System and method for optimizing sample scanning in inspection system

Abstract

Systems and methods for optimizing sample scanning. Systems and methods may include providing a plurality of charged particle beams for scanning a first sample; identifying one or more failed charged particle beams of the plurality of charged particle beams; identifying one or more effective charged particle beams of the plurality of charged particle beams that scan the first sample; generating a beam pattern based on the identified one or more failed charged particle beams and the identified one or more valid charged particle beams, wherein the generated beam pattern does not include the identified one or more failed charged particle beams; and formulating a scanning strategy for scanning the first sample or the second sample based on the generated beam pattern.

Description

Cross Reference to Related Applications

[0001] This application claims priority to U.S. Application 63 / 464,501, filed May 5, 2023, and incorporated by reference herein in its entirety. TECHNICAL FIELD

[0002] The description herein relates to the field of inspection and charged particle systems, and more particularly to methods for optimizing sample scanning in an inspection system. BACKGROUND

[0003] In the manufacturing process 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. Inspection systems that utilize optical microscopes typically have a resolution down to a few hundred nanometers; and the resolution is limited by the wavelength of light. As the physical size of IC components continues to decrease below 100 nanometers or even below 10 nanometers, there is a need for inspection systems that have a resolution higher than inspection systems that use optical microscopes.

[0004] Charged particle (e.g., electron) beam microscopes, such as scanning electron microscopes (SEMs) or transmission electron microscopes (TEMs), that have a resolution down to less than 1 nanometer serve as a practical tool for inspecting IC components that have feature sizes below 100 nanometers. With an SEM, the electrons of a single primary electron beam or the electrons of multiple primary electron beams can be focused at a location of interest of a wafer under inspection. The primary electrons interact with the wafer and can be backscattered or can cause the wafer to emit secondary electrons. The intensity of the electron beam, including the backscattered electrons and the secondary electrons, can vary based on the properties of the internal and external structures of the wafer, which can indicate whether the wafer has defects. SUMMARY

[0005] Embodiments of the present disclosure provide systems and methods for optimizing sample scanning in an inspection system. In some embodiments, the systems, methods, and non-transitory computer readable media can include providing a plurality of charged particle beams for scanning a first sample; identifying one or more failed charged particle beams of the plurality of charged particle beams; identifying one or more effective charged particle beams of the plurality of charged particle beams that scanned the first sample; generating a beam map based on the identified one or more failed charged particle beams and the identified one or more effective charged particle beams, wherein the generated beam map does not include the identified one or more failed charged particle beams; and formulating a scanning strategy for scanning the first sample or a second sample based on the generated beam map.

[0006] In some embodiments, the systems, methods, and non-transitory computer- readable media can include scanning a first sample; generating a beam map based on the one or more failed charged particle beams of the scan and the one or more successful charged particle beams of the scan; formulating a scan strategy based on the generated beam map; and scanning the first sample or a second sample according to the scan strategy.

[0007] In some embodiments, the systems, methods, and non-transitory computer- readable media can include determining one or more failed beams in a multi-beam system; determining one or more successful beams in the multi-beam system; generating a beam map based on determining the one or more failed beams and determining the one or more successful beams; and formulating a scan strategy for a sample based on the generated beam map. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 FIG. 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system consistent with embodiments of the present disclosure.

[0009] Figure 2A FIG. 2 is a schematic diagram illustrating an exemplary multi-beam system consistent with embodiments of the present disclosure, which is part of the exemplary charged particle beam inspection system of FIG. 1. Figure 1 FIG. 3 is a schematic diagram illustrating an exemplary single-beam system consistent with embodiments of the present disclosure, which is part of the exemplary charged particle beam inspection system of FIG. 1.

[0010] Figure 2B FIG. 4 is a schematic diagram illustrating an exemplary single-beam system consistent with embodiments of the present disclosure, which is part of the exemplary charged particle beam inspection system of FIG. 1. Figure 1 FIG. 5 is a schematic diagram illustrating an exemplary multi-beam system consistent with embodiments of the present disclosure, which is part of the exemplary charged particle beam inspection system of FIG. 1.

[0011] Figure 3 FIG. 6 illustrates an exemplary beam map corresponding to a multi-beam system consistent with embodiments of the present disclosure.

[0012] Figure 4 FIG. 7 illustrates an exemplary missing spot map corresponding to a multi-beam system consistent with embodiments of the present disclosure.

[0013] Figure 5 FIG. 8 illustrates an exemplary construction of an optimized beam map corresponding to a multi-beam system consistent with embodiments of the present disclosure.

[0014] Figure 6A FIG. 9 illustrates an exemplary array with position tags consistent with embodiments of the present disclosure.

[0015] Figure 6B FIG. 10 illustrates an exemplary optimized beam map consistent with embodiments of the present disclosure.

[0016] Figure 6C FIG. 11 illustrates an exemplary beam map.

[0017] Figure 6D FIG. 12 illustrates an exemplary plot consistent with embodiments of the present disclosure.

[0018] Figure 7 An exemplary process for optimizing sample scanning consistent with embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0019] 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 same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementation described in the following description relates to semiconductor processing. Implementations of the techniques, however, can be used in other implementations and contexts. In the following description, numerous specific details are set forth to provide a thorough understanding of the implementations. However, implementations can be practiced without one or more of these specific details. In other instances, well-known structures and components are not described in detail to avoid obscuring the implementations. In some aspects, some embodiments are described in the context of using an electron beam. However, the present disclosure is not limited to this. Other types of charged particle beams can be similarly applied. Moreover, other imaging systems can be used, such as optical imaging, photoelectric detection, x-ray detection, extreme ultraviolet inspection, deep ultraviolet inspection, etc., where they generate corresponding types of images.

[0020] Electronic devices are made up of circuits formed on silicon wafers called substrates. Many circuits can be formed together on the same silicon wafer and are referred to as integrated circuits or ICs. The size of these circuits has dramatically decreased so that more circuits can be installed on the substrate. For example, an IC chip in a smartphone can be as small as a fingernail but can include over 2 billion transistors, each smaller than 1 / 1000th the size of a human hair.

