Systems and methods for signal-based defect classification in transient inspection

Abstract

Systems, methods, and non-transitory computer readable mediums for signal-based defect classification in transient inspection include obtaining a first image of a sample; locating a location of interest using the first image; obtaining a plurality of second images of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest; tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images; and determining, using the plotting, whether a defect has occurred at the location of interest.

Description

SYSTEMS AND METHODS FOR SIGNAL-BASED DEFECT CLASSIFICATION INTRANSIENT INSPECTIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of PCT application PCT / CN2023 / 140723 which was filed on 21 December 2023 and which is incorporated herein in its entirety by reference.FIELD

[0002] The description herein relates to the field of inspection and charged particle systems, and more particularly to methods for signal-based defect classification in transient inspection.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, methods, and non-transitory computer readable mediums for signal-based defect classification in transient inspection.Embodiments may include obtaining a first image of a sample; locating a location of interest using the first image; obtaining a plurality of second images of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest; tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on theplurality of second images; and determining, using the plotting, whether a defect has occurred at the location of interest.

[0006] Embodiments may include locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.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. 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.

[0011] Fig. 4 is a schematic diagram illustrating an exemplary a voltage contrast response of a wafer, consistent with embodiments of the present disclosure.

[0012] Fig. 5 shows exemplary images of a sample during transient inspection.

[0013] Fig. 6 shows exemplary images from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0014] Fig. 7 shows an exemplary image from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0015] Fig. 8 shows exemplary images from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0016] Figs. 9A, 9B, 9C, and 9D show exemplary graphs of plotted relationships between GLV values and time, consistent with embodiments of the present disclosure.

[0017] Fig. 10 shows an exemplary process for classifying defects on a sample, consistent with embodiments of the present disclosure.DETAILED DESCRIPTION

[0018] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplaryembodiments 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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 thestructures. 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.

[0023] Typical voltage contrast (VC) inspections obtain a SEM image within a field of view (FOV), which contains features of interests on a sample. In typical VC inspections, during acquisition of SEM images, features under inspection are either positively or negatively charged by scanning a sample with charged particle beams. The charging condition is controlled by properly setting the scan time and beam current of the primary electron beam. The charging condition may also be controlled by the landing energy of primary electron beamlets, which may control the yield of secondary electrons, resulting in positive or negative charging of features on a wafer. A typical system may identify defects at features based on the grey levels of those features.

[0024] Typical inspection methods focus on using image -based die-to die (D2D) or die-to-database (D2DB) inspection, which target the spatial difference on a sample to capture the defect of interest (DOI). Some typical inspection methods include transient inspection based on existing D2D inspection modules.

[0025] Typical inspection methods, however, suffer from constraints. For example, image-based D2D and D2DB inspection compare SEM images between different samples or dies without a timedependent (e.g., temporal) aspect in the data. Transient inspection involves comparing images of the same sample or die at different times. However, in typical D2D, D2DB, and transient inspection methods, the defect portion of an image is small compared to the area of the entire image (e.g., in typical methods, only one plug may have a defect). As a result of the entire image being inspected when the DOI is only a small portion of the image, typical inspection methods need a minimum threshold value to identify defects and it may be difficult to differentiate between DOIs and non- DOIs.

[0026] Moreover, while typical transient inspection methods locate points of interest, it does not typically extract data to conduct analyses of the temporal effects on a sample at points of interest. In typical transient inspection methods, only one to two images may be used to show or compare minimum and maximum grey levels.

[0027] The disclosed embodiments provide systems and methods that address some or all of these disadvantages by performing signal-based defect classification during transient inspection. The disclosed embodiments extract signals from a targeted area of a sample over time to classify defects. For example, methods may include locating a location of interest (e.g., DOI) on a first image; obtaining a plurality of second images of the sample; tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images; and determining or classifying a defect based on the plotting. Advantageously, the disclosed embodiments involve the analysis of smaller, targeted areas of an image based on locations of interest. As a result, the disclosedembodiments include inspection methods that are more sensitive to time-dependent defects on a sample.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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 shownin 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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 anelectron 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.

