive alignment methods cannot solve this problem because they align all patterns in a field of view (FOV), especiallyfor repetitive patterns. It is difficult to distinguish a global peak from local peaks with similar crosscorrelation values (see pitch jump misalignment, e.g., Fig. 5). Therefore, the alignment result is unstable to image noise and pattern variations.

[0031] Typical image alignment methods also suffer from constraints because in practice, SEM image patterns and their references (e.g., GDS data) have different sizes and shapes (see, e.g., Fig. 6). Alignment between the GDS data and the image may be performed by determining the maximum peak on the frequency map. The maximum peak on the frequency map may correspond to the alignment (e.g., x,y coordinate shifts for each pixel) that may be applied between the GDS data and the image. However, when the misalignment is less than one pitch, the frequency map may show a flat region around a global peak on the corresponding cross-correlation map (see snapping misalignment, e.g., Fig. 6). For example, a deformed global peak may cause the resulting alignment between the GDS data and the image to be shifted away from the center of each pattern for at least some pixels (known as the snapping problem). As a result, the applied alignment may be inaccurate.

[0032] Additionally, binarized voltage contrast images with mixed grey levels have significant noise when aligning with binarized GDS data, which negatively impacts alignment accuracy.

[0033] The disclosed embodiments provide systems and methods that address some or all of these disadvantages by performing alignment methods using boundary masks of GDS data and images of samples. Instead of using an entire GDS image or entire SEM image to determine alignment, the disclosed embodiments use a masked region of boundaries of the images. Using boundary masks is beneficial to the alignment process because it breaks up the repeating patterns in the images, resulting in a more accurate alignment determination. Advantageously, by using boundary masks for the GDS data and the SEM image, the disclosed embodiments mitigate pitch jumps and snapping issues.

[0034] Relative dimensions of components in drawings may be exaggerated for clarity. Within the following description of drawings, the same or like reference numbers refer to the same or like components or entities, and only the differences with respect to the individual embodiments are described.

[0035] As used herein, unless specifically stated otherwise, the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a component may include A or B, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component may include A, B, or C, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0036] Without limiting the scope of the present disclosure, some embodiments may be described in the context of providing detectors and detection methods in systems utilizing electron beams. However, the disclosure is not so limited. Other types of charged particle beams may be similarly applied. Furthermore, systems and methods for detection may be used in other imaging systems, such as optical imaging, photon detection, x-ray detection, ion detection, etc.

[0037] Fig. 1 illustrates an exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure. EBI system 100 may be used for imaging. As shown in Fig. 1, EBI system 100 includes a main chamber 101, a load / lock chamber 102, an electron beam tool 104, and an equipment front end module (EFEM) 106. Electron beam tool 104 is located within main chamber 101. EFEM 106 includes a first loading port 106a and a second loading port 106b. EFEM 106 may include additional loading port(s). First loading port 106a and second loading port 106b receive wafer front opening unified pods (FOUPs) that contain wafers (e.g., semiconductor wafers or wafers made of other material(s)) or samples to be inspected (wafers and samples may be used interchangeably). A “lot” is a plurality of wafers that may be loaded for processing as a batch.

[0038] One or more robotic arms (not shown) in EFEM 106 may transport the wafers to load / lock chamber 102. Load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) which removes gas molecules in load / lock chamber 102 to reach a first pressure below the atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) may transport the wafer from load / lock chamber 102 to main chamber 101. Main chamber 101 is connected to a main chamber vacuum pump system (not shown) which removes gas molecules in main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is subject to inspection by electron beam tool 104. Electron beam tool 104 may be a single-beam system or a multi-beam system.

[0039] A controller 109 is electronically connected to electron beam tool 104. Controller 109 may be a computer configured to execute various controls of EBI system 100. While controller 109 is shown in Fig. 1 as being outside of the structure that includes main chamber 101, load / lock chamber 102, and EFEM 106, it is appreciated that controller 109 may be a part of the structure.

[0040] In some embodiments, controller 109 may include one or more processors (not shown). A processor may be a generic or specific electronic device capable of manipulating or processing information. For example, the processor may include any combination of any number of a central processing unit (or “CPU”), a graphics processing unit (or “GPU”), an optical processor, a programmable logic controllers, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a Programmable Logic Array (PLA), a Programmable Array Logic (PAL), a Generic Array Logic (GAL), a Complex Programmable Logic Device (CPLD), a Field- Programmable Gate Array (FPGA), a System On Chip (SoC), an Application-Specific Integrated Circuit (ASIC), a neural processing unit (NPU), and any type of circuit capable of data processing. The processor may also be a virtual processor that includes one or more processors distributed across multiple machines or devices coupled via a network.

[0041] In some embodiments, controller 109 may further include one or more memories (not shown). A memory may be a generic or specific electronic device capable of storing codes and data accessible by the processor (e.g., via a bus). For example, the memory may include any combination of any number of a random-access memory (RAM), a read-only memory (ROM), an optical disc, a magneticdisk, a hard drive, a solid-state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, or any type of storage device. The codes may include an operating system (OS) and one or more application programs (or “apps”) for specific tasks. The memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices coupled via a network.

[0042] Embodiments of this disclosure may provide a single charged-particle beam imaging system (“single-beam system”). Compared with a single-beam system, a multiple charged-particle beam imaging system (“multi-beam system”) may be designed to optimize throughput for different scan modes. Embodiments of this disclosure provide a multi-beam system with the capability of optimizing throughput for different scan modes by using beam arrays with different geometries and adapting to different throughputs and resolution requirements.