[0021] Manufacturing these tiny ICs is a complex, time-consuming, and expensive process that typically involves hundreds of individual steps. Even an error in one of the steps can cause the completed IC to be defective, making it unusable. Therefore, one goal of the manufacturing process is to avoid such defects to maximize the number of functional ICs manufactured in the process, i.e., to improve the overall yield of the process.

[0022] One component of improving yield is to monitor the chip manufacturing process to ensure that a sufficient number of functional ICs are produced. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. The inspection can be performed using a scanning electron microscope (SEM). The SEM can be used to image the tiny structures, in effect, to “take a picture” of the wafer structure. The image can be used to determine whether the structure was formed correctly and at the correct location. If the structure is defective, then the process can be adjusted so that the defect is less likely to occur again. Defects can occur during various stages of semiconductor processing. It is important to find defects early and accurately and efficiently for the reasons explained above.

[0023] A SEM works similarly to a camera. A camera takes a picture by receiving light reflected or emitted from a person or object and recording its brightness and color. A SEM takes a "photograph" by receiving electrons reflected or emitted from a structure and recording their energy or quantity. Before taking this "photograph," an electron beam can be projected onto the structure, and as the electrons are reflected or emitted ("leaving") from the structure, the SEM's detector can receive these electrons and record their energy or quantity to generate an image. To take this "photograph," some SEMs use a single electron beam (called a "single-beam SEM"), while others use multiple electron beams (called a "multi-beam SEM") to take multiple "photographs" of the wafer. By using multiple electron beams, the SEM can project more electron beams onto the structure to obtain these multiple "photographs," thus allowing more electrons to leave the structure. Therefore, the detector can receive more leaving electrons simultaneously and generate images of the wafer structure with greater efficiency and faster speed.

[0024] In multi-beam inspection systems, a certain percentage of beams fail during inspection. Due to beam failure, these inspection systems generate poor or no images, resulting in a loss of image capture rate and missing defects in the sample. For example, beam failure may occur because particles block one or more beams, or due to discharge between the microelectromechanical system (MEMS) region and other ground surfaces in the inspection system, which can result in large spots on the generated image.

[0025] However, typical inspection systems are constrained. They cannot re-acquire any images from the locations scanned on the sample using a failure beam, resulting in poor image quality and defects going undetected, thus reducing the system's throughput.

[0026] The disclosed embodiments provide systems and methods for addressing some or all of these drawbacks by optimizing sample scanning. The disclosed embodiments may include providing a plurality of charged particle beams to a first sample; identifying one or more failed charged particle beams among the plurality of beams; generating a beammap based on the identified one or more failed charged particle beams; developing a scanning strategy based on the beammap; and providing one or more charged particle beams among the plurality of beams to a second sample according to the scanning strategy. For example, by identifying one or more failed charged particle beams and generating corresponding beammaps, the inspection system can develop a scanning strategy such that regions of the sample corresponding to the failed charged particle beams can be scanned by effective charged particle beams.

[0027] In some embodiments, the first sample may be a test sample with a predetermined pattern, and the second sample may be an inspection sample. By first scanning the test sample and identifying the failed charged particle beam, a robust scanning strategy can be developed to provide an effective charged particle beam to the inspection sample. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0028] In some embodiments, the first sample can be a second sample. That is, the disclosed embodiments may scan only the sample during inspection. By scanning the sample during inspection and identifying failed charged particle beams, a high-throughput scanning strategy can be developed such that areas of the inspected sample scanned with failed charged particle beams can be rescanned with effective charged particle beams. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0029] For clarity, the relative dimensions of the components in the accompanying drawings may be enlarged. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only differences relative to the various embodiments are described.

[0030] As used herein, unless otherwise specified, the term "or" covers all possible combinations except those that are not feasible. For example, if a descriptive component may include A or B, then unless otherwise specified or feasible, the component may include A or B or A and B. As a second example, if a descriptive component may include A, B, or C, then unless otherwise specified or feasible, 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.

[0031] Some embodiments can be described in the context of providing detectors and detection methods in systems utilizing electron beams without limiting the scope of this disclosure. However, this disclosure is not limited thereto. Other types of charged particle beams can be applied similarly. Furthermore, the systems and methods used for detection can be used in other imaging systems, such as optical imaging, photon detection, X-ray detection, ion detection, etc.

[0032] Figure 1 An exemplary electron beam inspection (EBI) system 100 consistent with embodiments of this disclosure is illustrated. The EBI system 100 can be used for imaging. Figure 1As shown, the EBI system 100 includes a main chamber 101, a loading / locking chamber 102, an electron beam tool 104, and an Equipment Front-End Module (EFEM) 106. The electron beam tool 104 is located within the main chamber 101. The EFEM 106 includes a first loading port 106a and a second loading port 106b. The EFEM 106 may include multiple additional loading ports. The first loading port 106a and the second loading port 106b accommodate wafer front-opening transport cassettes (FOUPs) containing wafers (e.g., one or more semiconductor wafers made of (multiple) other materials) or samples (wafers and samples are interchangeable) to be inspected. A “batch” is a plurality of wafers that can be processed as a single load.

[0033] One or more robotic arms (not shown) in EFEM 106 can transport the wafer to loading / locking chamber 102. Loading / locking chamber 102 is connected to a loading / locking vacuum pump system (not shown), which removes gas molecules from loading / locking chamber 102 to achieve a first pressure below atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) can transport the wafer from loading / locking chamber 102 to main chamber 101. Main chamber 101 is connected to a main chamber vacuum pump system (not shown), which removes gas molecules from main chamber 101 to achieve a second pressure below the first pressure. After reaching the second pressure, the wafer is inspected by electron beam tool 104. Electron beam tool 104 can be a single-beam system or a multi-beam system.

