Systems and methods for guided template matching in metrology systems

Abstract

Systems and methods for guided template matching may include generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.

Description

SYSTEMS AND METHODS FOR GUIDED TEMPLATE MATCHING IN METROLOGY SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of US application 63 / 627,723 which was filed on 31 January 2024, and which is incorporated herein in its entirety by reference.FIELD

[0002] The description herein relates to the field of metrology and charged particle systems, and more particularly to methods for guided template matching in metrology systems.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 guided template matching. Embodiments may include generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.

[0006] Embodiments may include determining a characteristic of each feature of a plurality of features in an image; generating a template of each feature based on the determined characteristic of the corresponding feature; comparing the template to the corresponding feature of the image, wherein comparing the template comprises adjusting the template using a range of adjustment factors; generating a final template using the comparison; and outputting a position of each feature based on the final template.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Fig. 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system, consistent with embodiments of the present disclosure.

[0008] 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.

[0009] 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.

[0010] Fig. 3A shows an exemplary image of a sample.

[0011] Fig. 3B shows an exemplary image of a sample, consistent with embodiments of the present disclosure.

[0012] Fig. 4 shows exemplary images associated with guided template matching, consistent with embodiments of the present disclosure.

[0013] Fig. 5 shows an exemplary template image, consistent with embodiments of the present disclosure.

[0014] Fig. 6 shows exemplary diagrams and exemplary graph associated with guided template matching, consistent with embodiments of the present disclosure.

[0015] Fig. 7 shows an exemplary process for guided template matching, consistent with embodiments of the present disclosure.DETAILED DESCRIPTION

[0016] 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 besimilarly 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] Typical systems may use a SEM to acquire high spatial resolution images of test targets and actual devices or device-like structures on wafers. Typical image processing algorithms have beendeveloped to extract various information about targets on the wafer, including critical dimension (CD), overlay, defects, etc.

[0022] For example, in obtaining overlay metrology data, typical systems may use different image processing methods, such as segmentation (e.g., segmenting an image into bright and dark areas), contour finding (e.g., finding transitions between regions of sample), and template matching algorithms to process SEM images and report overlay values for semiconductor manufacture process control and monitoring. The features that have the same design on the reticle may have similar sizes in SEM images (e.g., within ±1-10% variation in size) such that one template may be used for processing the images and obtaining robust overlay results.

[0023] However, due to wafer manufacturing process limitations, the feature size on some wafers may vary greatly (e.g., greater than ±10%, ±30%, etc. variation in size). When the feature size on a wafer has high variation, typical systems (e.g., hardware and software installed on hardware) suffer from constraints because using one template for all features in an image may not achieve accurate or consistent overlay results, especially when the feature size varies field by field, wafer by wafer, lot by lot, etc.

[0024] The disclosed embodiments provide systems and methods that address some or all of these disadvantages by performing guided template matching during metrology. The disclosed embodiments may perform metrology using guided template matching to account for variations in the feature size on a sample. For example, guided template matching may include generating an image of a sample and, per unit cell of the image (e.g., per feature area of a sample in the image), determining a size of a corresponding feature of the sample of the image.

[0025] For each feature of the sample, the method may include generating a template based on the determined size of the corresponding feature and matching the template to the corresponding feature of the image. The matching may include dynamic updating the template with a range of adjustment factors. For example, each adjustment factor in the range may be applied to the template and for each adjustment factor, a similarity score may be calculated between the feature in the template and the corresponding feature in the image.

[0026] In some embodiments, a final template may be generated based on the adjustment factor that results in the highest similarity score. As a result of a template being generated for each feature in an image, metrology accounting for variations in feature size may be performed on a sample.

[0027] 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.

[0028] 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 andB. 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.

[0029] 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.

[0030] 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.

[0031] One or more robotic arms (not shown) in EFEM 106 may transport the wafers to load / lock chamber 102. Load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) which removes gas molecules in load / lock chamber 102 to reach a first pressure below the atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) may transport the wafer from load / lock chamber 102 to main chamber 101. Main chamber 101 is connected to a main chamber vacuum pump system (not shown) which removes gas molecules in main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is subject to inspection by electron beam tool 104. Electron beam tool 104 may be a single-beam system or a multibeam system.

[0032] 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.

[0033] 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.