[0043] Reference is now made to Fig. 2A, which is a schematic diagram illustrating an exemplary electron beam tool 104 including a multi -beam inspection tool that is part of the EBI system 100 of Fig. 1, consistent with embodiments of the present disclosure. In some embodiments, electron beam tool 104 may be operated as a single-beam inspection tool that is part of EBI system 100 of Fig. 1. Multi -beam electron beam tool 104 (also referred to herein as apparatus 104) comprises an electron source 201, a Coulomb aperture plate (or “gun aperture plate”) 271, a condenser lens 210, a source conversion unit 220, a primary projection system 230, a motorized stage 209, and a sample holder 207 supported by motorized stage 209 to hold a sample 208 (e.g., a wafer or a photomask) to be inspected. Multi -beam electron beam tool 104 may further comprise a secondary projection system 250 and an electron detection device 240. Primary projection system 230 may comprise an objective lens 231. Electron detection device 240 may comprise a plurality of detection elements 241, 242, and 243. A beam separator 233 and a deflection scanning unit 232 may be positioned inside primary projection system 230.

[0044] Electron source 201, Coulomb aperture plate 271, condenser lens 210, source conversion unit 220, beam separator 233, deflection scanning unit 232, and primary projection system 230 may be aligned with a primary optical axis 204 of apparatus 104. Secondary projection system 250 and electron detection device 240 may be aligned with a secondary optical axis 251 of apparatus 104.

[0045] Electron source 201 may comprise a cathode (not shown) and an extractor or anode (not shown), in which, during operation, electron source 201 is configured to emit primary electrons from the cathode and the primary electrons are extracted or accelerated by the extractor and / or the anode to form a primary electron beam 202 that form a primary beam crossover (virtual or real) 203. Primary electron beam 202 may be visualized as being emitted from primary beam crossover 203.

[0046] Source conversion unit 220 may comprise an image-forming element array (not shown), an aberration compensator array (not shown), a beam-limit aperture array (not shown), and a pre-bending micro-deflector array (not shown). In some embodiments, the pre-bending micro-deflector array deflects a plurality of primary beamlets 211, 212, 213 of primary electron beam 202 to normally enterthe beam-limit aperture array, the image-forming element array, and an aberration compensator array. In some embodiments, apparatus 104 may be operated as a single-beam system such that a single primary beamlet is generated. In some embodiments, condenser lens 210 is designed to focus primary electron beam 202 to become a parallel beam and be normally incident onto source conversion unit 220. The image-forming element array may comprise a plurality of micro-deflectors or micro-lenses to influence the plurality of primary beamlets 211, 212, 213 of primary electron beam 202 and to form a plurality of parallel images (virtual or real) of primary beam crossover 203, one for each of the primary beamlets 211, 212, and 213. In some embodiments, the aberration compensator array may comprise a field curvature compensator array (not shown) and an astigmatism compensator array (not shown). The field curvature compensator array may comprise a plurality of micro-lenses to compensate field curvature aberrations of the primary beamlets 211, 212, and 213. The astigmatism compensator array may comprise a plurality of micro-stigmators to compensate astigmatism aberrations of the primary beamlets 211, 212, and 213. The beam -limit aperture array may be configured to limit diameters of individual primary beamlets 211, 212, and 213. Fig. 2A shows three primary beamlets 211, 212, and 213 as an example, and it is appreciated that source conversion unit 220 may be configured to form any number of primary beamlets. Controller 109 may be connected to various parts of EBI system 100 of Fig. 1, such as source conversion unit 220, electron detection device 240, primary projection system 230, or motorized stage 209. In some embodiments, as explained in further details below, controller 109 may perform various image and signal processing functions. Controller 109 may also generate various control signals to govern operations of the charged particle beam inspection system.

[0047] Condenser lens 210 is configured to focus primary electron beam 202. Condenser lens 210 may further be configured to adjust electric currents of primary beamlets 211, 212, and 213 downstream of source conversion unit 220 by varying the focusing power of condenser lens 210. Alternatively, the electric currents may be changed by altering the radial sizes of beam -limit apertures within the beam-limit aperture array corresponding to the individual primary beamlets. The electric currents may be changed by both altering the radial sizes of beam -limit apertures and the focusing power of condenser lens 210. Condenser lens 210 may be an adjustable condenser lens that may be configured so that the position of its first principal plane is movable. The adjustable condenser lens may be configured to be magnetic, which may result in off-axis beamlets 212 and 213 illuminating source conversion unit 220 with rotation angles. The rotation angles change with the focusing power or the position of the first principal plane of the adjustable condenser lens. Condenser lens 210 may be an anti-rotation condenser lens that may be configured to keep the rotation angles unchanged while the focusing power of condenser lens 210 is changed. In some embodiments, condenser lens 210 may be an adjustable anti-rotation condenser lens, in which the rotation angles do not change when its focusing power and the position of its first principal plane are varied.

[0048] Objective lens 231 may be configured to focus beamlets 211, 212, and 213 onto a sample 208 for inspection and may form, in the current embodiments, three probe spots 221, 222, and 223 on the surface of sample 208. Coulomb aperture plate 271, in operation, is configured to block off peripheral electrons of primary electron beam 202 to reduce Coulomb effect. The Coulomb effect may enlarge the size of each of probe spots 221, 222, and 223 of primary beamlets 211, 212, 213, and therefore deteriorate inspection resolution.

[0049] Beam separator 233 may, for example, be a Wien filter comprising an electrostatic deflector generating an electrostatic dipole field and a magnetic dipole field (not shown in Fig. 2A). In operation, beam separator 233 may be configured to exert an electrostatic force by electrostatic dipole field on individual electrons of primary beamlets 211, 212, and 213. The electrostatic force is equal in magnitude but opposite in direction to the magnetic force exerted by magnetic dipole field of beam separator 233 on the individual electrons. Primary beamlets 211, 212, and 213 may therefore pass at least substantially straight through beam separator 233 with at least substantially zero deflection angles.