[0034] The controller 109 is electronically connected to the electron beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. Although in Figure 1 The controller 109 is shown outside the structure including the main chamber 101, the loading / locking chamber 102 and the EFEM 106, but it should be understood that the controller 109 may be part of the structure.

[0035] In some embodiments, controller 109 may include one or more processors (not shown). A processor may be a general-purpose or specific electronic device capable of manipulating or processing information. For example, a processor may include any number of central processing units (or “CPU”), graphics processing units (or “GPU”), optical processors, programmable logic controllers, microcontrollers, microprocessors, digital signal processors, intellectual property (IP) cores, programmable logic arrays (PLAs), programmable array logic (PALs), general-purpose array logic (GALs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), system-on-a-chip (SoCs), application-specific integrated circuits (ASICs), and any combination of any type of circuitry capable of data processing. A processor may also be a virtual processor, comprising one or more processors distributed across multiple machines or devices coupled via a network.

[0036] In some embodiments, controller 109 may also include one or more memories (not shown). The memories can be general-purpose or specific electronic devices capable of storing processor-accessible (e.g., via a bus) code and data. For example, the memories can include any number of random access memory (RAM), read-only memory (ROM), optical discs, magnetic disks, hard disks, solid-state drives, flash drives, secure digital cards (SD cards), memory sticks, compact flash (CF) cards, or any combination of any type of storage device. The code can include an operating system (OS) and one or more applications (or "applications") for a specific task. The memories can also be virtual memory, which includes one or more memories distributed across multiple machines or devices coupled via a network.

[0037] Embodiments of this disclosure can provide a single charged particle beam imaging system (“single-beam system”). Compared to a single-beam system, a multi-charged particle beam imaging system (“multi-beam system”) can be designed to optimize throughput for different scanning modes. Embodiments of this disclosure provide a multi-beam system with the ability to optimize throughput for different scanning modes by using beam arrays with different geometries and adapting to different throughput and resolution requirements.

[0038] Now for reference Figure 2A Figure 2 is a schematic diagram illustrating an exemplary electron beam tool 104 consistent with embodiments of the present disclosure, the electron beam tool 104 including as... Figure 1 The electron beam tool 104 is a multi-beam inspection tool that is part of the EBI system 100. In some embodiments, the electron beam tool 104 can operate as a single-beam inspection tool. Figure 1This is part of the EBI system 100. The multi-beam electron beam tool 104 (also referred to herein as apparatus 104) includes an electron source 201, a coulomb aperture plate (or “gun aperture plate”) 271, a converging lens 210, a source conversion unit 220, a primary projection system 230, a motorized platform 209, and a sample holder 207 supported by the motorized platform 209 to hold a sample 208 (e.g., a wafer or photomask) to be inspected. The multi-beam electron beam tool 104 may also include a secondary projection system 250 and an electron detection device 240. The primary projection system 230 may include an objective lens 231. The electron detection device 240 may include multiple detection elements 241, 242, and 243. A beam splitter 233 and a deflection scanning unit 232 may be positioned within the primary projection system 230.

[0039] The electron source 201, coulomb aperture plate 271, converging lens 210, source conversion unit 220, beam splitter 233, deflection scanning unit 232, and primary projection system 230 can be aligned with the primary optical axis 204 of device 104. The secondary projection system 250 and electronic detection device 240 can be aligned with the secondary optical axis 251 of device 104.

[0040] The electron source 201 may include a cathode (not shown) and an extractor or anode (not shown), wherein, during operation, the 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, which forms a primary beam cross (virtual or real) 203. The primary electron beam 202 can be visualized as being emitted from the primary beam cross 203.

[0041] Source conversion unit 220 may include an image forming element array (not shown), an aberration compensator array (not shown), a beam-limiting aperture array (not shown), and a pre-bent micro-polarizer array (not shown). In some embodiments, the pre-bent micro-polarizer array deflects multiple primary beam waves 211, 212, 213 of the primary electron beam 202 so that they enter the beam-limiting aperture array, the image forming element array, and the aberration compensator array normally. In some embodiments, device 104 may operate as a single-beam system, thereby generating a single primary beam wave. In some embodiments, converging lens 210 is designed to focus the primary electron beam 202 into a parallel beam and incident normally onto source conversion unit 220. The image forming element array may include multiple micro-polarizers or microlenses to influence the multiple primary beam waves 211, 212, 213 of the primary electron beam 202 and form multiple parallel images (virtual or real) of the primary beam cross 203, one parallel image for each of the primary beam waves 211, 212, and 213. In some embodiments, the aberration compensator array may include a field curvature compensator array (not shown) and an astigmatism compensator array (not shown). The field curvature compensator array may include a plurality of microlenses to compensate for field curvature aberrations of primary beams 211, 212, and 213. The astigmatism compensator array may include a plurality of micro-astigmatism reducers to compensate for astigmatic aberrations of primary beams 211, 212, and 213. A beam-limiting aperture array may be configured to limit the diameter of each primary beam 211, 212, and 213. Figure 2A Three primary beams 211, 212, and 213 are shown as examples, and it should be understood that the source conversion unit 220 can be configured to form any number of primary beams. The controller 109 can be connected to... Figure 1 Various components of the EBI system 100, such as the source conversion unit 220, the electronic inspection device 240, the primary projection system 230, or the motorized platform 209. In some embodiments, as explained in further detail below, the controller 109 can perform various image and signal processing functions. The controller 109 can also generate various control signals to manage the operation of the charged particle beam inspection system.