[0038] 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.

[0039] 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.

[0040] 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 processingfunctions. Controller 109 may also generate various control signals to govern operations of the charged particle beam inspection system.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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 post-processed images.

[0047] 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 109may be configured to perform image processing steps with the multiple images of the same location of sample 208.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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 indicatesthe 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.

[0057] As shown in the graph of Fig. 3, when the landing energy of the primary electrons is within a range from Ei to Ez, 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).

[0058] When the landing energy is lower than Ei or higher than Ez, 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) may generate a brighter voltage contrast image of a device structure with a more negative surface potential a detection device (e.g., detection device 240 of Fig. 2) may receive more secondary electrons (see Fig. 4).

[0059] 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.

[0060] 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.

[0061] 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 byinsulating material 450 such that a thin insulator structure 470 (e.g., thin oxide) exists between device structure 440 and substrate 410.

[0062] 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 Ez (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.

[0063] 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.

[0064] 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.

[0065] 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 electricalsurface 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.

[0066] 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, CoSiz, CozSi, Co sSi, etc.) layer between a tungsten plug and a source or drain area of a field-effect transistor (FET).

[0067] 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.

[0068] Reference is now made to Fig. 5, which shows exemplary images 510 and 520 of a sample during transient inspection.

[0069] Transient inspection involves comparing images of the same sample or die at different times. However, in typical transient inspection methods, the defect portion of an image is small compared to area of the entire image. For example, image 510 shows a plug at a first grey level 512 at a first time while image 520 shows the plug at a second grey level 522 at a second time. During transient inspection, the entire images of the sample (e.g., an area of 512x512 to 1024x1042 pixels) or large cropped areas of an image (e.g., 64x64 pixels) may be compared and inspected, even when the area of interest with a defect may only be a small portion (e.g., 5x5 pixels as shown in images 510 and 520, 2x2 pixels, 3x3 pixels, etc.) of the image.

[0070] As a result, typical transient inspection methods need a minimum threshold value to identify defects and it may be difficult to differentiate between defects of interest (DOIs) and non-DOIs. Moreover, while typical transient inspection methods locate points of interest, it does not typically extract data to conduct analyses of the temporal effects on a sample at points of interest. In typical transient inspection methods, only one to two images (e.g., images 510 and 520) may be used to show or compare minimum and maximum grey levels.

[0071] Reference is now made to Fig. 6, showing exemplary images from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0072] In some embodiments, an inspection system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B) may obtain an image 610 of a sample at time zero (e.g., a time before charging of the sample takes effect) and obtain a first image 620 of the sample. In some embodiments, image 620 may include features 622 and 624 of sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B), where each feature of the sample may have a corresponding grey level in image 620.

[0073] In some embodiments, first image 620 may be obtained to assist with alignment of the sample and the inspection tool to determine on which areas of a sample inspection should be focused for defect detection. It is understood that in practice, first image 620 may be larger or smaller than depicted in Fig. 6. It is also understood that first image 620 is not limited to the arrangement of features shown in Fig. 6 and that in practice, images may include various arrangements of features.

[0074] Reference is now made to Fig. 7, showing an exemplary image from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0075] In some embodiments, image 700 (e.g., first image 620 of Fig. 6) may include various features (e.g., features 622 or 624 of Fig. 6) of a sample. In some embodiments, an inspection system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B) may locate one or more locations of interest (e.g., defects of interest (DOIs)) 710, 712, 714, 716, 718, and 720 on image 700, where each location of interest may correspond to a feature of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B) where a defect may occur.

[0076] In some embodiments, the locations of interest may be located based on layout data related to the sample. A layout design may be stored in a layout file for a wafer design. The 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. The 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 information stored in a binary file format representing planar geometric shapes, text, and other information related to the wafer design.