[0034] 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 magnetic disk, a hard drive, a solid-state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, or any type of storage device. The codes may include an operating system (OS) and one or more application programs (or “apps”) for specific tasks. The memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices coupled via a network.

[0035] 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.

[0036] 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. Multibeam electron beam tool 104 (also referred to herein as apparatus 104) comprises an electron source 201, a Coulomb aperture plate (or “gun aperture plate”) 271, a condenser lens 210, a source conversion unit 220, a primary projection system 230, a motorized stage 209, and a sample holder 207 supported by motorized stage 209 to hold a sample 208 (e.g., a wafer or a photomask) to be inspected. Multi-beam electron beam tool 104 may further comprise a secondary projection system 250 and an electron detection device 240. Primary projection system 230 may comprise an objective lens 231. Electron detection device 240 may comprise a plurality of detection elements 241, 242, and 243. A beam separator 233 and a deflection scanning unit 232 may be positioned inside primary projection system 230.

[0037] 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.

[0038] 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.

[0039] 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 enter the 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.

[0040] Condenser lens 210 is configured to focus primary electron beam 202. Condenser lens 210 may further be configured to adjust electric currents of primary beamlets 211, 212, and 213 downstream of source conversion unit 220 by varying the focusing power of condenser lens 210. Alternatively, the electric currents may be changed by altering the radial sizes of beam-limit apertures within the beamlimit aperture array corresponding to the individual primary beamlets. The electric currents may be changed by both altering the radial sizes of beam-limit apertures and the focusing power of condenser lens 210. Condenser lens 210 may be an adjustable condenser lens that may be configured so that the position of its first principal plane is movable. The adjustable condenser lens may be configured to bemagnetic, 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 antirotation condenser lens, in which the rotation angles do not change when its focusing power and the position of its first principal plane are varied.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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 detectionelement 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.

[0045] 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 combination thereof. The image acquirer may be communicatively coupled to electron detection device 240 of apparatus 104 through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, among others, or a combination thereof. 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 postprocessed images.

[0046] 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.

[0047] 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.

[0048] 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 209to 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.

[0049] 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. In some 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.

[0050] 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.

[0051] 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 savingscanned 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.

[0052] 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 a scanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas that may contain various features of wafer 150. The single image may be stored in storage 130. Imaging may be performed on the basis of imaging frames.

[0053] 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.

[0054] 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.

[0055] Reference is now made to Fig. 3A, which shows an exemplary image 310A of a sample.

[0056] As shown in image 310A, a sample may include via features 320A without great variation in size (e.g., within ±10% variation in size). In embodiments where the feature size does not have great variation (e.g., features that are substantially uniform in size, features that are substantially the same size, etc.), a single template may be used for processing the images and obtaining robust overlay results (e.g., the same template may be used for each feature in the image).

[0057] Reference is now made to Fig. 3B, which shows an exemplary image 310B of a sample, consistent with embodiments of the present disclosure.

[0058] As shown in image 310B, a sample may include contact hole features 320B, which have a higher variation in size (e.g., greater than ±10%, ±30%, etc. variation in size) than via features 320A of Fig. 3A. When the feature size on a wafer has high variation, using one template for all images may not achieve accurate or consistent overlay results, especially when the feature size varies field by field, wafer by wafer, lot by lot, etc. Guided template matching may be used during metrology of contact hole features 320B to account for variations in feature size.

[0059] Reference is now made to Fig. 4, showing exemplary images 410, 420, and 430 associated with guided template matching, consistent with embodiments of the present disclosure.

[0060] In some embodiments, a system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B, hardware and software installed on hardware, etc.) maygenerate a grey level image 410 of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B), which may include features 412 (e.g., contact holes). Image 410a shows a portion of image 410. Images 410 and 410a may include a plurality of unit cells 414, where each unit cell corresponds to an area in the image containing a feature 412. It is appreciated that the box noting unit cell 414 is used for illustrative purposes, and that images 410 and 410a may not include the box.

[0061] In some embodiments, per unit cell 414 of image 410, the system may determine a characteristic (e.g., size) of a corresponding feature 412 of the sample in image 410 by binarizing image 410 with or without image quality enhancement. In some embodiments, binarizing image 410 may include segmenting image 410 into a first color 424 (e.g., a “bright” color) and a second color 426 (e.g., a “dark” color). In some embodiments, features 412 in image 410 may be adjusted to be first color 424 during the binarization process (e.g., segmenting image 410 may include adjusting features 412 such that each of the features includes the first color 424). In some embodiments, the areas of image 410 that do not include features 412 may be adjusted to second color 426 during the binarization. Image 420 shows binarized image 410.