[0042] Converging lens 210 is configured to focus primary electron beam 202. Converging lens 210 can also be configured to adjust the current of primary beams 211, 212, and 213 downstream of source conversion unit 220 by changing the focusing capability of converging lens 210. Alternatively, the current can be changed by altering the radial dimension of the limiting aperture within the aperture array corresponding to each primary beam. The current can be changed by altering the radial dimension of the limiting aperture and the focusing capability of converging lens 210. Converging lens 210 can be an adjustable converging lens, configured such that the position of its first principal plane is movable. The adjustable converging lens can be configured to be magnetic, which can cause off-axis beams 212 and 213 to irradiate source conversion unit 220 at a rotational angle. The rotational angle varies with the focusing capability or the position of the first principal plane of the adjustable converging lens. Converging lens 210 can be an anti-rotation converging lens, configured to maintain a constant rotational angle while the focusing capability of converging lens 210 is changed. In some embodiments, the converging lens 210 may be an adjustable anti-rotation converging lens, wherein the rotation angle does not change when its focusing capability and the position of the first principal plane change.

[0043] Objective lens 231 can be configured to focus beams 211, 212, and 213 onto sample 208 for inspection, and in the current embodiment, three probe spots 221, 222, and 223 can be formed on the surface of sample 208. Coulomb aperture plate 271 is configured in operation to block peripheral electrons of the primary electron beam 202 to reduce the Coulomb effect. The Coulomb effect can increase the size of each of the probe spots 221, 222, and 223 of the primary beams 211, 212, and 213, thus reducing inspection resolution.

[0044] Beam splitter 233 can be, for example, a Wien filter, including an electrostatic deflector that generates electrostatic dipole fields and magnetic dipole fields. Figure 2A (Not shown in the diagram). In operation, beam splitter 233 can be configured to apply electrostatic forces to the individual electrons of primary beams 211, 212, and 213 via an electrostatic dipole field. The electrostatic forces are equal in magnitude but opposite in direction to the magnetic forces applied to the individual electrons by the magnetic dipole field of beam splitter 233. Primary beams 211, 212, and 213 can therefore pass through beam splitter 233 at least substantially straight with a deflection angle of at least substantially zero.

[0045] Deflection scanning unit 232 is configured in operation to deflect primary beams 211, 212, and 213 to scan probe spots 221, 222, and 223 across various scanning regions in segments of the surface of sample 208. In response to the primary beams 211, 212, and 213 or probe spots 221, 222, and 223 being incident on sample 208, electrons emerge from sample 208 and generate three secondary electron beams 261, 262, and 263. Each of the secondary electron beams 261, 262, and 263 typically comprises secondary electrons (electron energy ≤ 50 eV) and backscattered electrons (electron energy between 50 eV and the landing energy of the primary beams 211, 212, and 213). Beam splitter 233 is configured to deflect the secondary electron beams 261, 262, and 263 toward secondary projection system 250. The secondary projection system 250 then focuses secondary electron beams 261, 262, and 263 onto the detection elements 241, 242, and 243 of the electron detection device 240. The detection elements 241, 242, and 243 are arranged to detect the corresponding secondary electron beams 261, 262, and 263 and generate corresponding signals, which are sent to the controller 109 or a signal processing system (not shown), for example, to construct an image of the corresponding scanned area of ​​the sample 208.

[0046] 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 include one or more pixels. The intensity signal output of the detection element may be the sum of signals generated by all pixels within the detection element.

[0047] In some embodiments, controller 109 may include an image processing system comprising an image acquirer (not shown) and a storage device (not shown). The image acquirer may include one or more processors. For example, the image acquirer may include a computer, server, mainframe, terminal, personal computer, any kind of mobile computing device, or a combination thereof. The image acquirer may be communicatively coupled to the electronic inspection device 240 of device 104 via a medium such as an electrical conductor, fiber optic cable, portable storage medium, IR, Bluetooth, Internet, wireless network, radio, or a combination thereof. In some embodiments, the image acquirer may receive signals from the electronic inspection device 240 and may construct an image. The image acquirer may thus acquire an image of sample 208. The image acquirer may also perform various post-processing functions, such as generating contours, overlaying indicators on the acquired image, etc. The image acquirer may be configured to perform adjustments such as brightness and contrast of the acquired image. In some embodiments, the storage device may be a storage medium such as a hard disk, flash drive, cloud storage device, random access memory (RAM), or other types of computer-readable storage. The storage device may be coupled to the image acquirer and may be used to save scanned raw image data as raw and post-processed images.

[0048] In some embodiments, the image acquirer may acquire one or more images of a sample based on imaging signals received from the electronic detection device 240. The imaging signals may correspond to a scanning operation for imaging charged particles. The acquired image may be a single image comprising multiple imaging regions. The single image may be stored in a storage device. The single image may be an original image that can be divided into multiple regions. Each region may include an imaging region containing features of the sample 208. The acquired images may include multiple images of a single imaging region of the sample 208 sampled multiple times over a time series. The multiple images may be stored in a storage device. In some embodiments, the controller 109 may be configured to perform image processing steps using multiple images of the same location on the sample 208.

[0049] In some embodiments, controller 109 may include a measurement circuitry (e.g., an analog-to-digital converter) to obtain the distribution of detected secondary electrons. The electron distribution data collected during the detection time window can be combined with corresponding scan path data for each of the primary beams 211, 212, and 213 incident on the wafer surface to reconstruct an image of the wafer structure being inspected. The reconstructed image can be used to reveal various features of the internal or external structure of sample 208, and thus can be used to reveal any defects that may exist in the wafer.

[0050] In some embodiments, the controller 109 may control the motorized platform 209 to move the sample 208 during examination. In some embodiments, the controller 109 may enable the motorized platform 209 to continuously move the sample 208 in one direction at a constant speed. In other embodiments, the controller 109 may enable the motorized platform 209 to change the speed of movement of the sample 208 over time depending on the steps of the scanning process.

[0051] although Figure 2A The apparatus 104 is shown using three primary electron beams, but it is to be understood that the apparatus 104 may use one, two, or more primary electron beams. This disclosure does not limit the number of primary electron beams used in the apparatus 104. In some embodiments, the apparatus 104 may be a SEM for photolithography. In some embodiments, the electron beam tool 104 may be a single-beam system or a multi-beam system.