[0050] Deflection scanning unit 232, in operation, is configured to deflect primary beamlets 211, 212, and 213 to scan probe spots 221, 222, and 223 across individual scanning areas in a section of the surface of sample 208. In response to incidence of primary beamlets 211, 212, and 213 or probe spots 221, 222, and 223 on sample 208, electrons emerge from sample 208 and generate three secondary electron beams 261, 262, and 263. Each of secondary electron beams 261, 262, and 263 typically comprise secondary electrons (having electron energy < 50eV) and backscattered electrons (having electron energy between 50eV and the landing energy of primary beamlets 211, 212, and 213). Beam separator 233 is configured to deflect secondary electron beams 261, 262, and 263 towards secondary projection system 250. Secondary projection system 250 subsequently focuses secondary electron beams 261, 262, and 263 onto detection elements 241, 242, and 243 of electron detection device 240. Detection elements 241, 242, and 243 are arranged to detect corresponding secondary electron beams 261, 262, and 263 and generate corresponding signals which are sent to controller 109 or a signal processing system (not shown), e.g., to construct images of the corresponding scanned areas of sample 208.

[0051] In some embodiments, detection elements 241, 242, and 243 detect corresponding secondary electron beams 261, 262, and 263, respectively, and generate corresponding intensity signal outputs (not shown) to an image processing system (e.g., controller 109). In some embodiments, each detection element 241, 242, and 243 may comprise one or more pixels. The intensity signal output of a detection element may be a sum of signals generated by all the pixels within the detection element.

[0052] In some embodiments, controller 109 may comprise image processing system that includes an image acquirer (not shown), a storage (not shown). The image acquirer may comprise one or more processors. For example, the image acquirer may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combinationthereof. The image acquirer may be communicatively coupled to electron detection device 240 of apparatus 104 through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, among others, or a combination thereof. In some embodiments, the image acquirer may receive a signal from electron detection device 240 and may construct an image. The image acquirer may thus acquire images of sample 208. The image acquirer may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, and the like. The image acquirer may be configured to perform adjustments of brightness and contrast, etc. of acquired images. In some embodiments, the storage may be a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), other types of computer readable memory, and the like. The storage may be coupled with the image acquirer and may be used for saving scanned raw image data as original images, and post-processed images.

[0053] In some embodiments, the image acquirer may acquire one or more images of a sample based on an imaging signal received from electron detection device 240. An imaging signal may correspond to a scanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas. The single image may be stored in the storage. The single image may be an original image that may be divided into a plurality of regions. Each of the regions may comprise one imaging area containing a feature of sample 208. The acquired images may comprise multiple images of a single imaging area of sample 208 sampled multiple times over a time sequence. The multiple images may be stored in the storage. In some embodiments, controller 109 may be configured to perform image processing steps with the multiple images of the same location of sample 208.

[0054] In some embodiments, controller 109 may include measurement circuitries (e.g., analog-to- digital converters) to obtain a distribution of the detected secondary electrons. The electron distribution data collected during a detection time window, in combination with corresponding scan path data of each of primary beamlets 211, 212, and 213 incident on the wafer surface, can be used to reconstruct images of the wafer structures under inspection. The reconstructed images can be used to reveal various features of the internal or external structures of sample 208, and thereby can be used to reveal any defects that may exist in the wafer.

[0055] In some embodiments, controller 109 may control motorized stage 209 to move sample 208 during inspection of sample 208. In some embodiments, controller 109 may enable motorized stage 209 to move sample 208 in a direction continuously at a constant speed. In other embodiments, controller 109 may enable motorized stage 209 to change the speed of the movement of sample 208 over time depending on the steps of scanning process.

[0056] Although Fig. 2A shows that apparatus 104 uses three primary electron beams, it is appreciated that apparatus 104 may use one, two, or more number of primary electron beams. The present disclosure does not limit the number of primary electron beams used in apparatus 104. Insome embodiments, apparatus 104 may be a SEM used for lithography. In some embodiments, electron beam tool 104 may be a single-beam system or a multi -beam system.

[0057] For example, as shown in Fig. 2B, an electron beam tool 100B (also referred to herein as apparatus 100B) may be a single-beam inspection tool that is used in EBI system 100, consistent with embodiments of the present disclosure. Apparatus 100B includes a wafer holder 136 supported by motorized stage 134 to hold a wafer 150 to be inspected. Electron beam tool 100B includes an electron emitter, which may comprise a cathode 103, an anode 121, and a gun aperture 122. Electron beam tool 100B further includes a beam limit aperture 125, a condenser lens 126, a column aperture 135, an objective lens assembly 132, and a detector 144. Objective lens assembly 132, in some embodiments, may be a modified SORIL lens, which includes a pole piece 132a, a control electrode 132b, a deflector 132c, and an exciting coil 132d. In an imaging process, an electron beam 161 emanating from the tip of cathode 103 may be accelerated by anode 121 voltage, pass through gun aperture 122, beam limit aperture 125, condenser lens 126, and be focused into a probe spot 170 by the modified SORIL lens and impinge onto the surface of wafer 150. Probe spot 170 may be scanned across the surface of wafer 150 by a deflector, such as deflector 132c or other deflectors in the SORIL lens. Secondary or scattered primary particles, such as secondary electrons or scattered primary electrons emanated from the wafer surface may be collected by detector 144 to determine intensity of the beam and so that an image of an area of interest on wafer 150 may be reconstructed.

[0058] There may also be provided an image processing system 199 that includes an image acquirer 120, a storage 130, and controller 109. Image acquirer 120 may comprise one or more processors. For example, image acquirer 120 may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combination thereof. Image acquirer 120 may connect with detector 144 of electron beam tool 100B through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or a combination thereof. Image acquirer 120 may receive a signal from detector 144 and may construct an image. Image acquirer 120 may thus acquire images of wafer 150. Image acquirer 120 may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, and the like. Image acquirer 120 may be configured to perform adjustments of brightness and contrast, etc. of acquired images. Storage 130 may be a storage medium such as a hard disk, random access memory (RAM), cloud storage, other types of computer readable memory, and the like. Storage 130 may be coupled with image acquirer 120 and may be used for saving scanned raw image data as original images, and post-processed images. Image acquirer 120 and storage 130 may be connected to controller 109. In some embodiments, image acquirer 120, storage 130, and controller 109 may be integrated together as one electronic control unit.