[0062] In some embodiments, nuisance data may be filtered from binarized image 420. Image 430 shows the filtered binarized image 420. Image 430 may include features 432 corresponding to features 412 and bright colored spots 424. Image 430a shows a portion of filtered image 430.

[0063] In some embodiments, determining the size of a corresponding feature 412 may include determining a position of the feature in filtered image 430. In some embodiments, the position of the corresponding feature in filtered image 430 may include coordinates of the feature in the filtered image, where the coordinates correspond to coordinates of a center of feature 432 in image 430 and an intensity value of the feature (e.g., x, y, z coordinates, where x and y correspond to the position of the feature center in the x and y directions of image 430 and z corresponds to an intensity value of the feature).

[0064] In some embodiments, determining the size of the corresponding feature may include determining a length 434 of the feature along a first axis and determining a length 436 of the feature along a second axis. In some embodiments, determining the size of the corresponding feature may include contour extraction and contour fitting. For example, the contour of a contact hole may be extracted based on an edge finding algorithm and the contour can be fitted into an ellipse.

[0065] Reference is now made to Fig. 5, showing an exemplary template images 510, consistent with embodiments of the present disclosure.

[0066] In some embodiments, the system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B, hardware and software installed on hardware, etc.) may generate template images 510 including a template 512 of each feature (e.g., features 412 of Fig. 4) of a sample based on the determined characteristic (e.g., size) (e.g., determined based on filtered image 430) of the corresponding feature. It is appreciated that in some embodiments, each template 512 may be a separate template image (e.g., four template images may be shown in Fig. 5).

[0067] In some embodiments, generating the template 512 of each feature of the sample may include generating template images 510 of the features, where each feature in the template images 510 includes the determined size of the corresponding feature (e.g., the size of each template 512 corresponds to the determined size of the feature based on filtered image 430 of Fig. 4).

[0068] Reference is now made to Fig. 6, showing exemplary diagrams 610 and 620 and exemplary graph 630 associated with guided template matching, consistent with embodiments of the present disclosure.

[0069] Diagram 610 shows a region 612 corresponding to the possible adjustment range of the radius (e.g., a range from outer radius 612o to inner radius 612i) of a template of a feature (e.g., template 512 of Fig. 5). Dashed circle 614 corresponds to a radius offset of the feature as a result of an applied adjustment factor. Dashed circle 616 corresponds to a radius offset of the feature as a result of another applied adjustment factor.

[0070] In some embodiments, the system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B, hardware and software installed on hardware, etc.) may match or compare the template (e.g., template 512 of Fig. 5) to the corresponding feature (e.g., feature 412 of Fig. 4) of the image (e.g., image 410 of Fig. 4), where matching the template may include dynamic updating (e.g., adjusting) the template with a range of adjustment factors. In some embodiments, the dynamic updating the template may include applying each adjustment factor of the range of adjustment factors to the template. For example, for each applied adjustment factor, the system may calculate a similarity score between a feature in the template and a corresponding feature in the image. For example, calculating the similarity score may include calculating a normalized crosscorrelation between the feature in the template and the corresponding feature in the image.

[0071] In some embodiments, the adjustment factor may be a radius offset of a feature. For example, applying the adjustment factor to the template may include adjusting the template such that a radius of the template of a feature is offset from an initial position of the template (e.g., a radius offset may be an offset of the radius by ±1, 2, 3 pixels, etc.). In some embodiments, a radius offset may extend to geometry size parameters such as a length of the template, a width of the template, etc.

[0072] In some embodiments, the adjustment factor may be a rotational offset of a feature. For example, applying the adjustment factor to the template may include adjusting the template such that an orientation or rotation of the template of a feature is offset from an initial position of the template (e.g., a rotational offset may be an offset of the orientation by ±0.1, 0.2, 0.5 degrees, etc.).

[0073] Diagram 620 shows a feature 622 (e.g., feature 512 of Fig. 5) in an image (e.g., image 410 of Fig. 4), where the image includes pixels 624. In some embodiments, the image may be processed (see, e.g., Fig. 4) and a template of each feature (e.g., template 512 of Fig. 5) may be generated. In some embodiments, calculating the similarity score may include calculating a normalized cross-correlation between the template and the feature in the image in the memory (e.g., a memory associated with controller 109 of Figs. 1, 2A, or 2B) during algorithm execution.