[0077] In some embodiments, the locations of interest may be located based on image-based binning using image 700.

[0078] It is understood that in practice, image 700 may be larger or smaller than depicted in Fig. 7. It is also understood that image 700 is not limited to the arrangement of features shown in Fig. 7 and that in practice, images may include various arrangements of features.

[0079] Reference is now made to Fig. 8, showing exemplary images from a signal-based defect classification in transient inspection method, consistent with embodiments of the present disclosure.

[0080] In some embodiments, an inspection system (e.g., EBI system 100 of Fig. 1, electron beam tool 104 of Fig. 2A, electron beam tool 100B of Fig. 2B) may obtain a plurality of second images 810, 820, 830, and 840 of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B), where each second image may correspond to a different point in time 850. In some embodiments, images 810, 820, 830, and 840 may be obtained after locating locations of interest on the first image (e.g., image 700 of Fig. 7). In some embodiments, each of images 810, 820, 830, and 840 may show a location of interest (e.g., defect of interest 710, 712, 714, 716, 718, or 720 of Fig. 7) corresponding to a feature of the sample where the system predicts a defect may occur. Each of images 810, 820, 830, and 840 mayshow location of interest 812, 822, 832, and 842, respectively, with variation of grey levels (GLV) values over time 850.

[0081] In some embodiments, the inspection system may track the GLV for locations of interest based on images 810, 820, 830, and 840. In some embodiments, the inspection system may plot a relationship between the GLV 852 and a time 850 at which the GLV was captured based on images 810, 820, 830, and 840. As shown in graph 854, the relationship between the GLV 852 and time 850 may be plotted for a location of interest 812, 822, 832, and 842 in images 810, 820, 830, and 840, respectively. In some embodiments, the inspection system may determine one or more defects based on the plotting, as described in more detail below.

[0082] While Fig. 8 shows images and plotting for a single location of interest, it is understood that embodiments of the present disclosure are not limited to analysis of a single location of interest and that the methods described with respect to Fig. 8 may apply to a plurality of locations of interest (e.g., defect of interest 710, 712, 714, 716, 718, or 720 of Fig. 7). For example, a plurality of curves showing the relationship between the GLV and time may be generated for a plurality of locations of interest (e.g., where each curve corresponds to a different location of interest). For each location of interest, a plurality of images may be generated for a certain time span.

[0083] It is understood that in practice, images 810, 820, 830, and 840 may be larger or smaller than depicted in Fig. 8. It is also understood that images 810, 820, 830, and 840 are not limited to the arrangement of features shown in Fig. 8 and that in practice, images may include various arrangements of features.

[0084] Reference is now made to Figs. 9A, 9B, 9C, and 9D, showing exemplary graphs of plotted relationships between GLV values and time, consistent with embodiments of the present disclosure.

[0085] Fig. 9A shows a graph 900A of a relationship 910A between the GLV 902A and time 904A. Relationship 910A may correspond to a defect of interest (e.g., defect of interest 710, 712, 714, 716, 718, or 720 of Fig. 7) on an image (e.g., images 810, 820, 830, and 840 of Fig. 8) of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B). Graph 900A may include a characteristic 912A, which may correspond to a GLV range (e.g., the difference between the maximum GLV value and the minimum GLV value) of the plotting of relationship 910A.

[0086] Fig. 9B shows a graph 900B of a relationship 910B between the GLV 902B and time 904B. Relationship 910B may correspond to a defect of interest (e.g., defect of interest 710, 712, 714, 716, 718, or 720 of Fig. 7) on an image (e.g., images 810, 820, 830, and 840 of Fig. 8) of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B). Graph 900B may include a characteristic 912B, which may correspond to a slope of the plotting of relationship 910B. In some embodiments, the slope of the plotting may indicate any one of a charging rate or a discharging rate over time associated with the sample.