[0052] For example, such as Figure 2B As shown, consistent with embodiments of this disclosure, the electron beam tool 100B (also referred to herein as apparatus 100B) can be a single-beam inspection tool used in the EBI system 10. Apparatus 100B includes a wafer holder 136 supported by a motorized platform 134 for holding a wafer 150 to be inspected. The electron beam tool 100B includes an electron emitter that may include a cathode 103, an anode 121, and a gun aperture 122. The electron beam tool 100B also includes a beam-limiting aperture 125, a converging lens 126, a column aperture 135, an objective lens assembly 132, and a detector 144. In some embodiments, the objective lens assembly 132 may be a modified SORIL lens, including a pole piece 132a, a control electrode 132b, a deflector 132c, and an excitation coil 132d. In the imaging process, an electron beam 161 emitted from the tip of cathode 103 can be accelerated by the voltage of anode 121, pass through gun aperture 122, beam-limiting aperture 125, converging lens 126, and focused into probe spot 170 by a modified SORIL lens, impacting the surface of wafer 150. Probe spot 170 can be scanned on the surface of wafer 150 by deflectors (such as deflector 132c or other deflectors in the SORIL lens). Detector 144 can collect secondary or scattered primary particles emitted from the wafer surface, such as secondary electrons or scattered primary electrons, to determine the beam intensity and enable the reconstruction of an image of the region of interest on wafer 150.

[0053] An image processing system 199 may also be provided, including an image acquirer 120, a storage device 130, and a controller 109. The image acquirer 120 may include one or more processors. For example, the image acquirer 120 may include a computer, server, mainframe, terminal, personal computer, any kind of mobile computing device, or a combination thereof. The image acquirer 120 can be connected to the detector 144 of the electron beam tool 100B via a medium such as an electrical conductor, fiber optic cable, portable storage medium, IR, Bluetooth, Internet, wireless network, radio, or a combination thereof. The image acquirer 120 can receive signals from the detector 144 and can construct an image. The image acquirer 120 can thus acquire an image of the wafer 150. The image acquirer 120 can also perform various post-processing functions, such as generating contours, overlaying indicators on the acquired image, etc. The image acquirer 120 can be configured to perform adjustments such as brightness and contrast of the acquired image. The storage device 130 may be a storage medium such as a hard disk, random access memory (RAM), cloud storage device, or other types of computer-readable storage. Storage device 130 may be coupled to image acquirer 120 and may be used to save scanned raw image data as raw images and post-processed images. Image acquirer 120 and storage device 130 may be connected to controller 109. In some embodiments, image acquirer 120, storage device 130 and controller 109 may be integrated into a single electronic control unit.

[0054] In some embodiments, the image acquirer 120 may acquire one or more images of a sample based on imaging signals received from the detector 144. The imaging signals may correspond to scanning operations for imaging charged particles. The acquired image may be a single image comprising multiple imaging regions, which may contain various features of the wafer 150. The single image may be stored in the storage device 130. Imaging may be performed based on imaging frames.

[0055] The converging and illumination optics of an electron beam tool may include or be supplemented by an electromagnetic quadrupole electron lens. For example, such as Figure 2B As shown, the electron beam tool 100B may include a first quadrupole lens 148 and a second quadrupole lens 158. In some embodiments, the quadrupole lenses are used to control the electron beam. For example, the first quadrupole lens 148 may be controlled to adjust the beam current, and the second quadrupole lens 158 may be controlled to adjust the beam spot size and beam shape.

[0056] Figure 2B The illustration depicts a charged particle beam device in which the inspection system can use a single primary beam, which can be configured to generate secondary electrons by interacting with a wafer 150. A detector 144 can be positioned along an optical axis 105, as shown. Figure 2BAs in the illustrated embodiment. The primary electron beam can be configured to travel along the optical axis 105. Therefore, the detector 144 can include a hole at its center, allowing the primary electron beam to pass through and reach the wafer 150.

[0057] Now for reference Figure 3 The embodiments consistent with those of this disclosure correspond to multi-beam systems (e.g.) Figure 1 EBI system 100, Figure 2A An exemplary beam pattern 300 of a multi-beam electron beam tool 104. In some embodiments, the multi-beam system can generate beam pattern 300 during the first scan of the sample, wherein the first scan may include providing multiple beams of charged particles to the sample. Beam pattern 300 may illustrate the multiple beams of charged particles (e.g., ...) of the multi-beam system. Figure 2A The array 310 corresponds to the primary electron beam 202. For example, the array 310 can show a multi-beam system provided to a sample (e.g., the primary electron beam 202). Figure 2A The array 310 is a layout of multiple charged particle beams of sample 208. In some embodiments, the array 310 may include at least one active charged particle beam 312 and at least one inactive charged particle beam 314. In some embodiments, if the beam diagram 300 shows that the percentage of inactive charged particle beams in the array is below a certain threshold (e.g., if inactive charged particle beams comprise 5% or less of the array), the system may not rescan the region of the sample.

[0058] For example, the effective charged particle beam 312 may correspond to the working charged particle beam that correctly scans the sample region, while the failed charged particle beam 314 may correspond to the failed charged particle beam that did not correctly scan (e.g., did not scan) the sample region.

[0059] In some embodiments, the system can scan a first sample by providing multiple charged particle beams to the first sample. The first sample may be a test sample with a predetermined pattern, and the second sample may be an inspection sample. For example, the test sample may have a predetermined pattern such that the system is aware of the pattern, and each charged particle beam scans the same feature on the test sample. In some embodiments, the predetermined pattern may be a layout design, which may be stored in a layout file of a wafer design. The layout file may be in Graphics Database System (GDS) format, Graphics Database System II (GDS II) format, Open Art System Exchange Standard (OASIS) format, Caltech Intermediate Format (CIF), etc. The wafer design may include patterns or structures for inclusion on the wafer. The pattern or structure may be a mask pattern for transferring features from a lithographic mask or mask stencil to the wafer. In some embodiments, the layout in GDS or OASIS format, etc., may include feature information stored in a binary file format, representing planar geometry, text, and other information related to the wafer design. In some embodiments, the layout design may correspond to the FOV of an inspection system (e.g., the FOV of an inspection system may include one or more layout structures of the layout design).