[0059] In some embodiments, image acquirer 120 may acquire one or more images of a sample based on an imaging signal received from detector 144. An imaging signal may correspond to ascanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas that may contain various features of wafer 150. The single image may be stored in storage 130. Imaging may be performed on the basis of imaging frames.

[0060] The condenser and illumination optics of the electron beam tool may comprise or be supplemented by electromagnetic quadrupole electron lenses. For example, as shown in Fig. 2B, electron beam tool 100B may comprise a first quadrupole lens 148 and a second quadrupole lens 158. In some embodiments, the quadrupole lenses are used for controlling the electron beam. For example, first quadrupole lens 148 can be controlled to adjust the beam current and second quadrupole lens 158 can be controlled to adjust the beam spot size and beam shape.

[0061] Fig. 2B illustrates a charged particle beam apparatus in which an inspection system may use a single primary beam that may be configured to generate secondary electrons by interacting with wafer 150. Detector 144 may be placed along optical axis 105, as in the embodiment shown in Fig. 2B. The primary electron beam may be configured to travel along optical axis 105. Accordingly, detector 144 may include a hole at its center so that the primary electron beam may pass through to reach wafer 150.

[0062] Fig. 3 illustrates an exemplary graph showing a yield rate of secondary electrons relative to landing energy of primary electron beamlets, consistent with embodiments of the present disclosure. The graph illustrates the relationship of the landing energy of a primary electron beam (e.g., primary electron beam 202 of Fig. 2) and the yield rate of secondary electron beams). The yield rate indicates the number of secondary electrons that are produced in response to the impact of the primary electrons. For example, a yield rate greater than 1.0 indicates that more secondary electrons may be produced than the number of primary electrons that have landed on the wafer. Similarly, a yield rate of less than 1.0 indicates that less secondary electrons may be produced in response to the impact of the primary electrons.

[0063] As shown in the graph of Fig. 3, when the landing energy of the primary electrons is within a range from Ei to E2, more secondary electrons may leave the surface of the wafer than land onto the surface of the wafer, which may result in a positive electrical potential at the surface of the wafer. In some embodiments, defect inspection may be performed in the foregoing range of landing energies, which is called “positive mode.” An electron beam tool (e.g., electron beam tool 104 of Fig. 2) may generate a darker voltage contrast image of a device structure with a more positive surface potential since a detection device (e.g., detection device 240 of Fig. 2) may receive less secondary electrons (see Fig. 4).

[0064] When the landing energy is lower than Ei or higher than E2, less electrons may leave the surface of the wafer, thereby resulting in a negative electrical potential at the surface of the wafer. In some embodiments, defect inspection may be performed in this range of the landing energies, which is called “negative mode.” An electron beam tool (e.g., electron beam tool 104 of Fig. 2) maygenerate a brighter voltage contrast image of a device structure with a more negative surface potential a detection device (e.g., detection device 240 of Fig. 2) may receive more secondary electrons (see Fig. 4).

[0065] In some embodiments, the landing energy of the primary electron beams may be controlled by the total bias between the electron source and the wafer.

[0066] Fig. 4 illustrates a schematic diagram of a voltage contrast response of a wafer, consistent with embodiments of the present disclosure. In some embodiments, physical and electrical defects in a wafer (e.g., resistive shorts and opens, defects in deep trench capacitors, back end of line (BEOL) defects, etc.) can be detected using a voltage contrast method of a charged particle inspection system. Defect detection using voltage contrast images may use a pre-scanning process (i.e., a charging, flooding, neutralization, or prepping process), where charged particles are applied to an area of the wafer (e.g., sample 208 of Fig. 2) to be inspected before conducting the inspection.

[0067] In some embodiments, an electron beam tool (e.g., electron beam tool 104 of Fig. 2) may be used to detect defects in internal or external structures of a wafer by illuminating the wafer with a plurality of beamlets of a primary electron beam (e.g., plurality of beamlets 211, 212, or 213 of primary electron beam 202 of Fig. 2) and measuring a voltage contrast response of the wafer to the illumination. In some embodiments, the wafer may comprise a test device region 420 that is developed on a substrate 410. In some embodiments, test device region 420 may include multiple device structures 430 and 440 separated by insulating material 450. For example, device structure 430 is connected to substrate 410. In contrast, device structure 440 is separated from substrate 410 by insulating material 450 such that a thin insulator structure 470 (e.g., thin oxide) exists between device structure 440 and substrate 410.

[0068] The electron beam tool may generate secondary electrons (e.g., secondary electron beams 261, 262, or 263 of Fig. 2) from the surface of test device region 420 by scanning the surface of test device region 420 with a plurality of beamlets of a primary electron beam. As explained above, when the landing energy of the primary electrons is between Ei and E2(i.e., the yield rate is greater than 1.0 in Fig. 3), more electrons may leave the surface of the wafer than land on the surface, thereby resulting in a positive electrical potential at the surface of the wafer.

[0069] As shown in Fig. 4, a positive electrical potential may build-up at the surface of a wafer. For example, after an electron beam tool scans test device region 420 (e.g., during a pre-scanning process), device structure 440 may retain more positive charges because device structure 440 is not connected to an electrical ground in substrate 410, thereby resulting in a positive electrical potential at the surface of device structure 440. In contrast, primary electrons with the same landing energy (i.e., the same yield rate) applied to device structure 430 may result in less positive charges retained in device structure 430 since positive charges may be neutralized by electrons supplied by the connection to substrate 410.

[0070] An image processing system (e.g., controller 109 of Fig. 2) of an electron beam tool may generate voltage contrast images 435 and 445 of corresponding device structures 430 and 440, respectively. For example, device structure 430 is shorted to the ground and may not retain built-up positive charges. Accordingly, when primary electron beamlets land on the surface of the wafer during inspection, device structure 430 may repel more secondary electrons thereby resulting in a brighter voltage contrast image. In contrast, because device structure 440 has no connection to substrate 410 or any other grounds, device structure 440 may retain a build-up of positive charges. This build-up of positive charges may cause device structure 440 to repel less secondary electrons during inspection, thereby resulting in a darker voltage contrast image.