[0074] Graph 630 shows curves 638, where axis 632 corresponds to radius offset values and axis 634 corresponds to similarity scores. Each curve of curves 638 may correspond to a different feature template. A maximum similarity score 636 may correspond to a radius offset represented by dashed circle 614. In some embodiments, the system may generate a final template of each feature based on the matching between the template and the corresponding feature of the image. For example, the system may generate a final template for each feature at a position corresponding to a maximum similarity score. For example, the system may generate the final template based on the applied adjustment factor that results in the highest similarity score between the image and the filtered template image. For example, the final template may be generated by applying the adjustment factor corresponding to dashed circle 614.

[0075] It is appreciated that the above description for a radius offset adjustment factor is also applicable to a rotational offset adjustment factor (e.g., similarity scores may be calculated by applying a rotational offset to the template, etc.).

[0076] In some embodiments, the system may determine one or more characteristics (e.g., critical dimension of features, overlay data, defect data, etc.) of the sample using the final template.

[0077] In conventional template matching algorithms, where the same sized template is used for all unit cells in an image with large feature size variation, the template size requires fine tuning. For example, the template size needs to be fine-tuned to achieve a set-get-overlay slope and R2near 1 by processing a set of set-overlay images. Since the fine-tuned template size is based on existing SEM images, the fine-tuned template size may not work for future images due to wafer process variations. In contrast, guided template matching and dynamic guided template matching (e.g., as described in Figs. 4-6) do not require fine tuning of the template size to achieve a set-get-overlay slope and R2near 1. Advantageously, because guided template matching and dynamic guided template matching do not require fine tuning of the template size to achieve a set-get-overlay slope and R2near 1, these methods provide template matching that is both time efficient and robust compared to conventional methods.

[0078] Guided template matching is similar to dynamic guided template matching, the difference being that guided template matching does not include updating templates with a range of adjustment factors.

[0079] Advantageously, compared to conventional template matching algorithms, guided template matching and dynamic guided template matching algorithms achieve better overlay uniformity and can accommodate images with large feature size variation. Overlay uniformity may be defined as the variance of all unit cells' overlay values within a defined region of interest. It is the key indicator of overlay consistency within a region of interest.

[0080] For example, a region of interest may be defined as a lum by lum square area in an image. If there are 100 unit cells total (also called 100 features) in this area, 100 overlay values may be obtained. That is, one overlay value per each unit cell may be obtained. The variance of those 100 overlay values may be calculated, and the variance is called overlay uniformity.

[0081] Therefore, guided template matching and dynamic guided template matching algorithms provide more accurate and robust positions of features than conventional template matching algorithms.

[0082] Reference is now made to Fig. 7, an exemplary process 700 for guided template matching, consistent with embodiments of the present disclosure. It is understood that process 700 may be performed in a multi-beam environment (e.g., electron beam tool 104 of Fig. 2A) or in a single beam environment (e.g., electron beam tool 104 of Fig. 2B).

[0083] At step 702, a system (e.g., electron beam tool 104 of Fig. 2A, electron beam tool 104 of Fig. 2B) may generate an image (e.g., image 410 of Fig. 4) of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B). In some embodiments, the image may be a grey level image that includes features (e.g., feature 412 of Fig. 4). In some embodiments, the image may include a plurality of unit cells (e.g., unit cells 414 of Fig. 4), where each unit cell corresponds to an area in the image containing a feature.

[0084] At step 704, per unit cell of the image, the system may determine a size of a corresponding feature of the sample of the image.

[0085] In some embodiments, per unit cell of the image, the system may determine a characteristic (e.g., size) of a corresponding feature of the sample in the image by binarizing the image with or without image quality enhancement. In some embodiments, binarizing the image may include segmenting the image into a first color (e.g., color 424 of Fig. 4) (e.g., a “bright” color) and a second color (e.g., color 426 of Fig. 4) (e.g., a “dark” color). In some embodiments, features in the image may be adjusted to be the first color during the binarization process (e.g., segmenting the image may include adjusting the features such that each of the features includes the first color). In some embodiments, the areas of the image that do not include features may be adjusted to the second color during the binarization.