[0087] Fig. 9C shows a graph 900C of a relationship 910C between the GLV 902C and time 904C. Relationship 910C may correspond to a defect of interest (e.g., defect of interest 710, 712, 714, 716,718, or 720 of Fig. 7) on an image (e.g., images 810, 820, 830, and 840 of Fig. 8) of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B). Graph 900C may include a characteristic 912C, which may correspond to a local minimum (or a local maximum in some embodiments) of the plotting of relationship 910A. In some embodiments, the local minimum or the local maximum may indicate an end of a charging process or a discharging process associated with the sample.

[0088] Fig. 9D shows a graph 900D of a relationship 910D between the GLV 902D and time 904D. Relationship 910D may correspond to a defect of interest (e.g., defect of interest 710, 712, 714, 716, 718, or 720 of Fig. 7) on an image (e.g., images 810, 820, 830, and 840 of Fig. 8) of a sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B). Graph 900D may include a characteristic 912D, which may correspond to a difference between an average GLV local minimum 914D (or local maximum) of the plurality of second images and a local minimum 916D (or a local maximum) of the plotting of relationship 910D.

[0089] In some embodiments, the inspection system may determine one or more defects by determining a characteristic (e.g., characteristics 912A-912D of Figs. 9A-9D) of the plotting. For example, based on reference value and the determined characteristic of the plotting, the inspection system may determine whether a defect exists in a location of interest in a sample. For example, the inspection system may have a reference GLV range and filter out plottings that do not satisfy the reference GLV range. The inspection system may analyze the plottings that do satisfy the reference GLV range (e.g., if GLV range 912A of Fig. 9A satisfies a reference range, the inspection system may analyze graph 900A to determine whether a defect associated with the location of interest exists in the sample).

[0090] In some embodiments, the inspection system may classify the determined defect based on the determined characteristic of the plotting.

[0091] In some embodiments, the inspection system may plot relationships between the GLV for each of a plurality of second locations of interest and a time at which the GLV was captured, determine a characteristic of each of the plottings, and compare the characteristic of the plotting associated with a first location of interest to the characteristics of the plottings associated with the plurality of second locations of interest.

[0092] The above described method with respect to Fig. 9A may also apply to Figs. 9B-9D. For example, the inspection system may have a reference GLV slope and filter out plottings that do not satisfy the reference GLV slope. The inspection system may analyze the plottings that do satisfy the reference GLV slope (e.g., if GLV slope 912B of Fig. 9B satisfies a reference slope, the inspection system may analyze graph 900B to determine whether a defect associated with the location of interest exists in the sample).

[0093] In some embodiments, the inspection system may have a reference GLV local minimum or local maximum and filter out plottings that do not satisfy the reference GLV local minimum or local maximum. The inspection system may analyze the plottings that do satisfy the reference GLV localminimum or local maximum (e.g., if GLV local minimum 912C of Fig. 9C satisfies a reference local minimum, the inspection system may analyze graph 900C to determine whether a defect associated with the location of interest exists in the sample).

[0094] In some embodiments, the inspection system may have a reference GLV difference and filter out plottings that do not satisfy the reference GLV difference (e.g., filter out plottings where the local minimum or local maximum are not outliers compared to an average local minimum or local maximum). The inspection system may analyze the plottings that do satisfy the reference GLV difference (e.g., if difference 912D of Fig. 9D satisfies a reference difference, the inspection system may analyze graph 900D to determine whether a defect associated with the location of interest exists in the sample).

[0095] Advantageously, the disclosed embodiments involve the analysis of smaller, targeted areas of an image based on locations of interest rather than analysis of an entire image (e.g., where most of the image does not contain locations of interest). As a result, the disclosed embodiments include inspection methods that are more sensitive to time-dependent defects on a sample.

[0096] Reference is now made to Fig. 10, an exemplary process 1000 for classifying defects on a sample, consistent with embodiments of the present disclosure. It is understood that process 1000 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).