[0060] In some embodiments, the system may identify one or more failed charged particle beams in the charged particle beam array and generate beam diagram 300 based on one or more identified failed charged particle beams.

[0061] In some embodiments, the system can identify one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam in the charged particle beam array. For example, since each pixel of an image of a test sample generated from a valid charged particle beam has a corresponding grayscale value, and since the pattern on the test sample is predetermined, the system can identify whether the image was generated from a valid charged particle beam and whether any charged particle beams failed based on the grayscale histogram generated for each charged particle beam.

[0062] For example, a charged particle beam may fail due to defocusing or obstruction. The beam may become defocused due to misalignment of the MEMS component, or it may be blocked by one or more components obstructing one or more beams. In some instances, the charged particle beam may fail due to discharge between the MEMS component area and other reference surfaces in the inspection system, which can result in large spots on the generated image.

[0063] In some embodiments, a dull, monotonous grayscale or a low (e.g., gradient) gradient across the entire generated image may indicate one or more failed charged particle beams.

[0064] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of an image generated by the charged particle beam array. For example, an image of a test sample generated by a valid charged particle beam has a corresponding sharpness or resolution. Since the pattern on the test sample is predetermined, the system can identify whether the image was generated by a valid charged particle beam and whether any charged particle beams have failed based on the sharpness or resolution of the generated image.

[0065] In some embodiments, the generated image of the test sample having a sharpness or resolution different from the expected (e.g. known) sharpness or resolution can indicate one or more failed charged particle beams.

[0066] In some embodiments, the system can formulate a scanning strategy based on the generated beammap 300, wherein the generated beammap 300 corresponds to the beammap of the scanned test sample. In some embodiments, the system can provide the test sample with one or more charged particle beams (e.g., effective charged particle beams 312, excluding any failed charged particle beams 314) according to the scanning strategy. Figure 2A Primary electron beam 202, Figure 2B The electron beam 161 is used to scan and examine the sample (e.g., Figure 2A Sample 208 Figure 2B (wafer 150). In some embodiments, the scanning strategy may include providing one or more charged particle beams to one or more regions of the test sample corresponding to an identified failed charged particle beam (e.g., failed charged particle beam 314). That is, the one or more charged particle beams used to scan the test sample may be a subset of the charged particle beams used to scan the test sample, wherein the subset does not include the failed charged particle beam, such that the test sample is scanned only by the valid charged particle beams.

[0067] By scanning the test sample and identifying failed charged particle beams before scanning the inspection sample, a robust scanning strategy can be developed to ensure that an effective charged particle beam is delivered to the inspection sample. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0068] In some embodiments, the system can formulate a scanning strategy based on the generated beam pattern 300 and a layout design corresponding to a predetermined pattern. For example, based on the layout design, the system can identify higher priority regions and lower priority regions of the sample. For example, a higher priority region of the sample may be an area of ​​the sample that requires scanning or inspection more than a lower priority region of the sample. In some embodiments, the system can formulate a scanning strategy by arranging an array of charged particle beams (e.g., by positioning an array, shifting an array, rotating an array, rearranging charged particle beams in the array, etc.) such that one or more failed charged particle beams identified during inspection scan (e.g., align) the lower priority regions of the sample, and one or more valid charged particle beams identified during inspection scan (e.g., align) the higher priority regions of the sample.

[0069] In some embodiments, the system can scan the test sample by providing multiple charged particle beams to the test sample without pre-scanning the test sample or generating a beammap. The system may not be aware of the pattern of the test sample. In some embodiments, during the inspection scan of the test sample, the system can identify one or more failed charged particle beams in the charged particle beam array and generate a beammap 300 based on one or more identified failed charged particle beams.

[0070] In some embodiments, the system can identify one or more failed charged particle beams by generating grayscale histograms for multiple generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, the system can analyze one or more images from the same charged particle beam to determine whether the grayscale histogram changes at different locations on the inspected sample. Based on the system's analysis of one or more images from one or more charged particle beams, the system can identify valid charged particle beams and any failed charged particle beams.

[0071] In some embodiments, grayscale histograms showing little or no variation at different locations on an inspected sample for a charged particle beam can indicate one or more failed charged particle beams.

[0072] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of one or more generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, an image of an inspection sample generated due to a valid charged particle beam may have sharp edges (e.g., corresponding to a pattern scanned by a valid charged particle beam). An image of an inspection sample generated due to a failed charged particle beam may not have sharp edges.

[0073] In some embodiments, the system can formulate a scanning strategy based on the generated beammap 300, wherein the generated beammap 300 corresponds to the beammap of the scanned test sample. In some embodiments, the system can scan the test sample (e.g., the effective charged particle beam 312) by providing one or more charged particle beams (excluding any failed charged particle beams 314) to the test sample according to the scanning strategy. Figure 2A Sample 208 Figure 2B The wafer 150). In some embodiments, the scanning strategy may include providing one or more charged particle beams (e.g., 314) to one or more regions of the sample being inspected that correspond to the identified failed charged particle beam (e.g., failed charged particle beam 314). Figure 2A The primary electron beam 202 of the multi-beam tool 104 Figure 2B The single-beam tool 100B uses an electron beam 161 to rescan the inspection sample. That is, one or more charged particle beams used to rescan the inspection sample can be a subset of the charged particle beams used for the initial scan of the inspection sample, wherein the subset does not include failed charged particle beams, such that the inspection sample is scanned only by the effective charged particle beams.

[0074] By scanning the sample during inspection and identifying failed charged particle beams during inspection, a high-throughput scanning strategy can be developed, allowing areas of the sample scanned with failed charged particle beams to be rescanned with effective charged particle beams. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher-quality images, detect defects in samples, and improve the throughput of the inspection system.