[0086] In some embodiments, nuisance data may be filtered from the binarized image (e.g., binarized image 420 of Fig. 4). In some embodiments, determining the size of a corresponding feature may include determining a position of the feature in the filtered image (e.g., filtered image 430 of Fig. 4). In some embodiments, the position of the corresponding feature (e.g., feature 432 of Fig. 4) in the filtered image may include coordinates of the feature in the filtered image, where the coordinates correspond to coordinates of a center of the feature in the filtered image and an intensity value of the feature (e.g., x, y, z coordinates, where x and y correspond to the position of the feature center in the x and y directions of the filtered image and z corresponds to an intensity value of the feature).

[0087] In some embodiments, determining the size of the corresponding feature may include determining a length (e.g., length 434 of Fig. 4) of the feature along a first axis and determining a length (e.g., length 436 of Fig. 4) of the feature along a second axis. In some embodiments, determining the size of the corresponding feature may include contour extraction and contour fitting. For example, the contour of a contact hole may be extracted based on an edge finding algorithm and the contour can be fitted into an ellipse.

[0088] At step 706, the system may generate a template (e.g., template 512 of Fig. 5) of each feature of the sample based on the determined size of the corresponding feature.

[0089] In some embodiments, generating the template of each feature of the sample may include generating a template image (e.g., template images 510 of Fig. 5) of the features, where each feature in the template image includes the determined size of the corresponding feature (e.g., the size of each template corresponds to the determined size of the feature based on the filtered image).

[0090] At step 708, the system may match or compare the template to the corresponding feature of the image, where matching the template may include dynamic updating (e.g., adjusting) the template with a range of adjustment factors.

[0091] In some embodiments, the dynamic updating the template may include applying each adjustment factor of the range of adjustment factors to the template. In some embodiments, the adjustment factor may be a radius offset of a feature. For example, applying the adjustment factor to the template may include adjusting the template such that a radius of the template of a feature is offset from an initial position of the template (e.g., a radius offset may be an offset of the radius by ±1, 2, 3 pixels, etc.). In some embodiments, a radius offset may extend to geometry size parameters such as a length of the template, a width of the template, etc.

[0092] In some embodiments, the adjustment factor may be a rotational offset of a feature. For example, applying the adjustment factor to the template may include adjusting the template such that an orientation or rotation of the template of a feature is offset from an initial position of the template (e.g., a rotational offset may be an offset of the orientation by ±0.1, 0.2, 0.5 degrees, etc.).

[0093] In some embodiments, for each applied adjustment factor, the system may calculate a similarity score between a feature in the template and a corresponding feature in the image. For example, calculating the similarity score may include calculating a difference between the feature in the template and the corresponding feature in the image.

[0094] In some embodiments, calculating the similarity score may include calculating a normalized cross-correlation between the template and the feature in the image.

[0095] At step 710, the system may generate a final template based on the matching. In some embodiments, the system may generate a final template of each feature based on the matching between the template and the corresponding feature of the image. For example, the system may generate a final template for each feature at a position corresponding to a maximum similarity score. For example, the system may generate the final template based on the applied adjustment factor that results in the highest similarity score between the image and the template image.

[0096] At step 712, the system may calculate or output each feature position based on the final template.

[0097] It is appreciated that the above description for a radius offset adjustment factor is also applicable to a rotational offset adjustment factor (e.g., similarity scores may be calculated by applying a rotational offset to the template, etc.).

[0098] A non-transitory computer readable medium may be provided that stores instructions for a processor of a controller (e.g., controller 109 of Fig. 1) for controlling the electron beam tool or other systems of other systems and servers, or components thereof, consistent with embodiments in thepresent 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. In some embodiments, the non-transitory computer readable medium may be provided that stores instructions for a processor to perform the steps of process 700. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a Compact Disc Read Only Memory (CD- ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read Only Memory (PROM), and Erasable Programmable Read Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non-Volatile Random Access Memory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same.