[0097] At step 1002, a system (e.g., electron beam tool 104 of Fig. 2A, electron beam tool 104 of Fig. 2B) may obtain a first image (e.g., first image 620 of Fig. 6, image 700 of Fig. 7) of a sample. In some embodiments, the first image may include features (e.g., features 622 and 624 of Fig. 6) of sample (e.g., sample 208 of Fig. 2A, wafer 150 of Fig. 2B), where each feature of the sample may have a corresponding grey level in the first image.

[0098] In some embodiments, the first image may be obtained to assist with alignment of the sample and the inspection tool to determine on which areas of a sample inspection should be focused for defect detection.

[0099] At step 1004, the system may locate a location of interest (e.g., predicted defects of interest (DOIs)) 710, 712, 714, 716, 718, and 720 on image 700 of Fig. 7) using the first image. In some embodiments, each location of interest may correspond to a feature of a sample where the system predicts a defect may occur.

[0100] In some embodiments, the locations of interest may be located based on layout data related to the sample. A layout design may be stored in a layout file for a wafer design. The 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. The 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 featureinformation stored in a binary file format representing planar geometric shapes, text, and other information related to the wafer design.

[0101] In some embodiments, the locations of interest may be located based on image-based binning using the first image.

[0102] At step 1006, the system may obtain a plurality of second images (e.g., plurality of second images 810, 820, 830, and 840 of Fig. 8) of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest.

[0103] In some embodiments, the plurality of second images may be obtained after locating locations of interest on the first image. In some embodiments, each of the plurality of second images may show a location of interest corresponding to a feature of the sample where the system predicts a defect may occur. Each of the plurality of second images may show a location of interest (e.g., locations of interest 812, 822, 832, and 842 of Fig. 8), with variation of grey level (GLV) values over time.

[0104] At step 1008, the system may track GLV for the location of interest based on the plurality of second images.

[0105] At step 1010, the system may plot a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images. The relationship between the GLV and time (e.g., in graph 854 of Fig. 8, graph 900A of Fig. 9A, graph 900B of Fig. 9B, graph 900C of Fig. 9C, graph 900D of Fig. 9D) may be plotted for a location of interest in the plurality of second images.

[0106] At step 1012, the system may determine, using the plotting, whether a defect has occurred at the location of interest.

[0107] In some embodiments, the inspection system may determine one or more defects by determining a characteristic (e.g., characteristics 912A-912D of Figs. 9A-9D) of the plotting. For example, based on reference value and the determined characteristic of the plotting, the inspection system may determine whether a defect exists in a location of interest in a sample. In some embodiments, the characteristic may be any one of GLV range (see, e.g., Fig. 9A), slope (see, e.g., Fig. 9B), a local minimum or a local maximum (see, e.g., Fig. 9C), or a difference between an average GLV local minimum or local maximum and a local minimum or a local maximum of a location of interest (see, e.g., Fig. 9D).

[0108] In some embodiments, the slope of the plotting may indicate any one of a charging rate or a discharging rate associated with the sample. In some embodiments, the local minimum or the local maximum may indicate an end of a charging process or a discharging process associated with the sample.

[0109] In some embodiments, the inspection system may classify the determined defect based on the determined characteristic of the plotting.

[0110] In some embodiments, the inspection system may plot relationships between the GLV for each of a plurality of second locations of interest and a time at which the GLV was captured, determine a characteristic of each of the plottings, and compare the characteristic of the plottingassociated with a first location of interest to the characteristics of the plottings associated with the plurality of second locations of interest.

[0111] A non-transitory computer readable medium may be provided that stores instructions for a processor of a controller (e.g., controller 109 of Fig. 1) for controlling the electron beam tool or other systems of other systems and servers, or components thereof, consistent with embodiments in the present disclosure. These instructions may allow the one or more processors to carry out image processing, data processing, beamlet scanning, graphical display, operations of a charged particle beam apparatus, or another imaging device, or the like for providing operations consistent with those described above for Fig. 10. In some embodiments, the non-transitory computer readable medium may be provided that stores instructions for a processor to perform the steps of process 1000.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.