[0075] Now for reference Figure 4 The embodiments consistent with those of this disclosure correspond to multi-beam systems (e.g.) Figure 1 EBI system 100, Figure 2A An exemplary missing spot pattern 400 of a multi-beam electron beam tool 104. In some embodiments, the multi-beam system can be based on the generated beam pattern (e.g., Figure 3 The system generates a missing spot map 400 from the generated beammap 300. In some embodiments, the system can generate the missing spot map 400 by arranging multiple generated beammaps within the field of view (FOV). In some embodiments, the missing spot map 400 may include a care area 412, which includes a sample (e.g., Figure 2A Sample 208 Figure 2B The chip 150) is composed of an effective beam of charged particles (e.g., Figure 3 The effective charged particle beam 312) scans the region. In some embodiments, the missing spot pattern 400 may include a missing spot 414, which corresponds to the sample with the ineffective charged particle beam (e.g., the effective charged particle beam 312) scanning area ... Figure 3The region corresponding to the failed charged particle beam 314. That is, the missing spot 414 can correspond to the region of the sample that was not scanned by the charged particle beam due to the failure of the charged particle beam.

[0076] In some embodiments, the system can scan a first sample by providing multiple charged particle beams to the first sample. The first sample may be a test sample with a predetermined pattern, and the second sample may be an inspection sample. For example, the test sample may have a predetermined pattern such that the system is aware of the pattern, and each charged particle beam scans the same feature on the test sample. In some embodiments, the predetermined pattern may be a layout design, which may be stored in a layout file of a wafer design. The layout file may be in GDS format, GDS II format, OASIS format, CIF, etc. The wafer design may include patterns or structures for inclusion on the wafer. The pattern or structure may be a mask pattern for transferring features from a lithographic mask or mask stencil to the wafer. In some embodiments, the layout in GDS or OASIS format, etc., may include feature information stored in a binary file format representing planar geometry, text, and other information related to the wafer design. In some embodiments, the layout design may correspond to the FOV of an inspection system (e.g., the FOV of an inspection system may include one or more layout structures of the layout design).

[0077] In some embodiments, the system may identify one or more failed charged particle beams in the charged particle beam array, generate a beam map based on one or more identified failed charged particle beams, and generate a missing spot map 400 based on the generated beam map.

[0078] In some embodiments, the system can identify one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam in the charged particle beam array. For example, since each pixel of an image of a test sample generated from a valid charged particle beam has a corresponding grayscale value, and since the pattern on the test sample is predetermined, the system can identify whether the image was generated from a valid charged particle beam and whether any charged particle beams failed based on the grayscale histogram generated for each charged particle beam.

[0079] For example, a charged particle beam may fail due to defocusing or obstruction. The beam may become defocused due to misalignment of the MEMS component, or it may be obstructed due to misalignment or one or more particles blocking one or more of the beam. In some instances, the charged particle beam may fail due to discharge between the MEMS component area and other reference surfaces in the inspection system, which can result in large spots on the generated image.

[0080] In some embodiments, a dull, monotonous grayscale or a low (e.g., gradient) gradient across the entire generated image may indicate one or more failed charged particle beams.

[0081] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of an image generated by the charged particle beam array. For example, an image of a test sample generated by a valid charged particle beam has a corresponding sharpness or resolution. Since the pattern on the test sample is predetermined, the system can identify whether the image was generated by a valid charged particle beam and whether any charged particle beams have failed based on the sharpness or resolution of the generated image.

[0082] In some embodiments, the generated image of the test sample having a sharpness or resolution different from the expected (e.g. known) sharpness or resolution can indicate one or more failed charged particle beams.

[0083] In some embodiments, the system can formulate a scanning strategy based on the generated missing spot map 400, wherein the generated missing spot map 400 corresponds to the missing spot map of the scanned test sample. In some embodiments, the system can provide the inspection sample with one or more charged particle beams (e.g., effective charged particle beam 312, etc.) that do not include any failed charged particle beams (e.g., corresponding to missing spot 414) according to the scanning strategy. Figure 2A Primary electron beam 202, Figure 2B The electron beam 161 is used to scan and examine the sample (e.g., Figure 2A Sample 208 Figure 2B (wafer 150). In some embodiments, the scanning strategy may include providing one or more charged particle beams to the missing spot 414, corresponding to one or more regions of the test sample that correspond to the identified failed charged particle beam. That is, the one or more charged particle beams used to scan the test sample may be a subset of the charged particle beams used to scan the test sample, wherein the subset does not include the failed charged particle beam, such that the test sample is scanned only by the valid charged particle beam.

[0084] By scanning the test sample and identifying failed charged particle beams before scanning the inspection sample, a robust scanning strategy can be developed to ensure that an effective charged particle beam is delivered to the inspection sample. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0085] In some embodiments, the system can formulate a scanning strategy based on the generated missing spot pattern 400 and a layout design corresponding to a predetermined pattern. For example, based on the layout design, the system can identify higher priority regions and lower priority regions of the sample. For example, a higher priority region of the sample may be an area of ​​the sample that requires scanning or inspection more than a lower priority region of the sample. In some embodiments, the system can formulate a scanning strategy by arranging an array of charged particle beams (e.g., by positioning an array, shifting an array, rotating an array, rearranging charged particle beams in the array, etc.) such that one or more failed charged particle beams identified during inspection scan (e.g., align) the lower priority regions of the sample, and one or more valid charged particle beams identified during inspection scan (e.g., align) the higher priority regions of the sample.

[0086] In some embodiments, the system can scan the test sample by providing multiple charged particle beams to the test sample without pre-scanning the test sample or generating a beammap. The system may not be aware of the pattern of the test sample. In some embodiments, during the inspection scan of the test sample, the system can identify one or more failed charged particle beams in the charged particle beam array, generate a beammap based on one or more identified failed charged particle beams, and generate a missing spot map 400 based on the generated beammap.

[0087] In some embodiments, the system can identify one or more failed charged particle beams by generating grayscale histograms for multiple generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, the system can analyze one or more images from the same charged particle beam to determine whether the grayscale histogram changes at different locations on the inspected sample. Based on the system's analysis of one or more images from one or more charged particle beams, the system can identify valid charged particle beams and any failed charged particle beams.