[0071] An electron beam tool (e.g., multi-beam electron beam tool 104 of Fig. 2) may pre-scan the surface of a wafer by supplying electrons to build up the electrical potential on the surface of the wafer. After pre-scanning the wafer, the electron beam tool may obtain images of multiple dies within the wafer. In some embodiments, defects may be detected by comparing the differences in voltage contrast images from multiple dies. In some embodiments, defects may be identified by imaging the same locations (e.g., points of interests) in different dies and comparing voltage contrast images (e.g., grey levels in the images) of the same locations in different dies. In some embodiments, defects may be identified by imaging the same locations in different areas of the same die (where the areas have the same structure or layout) and comparing voltage contrast images of the different areas of the same die. Defects may be identified if the differences in voltage contrast or grey levels (e.g., due to different surface potentials caused by different electrical properties at the points of interest) exceed a predefined threshold. Pre-scanning is applied to the wafer under the assumption that the electrical surface potential built-up on the surface of the wafer during pre-scanning will be retained during inspection and will remain above the detection threshold of the electron beam tool.

[0072] In some embodiments, an effect of leakage current may occur in a structure with improperly formed materials or a high resistance metal layer, for example a cobalt silicide (e.g., CoSi, CoSi2, Co2Si, CosSi, etc.) layer between a tungsten plug and a source or drain area of a field-effect transistor (FET).

[0073] A defective etching process may leave a thin oxide resulting in unwanted electrical blockage (e.g., open circuit) between two structures (e.g., device structure 440 and substrate 410) intended to be electrically connected. For example, device structures 430 and 440 may be designed to make contact with substrate 410 and function identically, but due to manufacturing errors, insulator structure 470 may exist in device structure 440. In this case, insulator structure 470 may represent a defect susceptible to a breakdown effect.

[0074] Reference is now made to Fig. 5A, which shows a diagram 500A of an image 510A including polygons 512A representing a repeating pattern on a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B).

[0075] Elements 514A represent reference / layout / GDS data (e.g., a GDS rendered image). As shown in Fig. 5A, image 510 has an alignment between the repeating pattern of polygons 512A and the GDS data 514A (e.g., the centers of polygons 512A are aligned with the centers of GDS data 514A). Graph 520A shows a frequency map of the calculated cross-correlation between each polygon 512A and corresponding GDS data 514A. For example, graph 520A shows a cross-correlation frequency 522A with respect to alignment location 524A. Each cross-correlation peak (e.g., peak 526A) corresponds to a cross-correlation between a polygon 512A and corresponding GDS data 514A.

[0076] Alignment between the GDS data 514A and the pattern 512A in image 510A may be performed by determining the maximum peak (e.g., peak 526A) on the frequency map in graph 520A. The maximum peak on the frequency map may correspond to the alignment (e.g., x,y coordinate shifts for each pixel) that may be applied between the GDS data and the image. As shown in Fig. 5A, when there is higher alignment between polygons 512A and GDS data 514A, the frequency map may show a single maximum peak to indicate the alignment value that can be applied to the images. As a result, the applied alignment may be considered accurate.

[0077] Reference is now made to Fig. 5B, which shows a diagram 500B of an image 510B including polygons 512B representing a repeating pattern on a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B).

[0078] Elements 514B represent reference / layout / GDS data (e.g., a GDS rendered image). As shown in Fig. 5B, image 510B has a misalignment between the repeating pattern of polygons 512B and the GDS data 514B of greater than one pitch. Graph 520B shows a frequency map of the calculated crosscorrelation between each polygon 512B and corresponding GDS data 514B. For example, graph 520B shows a cross-correlation frequency 522B with respect to alignment location 524B. Each crosscorrelation peak (e.g., peak 526B) corresponds to a cross-correlation between a polygon 512B and corresponding GDS data 514B.

[0079] Alignment between the GDS data 514B and the pattern 512B in image 510B may be performed by determining the maximum peak (e.g., peak 526B) on the frequency map in graph 520B. The maximum peak on the frequency map may correspond to the alignment (e.g., x,y coordinate shifts for each pixel) that may be applied between the GDS data and the image. However, as shown in Fig. 5B, when the misalignment is greater than one pitch, the frequency map may show several maximum peaks at approximately the same level. As a result, the applied alignment may be inaccurate based on a pitch-jump misalignment (as compared to Fig. 5A).

[0080] For example, existing scale adaptive alignment methods cannot solve the pitch jump misalignment problem for replicated array patterns where the misalignment is greater than one pitch. The existing scale adaptive alignment methods cannot solve this problem because they align all patterns in a field of view (FOV), especially for repetitive patterns. It is difficult to distinguish a global peak from local peaks with similar cross-correlation values, as shown in graph 520B.Therefore, the alignment result is unstable for image noise and pattern variations.

[0081] Reference is now made to Fig. 6, which shows a diagram 600 of an image 610 including polygons 612 representing a repeating pattern on a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B)

[0082] Elements 614 represent GDS data. As shown in Fig. 6, image 610 has a misalignment between the repeating pattern of polygons 612 and the GDS data 614 of less than one pitch. Graph 620 shows a frequency map of the calculated cross-correlation between each polygon 612 and corresponding GDS data 614. For example, graph 620 shows a cross-correlation frequency 622 with respect to alignment location 624. Each cross-correlation peak corresponds to a cross-correlation between a polygon 612 and corresponding GDS data 614.

[0083] Typical image alignment methods also suffer from constraints because in practice, SEM image patterns and their references (e.g., GDS data) have different sizes and shapes. For example, alignment between the GDS data 614 and the pattern 612 in image 610 may be performed by determining the maximum peak on the frequency map. As described above, the maximum peak on the frequency map may correspond to the alignment (e.g., x,y coordinate shifts for each pixel) that may be applied between the GDS data and the image.