[0112] The embodiments may further be described using the following clauses:1. A method for classifying defects on a sample, comprising: obtaining a first image of a sample; locating a location of interest using the first image; obtaining a plurality of second images of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest; tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images; and determining, using the plotting, whether a defect has occurred at the location of interest.2. The method of clause 1, wherein locating the location of interest is based on layout data related to the sample.3. The method of clause 1, wherein locating the location of interest is based on image-based binning using the first image.4. The method of any one of clauses 1-3, wherein the location of interest corresponds to a predicted defect of interest on the sample.5. The method of any one of clauses 1-4, wherein the location of interest comprises a plurality of locations of interest on the sample.6. The method of any one of clauses 1-5, wherein the location of interest comprises an area of the first image that is less than an area of the entire first image.7. The method of any one of clauses 1-6, wherein the location of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.8. The method of any one of clauses 1-7, wherein determining the defect comprises determining a characteristic of the plotting.9. The method of clause 8, wherein the characteristic is a GLV range of the plotting.10. The method of clause 8, wherein the characteristic is a slope of the plotting.11. The method of clause 10, wherein the slope indicates any one of a charging rate or a discharging rate associated with the sample.12. The method of clause 8, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.13. The method of clause 12, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with the sample.14. The method of clause 8, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.15. The method of any one of clauses 8-14, further comprising classifying the determined defect based on the determined characteristic of the plotting.16. The method of any one of clauses 8-15, wherein the location of interest is a first location of interest and further comprising a plurality of second locations of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second locations of interest and a time at which the GLV was captured; determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first location of interest to the characteristics of the plottings.17. A method for classifying defects on a sample, comprising: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.18. The method of clause 17, wherein locating the predicted defect of interest is based on layout data related to a sample.19. The method of clause 17, wherein locating the predicted defect of interest is based on image -based binning using the first image.20. The method of any one of clauses 17-19, wherein the predicted defect of interest comprises a plurality of predicted defects of interest on a sample.21. The method of any one of clauses 17-20, wherein the predicted defect of interest comprises an area of the first image that is less than an area of the entire first image.22. The method of any one of clauses 17-21, wherein the predicted defect of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.23. The method of any one of clauses 17-22, wherein determining the defect comprises determining a characteristic of a plotting of the relationship.24. The method of clause 23, wherein the characteristic is a GLV range of the plotting.25. The method of clause 23, wherein the characteristic is a slope of the plotting.26. The method of clause 25, wherein the slope indicates any one of a charging rate or a discharging rate associated with a sample.27. The method of clause 23, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.28. The method of clause 27, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with a sample.29. The method of clause 23, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.30. The method of any one of clauses 23-29, further comprising classifying the determined defect based on the determined characteristic of the plotting.31. The method of any one of clauses 23-30, wherein the predicted defect of interest is a first predicted defect of interest and further comprising a plurality of second predicted defects of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second predicted defects of interest and a time at which the GLV was captured; determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first predicted defect of interest to the characteristics of the plottings.32. A system for classifying defects on a sample, 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: obtaining a first image of a sample; locating a location of interest using the first image; obtaining a plurality of second images of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest;tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images; and determining, using the plotting, whether a defect has occurred at the location of interest.33. The system of clause 32, wherein locating the location of interest is based on layout data related to the sample.34. The system of clause 32, wherein locating the location of interest is based on image -based binning using the first image.35. The system of any one of clauses 32-34, wherein the location of interest corresponds to a predicted defect of interest on the sample.36. The system of any one of clauses 32-35, wherein the location of interest comprises a plurality of locations of interest on the sample.37. The system of any one of clauses 32-36, wherein the location of interest comprises an area of the first image that is less than an area of the entire first image.38. The system of any one of clauses 32-37, wherein the location of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.39. The system of any one of clauses 32-38, wherein determining the defect comprises determining a characteristic of the plotting.40. The system of clause 39, wherein the characteristic is a GLV range of the plotting.41. The system of clause 39, wherein the characteristic is a slope of the plotting.42. The system of clause 41, wherein the slope indicates any one of a charging rate or a discharging rate associated with the sample.43. The system of clause 39, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.44. The system of clause 43, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with the sample.45. The system of clause 39, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.46. The system of any one of clauses 39-45, wherein the operations further comprise classifying the determined defect based on the determined characteristic of the plotting.47. The system of any one of clauses 39-46, wherein the location of interest is a first location of interest and further comprising a plurality of second locations of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second locations of interest and a time at which the GLV was captured;determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first location of interest to the characteristics of the plottings.48. A system for classifying defects on a sample, 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: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.49. The system of clause 48, wherein locating the predicted defect of interest is based on layout data related to a sample.50. The system of clause 48, wherein locating the predicted defect of interest is based on image-based binning using the first image.51. The system of any one of clauses 48-50, wherein the predicted defect of interest comprises a plurality of predicted defects of interest on a