[0099] The embodiments may further be described using the following clauses:1. A method of guided template matching, comprising: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.2. The method of clause 1, wherein determining the size of the corresponding feature comprises binarizing the image.3. The method of clause 2, wherein binarizing the image comprises segmenting the image into a first color and a second color.4. The method of clause 3, wherein the first color corresponds to the features of the image.5. The method of clause 4, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.6. The method of any one of clauses 2-5, further comprising filtering nuisance from the binarized image.7. The method of clause 6, wherein determining the size of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.8. The method of clause 7, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.9. The method of clause 8, wherein the coordinates of the feature correspond to coordinates of a center of the feature.10. The method of any one of clauses 8-9, wherein the coordinates of the feature comprise an intensity value of the feature.11. The method of any one of clauses 6-10, wherein determining the size of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.12. The method of any one of clauses 1-11, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined size of the corresponding feature.13. The method of any one of clauses 1-12, wherein the dynamic updating the template comprises applying each adjustment factor of the range of adjustment factors to the template.14. The method of clause 13, further comprising: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.15. The method of clause 14, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.16. The method of any one of clauses 14-15, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.17. The method of any one of clauses 1-16, wherein the adjustment factor comprises a radius offset of a feature.18. The method of clause 17, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.19. The method of any one of clauses 1-16, wherein the adjustment factor comprises a rotational offset of a feature.20. The method of clause 19, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.21. The method of any one of clauses 1-20, further comprising determining one or more characteristics of the sample using the final template.22. A method of guided template matching, comprising: determining a characteristic of each feature of a plurality of features in an image; generating a template of each feature based on the determined characteristic of the corresponding feature; comparing the template to the corresponding feature of the image, wherein comparing the template comprises adjusting the template using a range of adjustment factors; generating a final template using the comparison; and outputting a position of each feature based on the final template.23. The method of clause 22, wherein determining the characteristic of the corresponding feature comprises binarizing the image.24. The method of clause 23, wherein binarizing the image comprises segmenting the image into a first color and a second color.25. The method of clause 24, wherein the first color corresponds to the features of the image.26. The method of clause 25, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.27. The method of any one of clauses 23-26, further comprising filtering nuisance from the binarized image.28. The method of clause 27, wherein determining the characteristic of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.29. The method of clause 28, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.30. The method of clause 29, wherein the coordinates of the feature correspond to coordinates of a center of the feature.31. The method of any one of clauses 29-30, wherein the coordinates of the feature comprise an intensity value of the feature.32. The method of any one of clauses 27-31, wherein determining the characteristic of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.33. The method of any one of clauses 22-32, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined characteristic of the corresponding feature.34. The method of any one of clauses 22-33, wherein adjusting the template comprises applying each adjustment factor of the range of adjustment factors to the template.35. The method of clause 34, further comprising: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.36. The method of clause 35, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.37. The method of any one of clauses 35-36, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.38. The method of any one of clauses 22-37, wherein the adjustment factor comprises a radius offset of a feature.39. The method of clause 38, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.40. The method of any one of clauses 22-37, wherein the adjustment factor comprises a rotational offset of a feature.41. The method of clause 40, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.42. The method of any one of clauses 22-41 , further comprising determining one or more characteristics of the sample using the final template.43. A system for guided template matching, 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: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.44. The system of clause 43, wherein determining the size of the corresponding feature comprises binarizing the image.45. The system of clause 44, wherein binarizing the image comprises segmenting the image into a first color and a second color.46. The system of clause 45, wherein the first color corresponds to the features of the image.47. The system of clause 46, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.48. The system of any one of clauses 44-47, wherein the operations further comprise filtering nuisance from the binarized image.49. The system of clause 48, wherein determining the size of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.50. The system of clause 49, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.51. The system of clause 50, wherein the coordinates of the feature correspond to coordinates of a center of the feature.52. The system of any one of clauses 50-51, wherein the coordinates of the feature comprise an intensity value of the feature.53. The system of any one of clauses 48-52, wherein determining the size of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.54. The system of any one of clauses 43-53, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined size of the corresponding feature.55. The system of any one of clauses 43-54, wherein the dynamic updating the template comprises applying each adjustment factor of the range of adjustment factors to the template.56. The system of clause 55, wherein the operations further comprise: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.57. The system of clause 56, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.58. The system of any one of clauses 56-57, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.59. The system of any one of clauses 43-58, wherein the adjustment factor comprises a radius offset of a feature.60. The system of clause 59, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.61. The system of any one of clauses 43-58, wherein the adjustment factor comprises a rotational offset of a feature.62. The system of clause 61, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.63. The system of any one of clauses 43-62, wherein the operations further comprise determining one or more characteristics of the sample using the final template.64. A system for guided template matching, 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: determining a characteristic of each feature of a plurality of features in an image; generating a template of each feature based on the determined characteristic of the corresponding feature; comparing the template to the corresponding feature of the image, wherein comparing the template comprises adjusting the template using a range of adjustment factors; generating a final template using the comparison; and outputting a position of each feature based on the final template.65. The system of clause 64, wherein determining the characteristic of the corresponding feature comprises binarizing the image.66. The system of clause 65, wherein binarizing the image comprises segmenting the image into a first color and a second color.67. The system of clause 66, wherein the first color corresponds to the features of the image.68. The system of clause 67, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.69. The system of any one of clauses 65-68, wherein the operations further comprise filtering nuisance from the binarized image.70. The system of clause 69, wherein determining the characteristic