[0084] However, as shown in graph 620, when the misalignment is less than one pitch, the frequency map may show a flat region around a global peak 626 on the corresponding cross-correlation map. For example, a deformed global peak may cause the resulting alignment between the GDS data and the image to be shifted away from the center of each pattern for at least some pixels (known as the snapping problem). As a result, the applied alignment may be inaccurate based on a snapping misalignment (as compared to Fig. 5A).

[0085] Fig. 7 shows a process 700 of image alignment, consistent with embodiments of the present disclosure.

[0086] At step 702, a system (e.g., controller 109 of Fig. 2A or Fig. 2B) may combine the Gaussian blur and pixel-wise square to generate the density function, yt= ( jENtwjxj)is a neighbor set around current pixel location index i, j is a neighbor pixel’s index, Wj are weights of a Gaussian filter, Xj is an x-coordinate of the corresponding pixel, and yz is the output value of the density function. The system may render GDS data corresponding to a sample image (e.g., SEM image) into a GDS image.

[0087] With respect to GDS data (e.g., reference data), a layout file can be in a Graphic Database System (GDS) format, Graphic Database System II (GDS II) format, an Open Artwork System Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), etc. A wafer design may include patterns or structures for inclusion on the wafer. The patterns or structures can be mask patterns used to transfer features from the photolithography masks or reticles to a wafer. In some embodiments, a layout in GDS or OASIS format, among others, may comprise feature informationstored in a binary file format representing planar geometric shapes, text, and other information related to the wafer design.

[0088] The system may apply the density function to both the GDS image and the SEM image to smooth the images (e.g., generating a SEM image and a GDS image). Advantageously, the density function may be used in any alignment method (e.g., scale adaptive alignment) to eliminate snapping issues.

[0089] In some embodiments, a parameter of the Gaussian blur filter, Gaussian sigma, may be adjusted and customized to eliminate the snapping issue in alignment (see snapping issue, e.g., Fig. 6). For example, setting the Gaussian sigma with a higher number may result in a stronger smoothing effect applied to the input image, thereby increasing the accuracy of the alignment (see, e.g., Fig. 8).

[0090] At step 704, the system may generate a boundary mask for an image (e.g., SEM image, voltage contrast image, etc.) and a boundary mask for corresponding GDS data (e.g., boundary mask 722). In some embodiments, the boundary masks may be generated by applying Gaussian blurring to the GDS image and the SEM image and applying an erosion operation to the images. Erosion size is a parameter of the erosion operation. The higher the erosion size, the wider the generated boundary mask. The boundary mask may be applied to the GDS image and the SEM image after the density function is applied (e.g., the boundary mask 722 may be applied to image 720) to generate a boundary GDS image (a “masked” GDS image) and a boundary SEM image (a “masked” SEM image) (e.g., generating image 724).

[0091] In some embodiments (e.g., voltage contrast images), a boundary image (e.g., a boundary voltage contrast image or a masked voltage contrast image) may be generated by designing a boundary mark layer into inspection or using a mark region (e.g., Voltage Contrast Edge Placement Error mark (VC-EPE mark)). In some embodiments, boundary or masked voltage contrast images may be generated by selecting bright voltage contrast or dark voltage contrast bits as a boundary mask layer for alignment.

[0092] At step 706, the system may calculate the cross-correlation between the boundary GDS image and the boundary SEM image to generate map 726. The system may identify region 728 (e.g., range of values) with the peak 730 (maximum value) in map 726. In some embodiments, region 728 may include coordinates (x, y) corresponding to an alignment shift between the GDS data and the SEM image, which may be used to determine the final alignment result.

[0093] At step 708, the system may calculate the cross-correlation between the GDS image and the SEM image (e.g., image 720) to generate map 732. The system may use map 732 and region 728 from step 706 as inputs to identify the peak 734 (maximum value) in map 732 within region 728 (e.g., a range of coordinates). The system may use the peak 734 to determine the alignment to apply to the original GDS data (or GDS image) and the original SEM image.

[0094] Advantageously, by using boundary masks for the GDS data and the SEM image, process 700 mitigates pitch jumps and snapping issues. That is, instead of using the entire GDS image or entireSEM image to determine alignment, process 700 uses the masked regions (the boundaries generated in masked images) to determine the alignment, thereby increasing alignment accuracy. Using boundary masks is beneficial to the alignment process because it breaks up the repeating patterns in the images, resulting in a more accurate alignment determination.

[0095] Fig. 8 shows an example diagram 800 of experimental results with respect to adjusted parameters of the density function, consistent with embodiments of the present disclosure.

[0096] In some embodiments, a parameter of the Gaussian blur filter, Gaussian sigma, may be adjusted and customized to eliminate the snapping issue in alignment (see snapping issue, e.g., Fig. 6). For example, setting the Gaussian sigma with a higher number may result in a stronger smoothing effect applied to the input image, thereby increasing the accuracy of the alignment.

[0097] Image 802 shows alignment results when no density function is applied to the GDS data or the SEM image. Image 802 includes polygons 812 of a pattern on a sample and GDS data 822. Image 804 shows alignment results when a Gaussian sigma of 2 is used in the density function and is applied to the GDS image and the SEM image. Image 804 includes polygons 814 of a pattern on a sample and GDS data 824. Image 806 shows alignment results when a Gaussian sigma of 5 is used in the density function and is applied to the GDS image and the SEM image. Image 806 includes polygons 816 of a pattern on a sample and GDS data 826.

[0098] As shown in images 802, 804, and 806, the alignment between the GDS data and the SEM image improves as the Gaussian sigma used in the density function is increased. That is, the center of the pattern is more aligned with the center of the GDS data when a higher Gaussian sigma is used in the density function. In some embodiments, the Gaussian sigma depends on the pattern size (e.g., it is a hyperparameter that may need fine tuning).

[0099] It is understood that the representation of the GDS data in images 802, 804, and 806 are shown for illustration purposes and are not necessarily part of the images.