sample.52. The system of any one of clauses 48-51, wherein the predicted defect of interest comprises an area of the first image that is less than an area of the entire first image.53. The system of any one of clauses 48-52, wherein the predicted defect of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.54. The system of any one of clauses 48-53, wherein determining the defect comprises determining a characteristic of a plotting of the relationship.55. The system of clause 54, wherein the characteristic is a GLV range of the plotting.56. The system of clause 54, wherein the characteristic is a slope of the plotting.57. The system of clause 56, wherein the slope indicates any one of a charging rate or a discharging rate associated with a sample.58. The system of clause 54, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.59. The system of clause 58, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with a sample.60. The system of clause 54, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.61. The system of any one of clauses 54-60, wherein the operations further comprise classifying the determined defect based on the determined characteristic of the plotting.62. The system of any one of clauses 54-61, wherein the predicted defect of interest is a first predicted defect of interest and further comprising a plurality of second predicted defects of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second predicted defects of interest and a time at which the GLV was captured; determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first predicted defect of interest to the characteristics of the plottings.63. 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 classifying defects on a sample, comprising: obtaining a first image of a sample; locating a location of interest using the first image; obtaining a plurality of second images of the sample, wherein each second image of the plurality of second images corresponds to a different point in time of the location of interest; tracking variation of grey level (GLV) for the location of interest based on the plurality of second images; plotting a relationship between the GLV and a time at which the GLV was captured based on the plurality of second images; and determining, using the plotting, whether a defect has occurred at the location of interest.64. The non-transitory computer readable medium of clause 63, wherein locating the location of interest is based on layout data related to the sample.65. The non-transitory computer readable medium of clause 63, wherein locating the location of interest is based on image-based binning using the first image.66. The non-transitory computer readable medium of any one of clauses 63-65, wherein the location of interest corresponds to a predicted defect of interest on the sample.67. The non-transitory computer readable medium of any one of clauses 63-66, wherein the location of interest comprises a plurality of locations of interest on the sample.68. The non-transitory computer readable medium of any one of clauses 63-67, wherein the location of interest comprises an area of the first image that is less than an area of the entire first image.69. The non-transitory computer readable medium of any one of clauses 63-68, wherein the location of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.70. The non-transitory computer readable medium of any one of clauses 63-69, wherein determining the defect comprises determining a characteristic of the plotting.71. The non-transitory computer readable medium of clause 70, wherein the characteristic is a GLV range of the plotting.72. The non-transitory computer readable medium of clause 70, wherein the characteristic is a slope of the plotting.73. The non-transitory computer readable medium of clause 72, wherein the slope indicates any one of a charging rate or a discharging rate associated with the sample.74. The non-transitory computer readable medium of clause 70, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.75. The non-transitory computer readable medium of clause 74, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with the sample.76. The non-transitory computer readable medium of clause 70, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.77. The non-transitory computer readable medium of any one of clauses 70-76, 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 classifying the determined defect based on the determined characteristic of the plotting.78. The non-transitory computer readable medium of any one of clauses 70-77, wherein the location of interest is a first location of interest and further comprising a plurality of second locations of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second locations of interest and a time at which the GLV was captured; determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first location of interest to the characteristics of the plottings.79. 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 classifying defects on a sample, comprising: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.80. The non-transitory computer readable medium of clause 79, wherein locating the predicted defect of interest is based on layout data related to a sample.81. The non-transitory computer readable medium of clause 79, wherein locating the predicted defect of interest is based on image-based binning using the first image.82. The non-transitory computer readable medium of any one of clauses 79-81, wherein the predicted defect of interest comprises a plurality of predicted defects of interest on a sample.83. The non-transitory computer readable medium of any one of clauses 79-82, wherein the predicted defect of interest comprises an area of the first image that is less than an area of the entire first image.84. The non-transitory computer readable medium of any one of clauses 79-83, wherein the predicted defect of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.85. The non-transitory computer readable medium of any one of clauses 79-84, wherein determining the defect comprises determining a characteristic of a plotting of the relationship.86. The non-transitory computer readable medium of clause 85, wherein the characteristic is a GLV range of the plotting.87. The non-transitory computer readable medium of clause 85, wherein the characteristic is a slope of the plotting.88. The non-transitory computer readable medium of clause 87, wherein the slope indicates any one of a charging rate or a discharging rate associated with a sample.89. The non-transitory computer readable medium of clause 85, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.90. The non-transitory computer readable medium of clause 89, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with a sample.91. The non-transitory computer readable medium of clause 85, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.92. The non-transitory computer readable medium of any one of clauses 85-91, 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 classifying the determined defect based on the determined characteristic of the plotting.93. The non-transitory computer readable medium of any one of clauses 85-92, wherein the predicted defect of interest is a first predicted defect of interest and further comprising a plurality of second predicted defects of interest, wherein determining whether the defect occurs comprises: plotting relationships between the GLV for each of the plurality of second predicted defects of interest and a time at which the GLV was captured; determining a characteristic of each of the plottings; and comparing the characteristic of the plotting associated with the first predicted defect of interest to the characteristics of the plottings.