of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.71. The system of clause 70, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.72. The system of clause 71, wherein the coordinates of the feature correspond to coordinates of a center of the feature.73. The system of any one of clauses 71-72, wherein the coordinates of the feature comprise an intensity value of the feature.74. The system of any one of clauses 69-73, wherein determining the characteristic of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.75. The system of any one of clauses 64-74, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined characteristic of the corresponding feature.76. The method of any one of clauses 64-75, wherein adjusting the template comprises applying each adjustment factor of the range of adjustment factors to the template.77. The system of clause 76, wherein the operations further comprise: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.78. The system of clause 77, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.79. The system of any one of clauses 77-78, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.80. The system of any one of clauses 64-79, wherein the adjustment factor comprises a radius offset of a feature.81. The system of clause 80, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.82. The system of any one of clauses 64-79, wherein the adjustment factor comprises a rotational offset of a feature.83. The system of clause 82, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.84. The system of any one of clauses 64-83, wherein the operations further comprise determining one or more characteristics of the sample using the final template.85. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method for guided template matching, the method comprising: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.86. The non-transitory computer readable medium of clause 85, wherein determining the size of the corresponding feature comprises binarizing the image.87. The non-transitory computer readable medium of clause 86, wherein binarizing the image comprises segmenting the image into a first color and a second color.88. The non-transitory computer readable medium of clause 87, wherein the first color corresponds to the features of the image.89. The non-transitory computer readable medium of clause 88, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.90. The non-transitory computer readable medium of any one of clauses 86-89, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform filtering nuisance from the binarized image.91. The non-transitory computer readable medium of clause 90, wherein determining the size of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.92. The non-transitory computer readable medium of clause 91, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.93. The non-transitory computer readable medium of clause 92, wherein the coordinates of the feature correspond to coordinates of a center of the feature.94. The non-transitory computer readable medium of any one of clauses 92-93, wherein the coordinates of the feature comprise an intensity value of the feature.95. The non-transitory computer readable medium of any one of clauses 90-94, wherein determining the size of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.96. The non-transitory computer readable medium of any one of clauses 85-95, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined size of the corresponding feature.97. The non-transitory computer readable medium of any one of clauses 85-96, wherein the dynamic updating the template comprises applying each adjustment factor of the range of adjustment factors to the template.98. The non-transitory computer readable medium of clause 97, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.99. The non-transitory computer readable medium of clause 98, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.100. The non-transitory computer readable medium of any one of clauses 98-99, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.101. The non-transitory computer readable medium of any one of clauses 85-100, wherein the adjustment factor comprises a radius offset of a feature.102. The non-transitory computer readable medium of clause 101, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.103. The non-transitory computer readable medium of any one of clauses 85-100, wherein the adjustment factor comprises a rotational offset of a feature.104. The non-transitory computer readable medium of clause 103, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.105. The non-transitory computer readable medium of any one of clauses 85-104, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform determining one or more characteristics of the sample using the final template.106. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method of guided template matching, the method comprising: determining a characteristic of each feature of a plurality of features in an image;generating a template of each feature based on the determined characteristic of the corresponding feature; comparing the template to the corresponding feature of the image, wherein comparing the template comprises adjusting the template using a range of adjustment factors; generating a final template using the comparison; and outputting a position of each feature based on the final template.107. The non-transitory computer readable medium of clause 106, wherein determining the characteristic of the corresponding feature comprises binarizing the image.108. The non-transitory computer readable medium of clause 107, wherein binarizing the image comprises segmenting the image into a first color and a second color.109. The non-transitory computer readable medium of clause 108, wherein the first color corresponds to the features of the image.110. The non-transitory computer readable medium of clause 109, wherein segmenting the image comprises adjusting the features such that the each of the features comprises the first color.111. The non-transitory computer readable medium of any one of clauses 107-110, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform filtering nuisance from the binarized image.112. The non-transitory computer readable medium of clause 111, wherein determining the characteristic of the corresponding feature comprises determining a position of the corresponding feature in the filtered image.113. The non-transitory computer readable medium of clause 112, wherein the position of the corresponding feature comprises coordinates of the feature in the filtered image.114. The non-transitory computer readable medium of clause 113, wherein the coordinates of the feature correspond to coordinates of a center of the feature.115. The non-transitory computer readable medium of any one of clauses 113-114, wherein the coordinates of the feature comprise an intensity value of the feature.116. The non-transitory computer readable medium of any one of clauses 111-115, wherein determining the characteristic of the corresponding feature comprises determining a length of the feature along a first axis and a length of the feature along a second axis.117. The non-transitory computer readable medium of any one of clauses 106-116, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined characteristic of the corresponding feature.118. The non-transitory computer readable medium of any one of clauses 106-117, wherein adjusting the template comprises applying each adjustment factor of the range of adjustment factors to the template.119. The non-transitory computer readable medium of clause 118, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.120. The non-transitory computer readable medium of clause 119, wherein calculating the similarity score comprises calculating a normalized cross -correlation between the feature in the template and the corresponding feature in the image.121. The non-transitory computer readable medium of any one of clauses 119-120, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.122. The non-transitory computer readable medium of any one of clauses 106-121, wherein the adjustment factor comprises a radius offset of a feature.123. The non-transitory computer readable medium of clause 122, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.124. The non-transitory computer readable medium of any one of clauses 106-121, wherein the adjustment factor comprises a rotational offset of a feature.125. The non-transitory computer readable medium of clause 124, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.126. The non-transitory computer readable medium of any one of clauses 106-125, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform determining one or more characteristics of the sample using the final template.