[0100] Fig. 9 shows a process 900 of image alignment for voltage contrast images, consistent with embodiments of the present disclosure.

[0101] At step 902, a system (e.g., controller 109 of Fig. 2A or Fig. 2B) may design alignment assistant structures and labelling during mark design (e.g., alignment markings may be added to mark designs of a GDS image and a SEM image). For example, a boundary image (e.g., a boundary voltage contrast image or a masked voltage contrast (VC) image) may be generated by designing a boundary mark layer into inspection or using a mark region (e.g., Voltage Contrast Edge Placement Error mark (VC-EPE mark)). The alignment assistant structures may be a boundary mask layer in a GDS VC image (or GDS data) or boundary mask layer 930 in SEM VC image 932 (e.g., boundary mask 722 of Fig. 7). In some embodiments, boundary or masked VC images may be generated by selecting bright VC or dark VC bits as a boundary mask layer for alignment.

[0102] At step 904, the system may label and pass the boundary mask layers for image global alignment between the voltage contrast GDS image and the voltage contrast SEM image.

[0103] At step 906, the system may binarize the GDS VC image and the SEM VC image to generate image 934. The binarization may be performed only at the boundary mask layer region using adaptive threshold.

[0104] At step 908, the system may calculate location offsets between binarized GDS VC image (or data) and binarized SEM VC image 934 at alignment template regions 936 (at the boundaries of the image) by calculating the cross-correlation between the binarized GDS VC image (or data) and binarized SEM VC image 934.

[0105] At step 910, the system may generate a distortion model, feed the calculated location offsets from step 908 into the distortion correction model as input, and determine the alignment to be applied to the GDS data and SEM VC image (e.g., to correct distortion between the GDS data and the SEM image based on the offsets). The system may perform full image correction by applying the determined alignment.

[0106] Fig. 10 shows an example diagram 1000 of experimental results with respect to voltage contrast images (e.g., Fig. 9), consistent with embodiments of the present disclosure.

[0107] Diagram 1000 shows an example SEM VC image 1002 of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B) with alignment assistant structures 1004 (e.g., boundary mask layer, boundary mask layer 930 of Fig. 9). Image 1012 shows an expanded view of image 1002.

[0108] Image 1022 shows a region of SEM VC image 1002, which may be used to determine the alignment between the SEM VC image and the GDS data. Image 1022 includes bright spots 1024 and GDS data 1026.

[0109] Images 1032 and 1042 show another region of SEM VC image 1002. Image 1032 shows an alignment between the GDS data and the SEM VC image using existing alignment methods. Image 1042 shows an alignment between the GDS data and the SEM VC image using embodiments of the present disclosure (e.g., Fig. 9). As shown by images 1032 and 1042, embodiments of the present disclosure result in a more accurate alignment between GDS data and VC images.

[0110] It is understood that the representation of the GDS data in images 1022, 1032, and 1042 are shown for illustration purposes and are not necessarily part of the images.[oni] 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, Fig. 2A, Fig. 2B) for controlling the electron beam tool or other systems of other systems and servers, or components thereof, consistent with embodiments in the present disclosure. These instructions may allow the one or more processors to carry out image processing, data processing, beamlet scanning, graphical display, operations of a charged particle beam apparatus, or another imaging device, or the like for providing operations consistent with those described above for Fig. 7 and Fig. 9. In some embodiments, the non-transitory computer readable medium may be provided that stores instructions for a processor to perform the steps of processes 700 and 900. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic datastorage medium, a Compact Disc Read Only Memory (CD-ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read Only Memory (PROM), and Erasable Programmable Read Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non-Volatile Random Access Memory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same.