[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 method for classifying defects on a sample, comprising: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.

2. The method of claim 1, wherein locating the predicted defect of interest is based on layout data related to a sample.

3. The method of claim 1, wherein locating the predicted defect of interest is based on image -based binning using the first image.

4. The method of claim 1, wherein the predicted defect of interest comprises a plurality of predicted defects of interest on a sample.

5. The method of claim 1, wherein the predicted defect of interest comprises an area of the first image that is less than an area of the entire first image.

6. The method of claim 1, wherein the predicted defect of interest on each of the plurality of second images comprises an area of each second image that is less than area of the entire second image.

7. The method of claim 1, wherein determining the defect comprises determining a characteristic of a plotting of the relationship.

8. The method of claim 7, wherein the characteristic is a GLV range of the plotting.

9. The method of claim 7, wherein the characteristic is a slope of the plotting.

10. The method of claim 9, wherein the slope indicates any one of a charging rate or a discharging rate associated with a sample.

11. The method of claim 7, wherein the characteristic is any one of a local minimum or a local maximum of the plotting.

12. The method of claim 11, wherein the local minimum or the local maximum indicates an end of a charging process or a discharging process associated with a sample.

13. The method of claim 7, wherein the characteristic is a difference between an average GLV of the plurality of second images and a local minimum or a local maximum of the plotting.

14. A system for classifying defects on a sample, 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: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.

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 a method for classifying defects on a sample, comprising: locating a predicted defect of interest on a first image; tracking variation of grey level (GLV) for the predicted defect of interest based on a plurality of second images that vary with time; and determining a defect based on a relationship between the GLV and a time at which the GLV was measured.

Images