[0100] 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 a method for guided template matching, the method comprising: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.

2. The non-transitory computer readable medium of claim 1, wherein determining the size of the corresponding feature comprises binarizing the image.

3. The non-transitory computer readable medium of claim 1, wherein generating the template of each feature of the sample comprises generating a template image of the features, wherein each feature in the template image comprises the determined size of the corresponding feature.

4. The non-transitory computer readable medium of claim 1, wherein the dynamic updating the template comprises applying each adjustment factor of the range of adjustment factors to the template.

5. The non-transitory computer readable medium of claim 4, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform: for each applied adjustment factor, calculating a similarity score between a feature in the template and a corresponding feature in the image.

6. The non-transitory computer readable medium of claim 5, wherein calculating the similarity score comprises calculating a normalized cross-correlation between the feature in the template and the corresponding feature in the image.

7. The non-transitory computer readable medium of claim 5, wherein the final template comprises each feature at a position corresponding to a maximum similarity score.

8. The non-transitory computer readable medium of claim 1, wherein the adjustment factor comprises a radius offset of a feature.

9. The non-transitory computer readable medium of claim 8, wherein the radius offset of the feature corresponds to an offset of a radius of the feature in the template from an initial position of the feature in the template.

10. The non-transitory computer readable medium of claim 1, wherein the adjustment factor comprises a rotational offset of a feature.

11. The non-transitory computer readable medium of claim 10, wherein the rotational offset of the feature corresponds to an offset of a rotation of the feature in the template from an initial position of the feature in the template.

12. The non-transitory computer readable medium of claim 1, wherein the set of instructions that is executable by at least one processor of a computing device to cause the computing device to further perform determining one or more characteristics of the sample using the final template.

13. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method of guided template matching, the method comprising: determining a characteristic of each feature of a plurality of features in an image; generating a template of each feature based on the determined characteristic of the corresponding feature; comparing the template to the corresponding feature of the image, wherein comparing the template comprises adjusting the template using a range of adjustment factors; generating a final template using the comparison; and outputting a position of each feature based on the final template.

14. A method of guided template matching, comprising: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature;matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.

15. A system for guided template matching, 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: generating an image of a sample; per unit cell of the image, determining a size of a corresponding feature of the sample of the image; generating a template of each feature of the sample based on the determined size of the corresponding feature; matching the template to the corresponding feature of the image using a dynamic updating of the template with a range of adjustment factors; generating a final template based on the matching; and calculating a position of each feature based on the final template.

Images