[0112] The embodiments may further be described using the following clauses:1. A method for aligning images, comprising: generating a first boundary mask for a first image of a sample and a second boundary mask that is based on reference data; generating a first cross-correlation map based on the first boundary mask and the second boundary mask; determining a peak value within a range of values based on the first cross-correlation map; and aligning the first and second images based on the peak.2. The method of clause 1, further comprising generating a density function and applying the density function to the first image and to a second image based on the reference data.3. The method of clause 2, wherein applying the density function to the first image and to the second image increases the smoothness of the first and second images.4. The method of any one of clauses 2-3, wherein generating the first and second boundary masks comprises applying Gaussian blurring to the first and second images.5. The method of any one of clauses 2-4, wherein generating the first and second boundary masks comprises applying an erosion operation to the first and second images.6. The method of any one of clauses 2-5, further comprising generating a first boundary image by applying the first boundary mask to the first image and generating a second boundary image by applying the second boundary mask to the second image.7. The method of clause 6, wherein the first and second boundary masks are applied to the first and second images, respectively, after the density function is applied to the first and second images.8. The method of any one of clauses 6-7, wherein generating the first cross-correlation map comprises calculating the cross-correlation between the first boundary image and the second boundary image.9. The method of any one of clauses 1-8, further comprising identifying the range of values in the first cross-correlation map.10. The method of any one of clauses 2-9, further comprising generating a second cross-correlation map based on the first and second images by calculating a cross-correlation between the first image and the second image.11. The method of clause 10, determining the peak value comprises determining a maximum value within the range of values in the second cross-correlation map.12. A method of aligning images, comprising: adding alignment markings to mark designs of a first image and a second image; performing global alignment between the first image and the second image; binarizing the first image and the second image; determining offset between the binarized first image and the binarized second image; and correcting distortion between the first and second images based on the determined offset.13. The method of clause 12, wherein the first and second images are voltage contrast images.14. The method of any one of clauses 12-13, wherein the first image is a reference data image and the second image is an image of a sample.15. The method of any one of clauses 12-14, wherein the binarization of the first and second images are performed at boundary regions of the first and second images.16. The method of any one of clauses 12-15, wherein determining the offset between the binarized first image and the binarized second image comprises calculating a cross-correlation between the binarized first and second images at boundary regions of the binarized first and second images.17. The method of any one of clauses 12-16, wherein correcting the distortion comprises determining an alignment to be applied to the first and second images.18. The method of clause 17, wherein correcting the distortion comprises feeding the determined offset into a model and determining the alignment using the model.19. The method of any one of clauses 12-18, wherein the alignment markings comprise boundary mask layers.20. A method for aligning images, comprising: determining a first cross-correlation based on a first boundary mask and a second boundary mask; determining a maximum value based on the determined first cross-correlation; and applying an alignment between a first image corresponding to the first boundary mask and reference data corresponding to the second boundary mask based on the maximum value.21. The method of clause 20, further comprising generating a density function and applying the density function to the first image and to a second image based on the reference data.22. The method of clause 21, wherein applying the density function to the first image and to the second image increases the smoothness of the first and second images.23. The method of any one of clauses 21-22, wherein generating the first and second boundary masks comprises applying Gaussian blurring to the first and second images.24. The method of any one of clauses 21-23, wherein generating the first and second boundary masks comprises applying an erosion operation to the first and second images.25. The method of any one of clauses 21-24, further comprising generating a first boundary image by applying the first boundary mask to the first image and generating a second boundary image by applying the second boundary mask to the second image.26. The method of clause 25, wherein the first and second boundary masks are applied to the first and second images, respectively, after the density function is applied to the first and second images.27. The method of any one of clauses 25-26, wherein determining the first cross-correlation comprises calculating the cross-correlation between the first boundary image and the second boundary image.28. The method of any one of clauses 20-27, further comprising identifying a range of values in the first cross-correlation.29. The method of any one of clauses 21-28, further comprising determining a second crosscorrelation based on the first and second images by calculating a cross-correlation between the first image and the second image.30. The method of any one of clauses 28-29, wherein determining the maximum value is based on the range of values in the second cross-correlation.31. A method of aligning images, comprising: binarizing a first image and a second image, wherein mark designs of the first image and the second image comprise alignment markings; and applying an alignment between the first and second images based on an offset between the binarized first image and the binarized second image.32. The method of clause 31, wherein the first and second images are voltage contrast images.33. The method of any one of clauses 31-32, wherein the first image is a reference data image and the second image is an image of a sample.34. The method of any one of clauses 31-33, wherein the binarization of the first and second images are performed at boundary regions of the first and second images.35. The method of any one of clauses 31-34, wherein determining the offset between the binarized first image and the binarized second image comprises calculating a cross-correlation between the binarized first and second images at boundary regions of the binarized first and second images.36. The method of any one of clauses 31-35, wherein applying the alignment comprises correcting distortion between the first and second images.37. The method of clause 36, wherein correcting the distortion comprises feeding the offset into a model and determining the alignment using the model.38. The method of any one of clauses 31-37, wherein the alignment markings comprise boundary mask layers.39. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for aligning images, the operations comprising any one of clauses 1-11.40. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations of aligning images, the operations comprising any one of clauses 12-19.41. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for aligning images, the operations comprising any one of clauses 20-30.42. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations of aligning images, the operations comprising any one of clauses 31-38.43. A system for aligning images, the system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising any one of clauses 1-11.44. A system of aligning images, the system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising any one of clauses 12-19.45. A system for aligning images, the system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising any one of clauses 20-30.46. A system of aligning images, the system comprising: a memory storing a set of instructions; and one or more processors configured to execute the set of instructions to cause the system to perform operations comprising any one of clauses 31-38.

[0113] It will be appreciated that the embodiments of the present disclosure are not limited to the exact construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

CLAIMS1. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for aligning images, the operations comprising: generating a first boundary mask for a first image of a sample and a second boundary mask that is based on reference data; generating a first cross-correlation map based on the first boundary mask and the second boundary mask; determining a peak value within a range of values based on the first cross-correlation map; and aligning the first and second images based on the peak.

2. The non-transitory computer readable medium of claim 1, wherein the operations further comprise generating a density function and applying the density function to the first image and to a second image based on the reference data.

3. The non-transitory computer readable medium of claim 2, wherein the operations further comprise generating a first boundary image by applying the first boundary mask to the first image and generating a second boundary image by applying the second boundary mask to the second image.

4. The non-transitory computer readable medium of claim 3, wherein the first and second boundary masks are applied to the first and second images, respectively, after the density function is applied to the first and second images.

5. The non-transitory computer readable medium of claim 3, wherein generating the first crosscorrelation map comprises calculating the cross-correlation between the first boundary image and the second boundary image.

6. The non-transitory computer readable medium of claim 1, wherein the operations further comprise identifying the range of values in the first cross-correlation map.

7. The non-transitory computer readable medium of claim 2, wherein the operations further comprise generating a second cross-correlation map based on the first and second images by calculating a crosscorrelation between the first image and the second image.

8. The non-transitory computer readable medium of claim 7, wherein determining the peak value comprises determining a maximum value within the range of values in the second cross-correlation map.

9. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for aligning images, the operations comprising: adding alignment markings to mark designs of a first image and a second image; performing global alignment between the first image and the second image; binarizing the first image and the second image; determining offset between the binarized first image and the binarized second image; and correcting distortion between the first and second images based on the determined offset.

10. The non-transitory computer readable medium of claim 9, wherein the first and second images are voltage contrast images.

11. The non-transitory computer readable medium of claim 9, wherein the first image is a reference data image and the second image is an image of a sample.

12. The non-transitory computer readable medium of claim 9, wherein the binarization of the first and second images are performed at boundary regions of the first and second images.

13. The non-transitory computer readable medium of claim 9, wherein determining the offset between the binarized first image and the binarized second image comprises calculating a cross-correlation between the binarized first and second images at boundary regions of the binarized first and second images.

14. The non-transitory computer readable medium of claim 9, wherein correcting the distortion comprises determining an alignment to be applied to the first and second images.

15. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for aligning images, the operations comprising: binarizing a first image and a second image, wherein mark designs of the first image and the second image comprise alignment markings; and applying an alignment between the first and second images based on an offset between the binarized first image and the binarized second image.

Images