[0088] In some embodiments, grayscale histograms showing little or no variation at different locations on an inspected sample for a charged particle beam can indicate one or more failed charged particle beams.

[0089] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of one or more generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, an image of an inspection sample generated due to a valid charged particle beam may have sharp edges (e.g., corresponding to a pattern scanned by a valid charged particle beam). An image of an inspection sample generated due to a failed charged particle beam may not have sharp edges.

[0090] In some embodiments, the system can formulate a scanning strategy based on the generated missing spot map 400, wherein the generated missing spot map 400 corresponds to the missing spot map of the scanned test sample. In some embodiments, the system can scan the test sample (e.g., effective charged particle beam 312) by providing one or more charged particle beams (e.g., effective charged particle beam 312) to the test sample according to the scanning strategy, excluding any failed charged particle beams (e.g., corresponding to missing spot 414). Figure 2A Sample 208 Figure 2B (wafer 150). In some embodiments, the scanning strategy may include providing one or more beams of charged particles (e.g., to the missing spot 414). Figure 2A Primary electron beam 202, Figure 2B The electron beam 161 is used to rescan the inspection sample, and the missing spot 414 corresponds to one or more regions of the inspection sample that correspond to the identified failed charged particle beam. That is, the one or more charged particle beams used to rescan the inspection sample may be a subset of the charged particle beams used for the initial scan of the inspection sample, wherein the subset does not include the failed charged particle beam, such that the inspection sample is scanned only by the effective charged particle beam.

[0091] By scanning the sample during inspection and identifying failed charged particle beams during inspection, a high-throughput scanning strategy can be developed, allowing areas of the sample scanned with failed charged particle beams to be rescanned with effective charged particle beams. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher-quality images, detect defects in samples, and improve the throughput of the inspection system.

[0092] Now for reference Figure 5 The embodiments consistent with those of this disclosure correspond to multi-beam systems (e.g.) Figure 1 EBI system 100, Figure 2A An exemplary configuration of an optimized beam pattern 500 for a multi-beam electron beam tool 104. In some embodiments, the multi-beam system may generate a beam pattern 510 during the first scan of the sample, wherein the first scan may include providing multiple beams of charged particles to the sample. The beam pattern 510 may illustrate the multiple beams of charged particles (e.g., ...) of the multi-beam system. Figure 2A Primary electron beam 202, Figure 3 The array corresponding to array 310. For example, beam diagram 510 can show the multi-beam system provided to the sample (e.g., array 310). Figure 2A The arrangement of multiple charged particle beams (sample 208). In some embodiments, beam pattern 510 may include at least one effective charged particle beam 512 (e.g., sample 208). Figure 3 Effective charged particle beam 312) and at least one failed charged particle beam 514 (e.g. Figure 3The failed charged particle beam 314).

[0093] For example, the effective charged particle beam 512 may correspond to the working charged particle beam that correctly scans the sample region, while the failed charged particle beam 514 may correspond to the failed charged particle beam that did not correctly scan (e.g., did not scan) the sample region.

[0094] In some embodiments, the system can scan a first sample by providing multiple charged particle beams to the first sample. The first sample may be a test sample with a predetermined pattern, and the second sample may be an inspection sample. For example, the test sample may have a predetermined pattern such that the system is aware of the pattern, and each charged particle beam scans the same feature on the test sample. In some embodiments, the system may identify one or more failed charged particle beams in the charged particle beam array and identify one or more valid charged particle beams in the charged particle beam array that are scanning the test sample. In some embodiments, the system may generate a beammap 510 based on one or more identified failed charged particle beams and based on one or more identified valid charged particle beams that are scanning the test sample.

[0095] In some embodiments, the system can identify one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam in the charged particle beam array. For example, since each pixel of an image of a test sample generated from a valid charged particle beam has a corresponding grayscale value, and since the pattern on the test sample is predetermined, the system can identify whether the image was generated from a valid charged particle beam and whether any charged particle beams failed based on the grayscale histogram generated for each charged particle beam.

[0096] For example, a charged particle beam may fail due to defocusing or obstruction. The beam may become defocused due to misalignment of the MEMS component, or it may be obstructed due to misalignment or one or more particles blocking one or more of the beam. In some instances, the charged particle beam may fail due to discharge between the MEMS component area and other reference surfaces in the inspection system, which can result in large spots on the generated image.

[0097] In some embodiments, a dull, monotonous grayscale or a low (e.g., gradient) gradient across the entire generated image may indicate one or more failed charged particle beams.

[0098] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of an image generated by the charged particle beam array. For example, an image of a test sample generated by a valid charged particle beam has a corresponding sharpness or resolution. Since the pattern on the test sample is predetermined, the system can identify whether the image was generated by a valid charged particle beam and whether any charged particle beams have failed based on the sharpness or resolution of the generated image.

[0099] In some embodiments, the generated image of the test sample having a sharpness or resolution different from the expected (e.g. known) sharpness or resolution can indicate one or more failed charged particle beams.

[0100] In some embodiments, the system can formulate a scanning strategy based on the generated beam pattern 510, wherein the generated beam pattern 510 corresponds to the beam pattern of the scanned test sample. In some embodiments, the system can provide the test sample with one or more charged particle beams (e.g., effective charged particle beams 312, excluding any failed charged particle beams 514) according to the scanning strategy. Figure 2A The primary electron beam 202 of the multi-beam tool 104 Figure 2B The single-beam tool 100B (electron beam 161) is used to scan and inspect samples (e.g., Figure 2A Sample 208 Figure 2B (wafer 150). In some embodiments, the scanning strategy may include providing one or more charged particle beams to one or more regions of the test sample corresponding to an identified failed charged particle beam (e.g., failed charged particle beam 514). That is, the one or more charged particle beams used to scan the test sample may be a subset of the charged particle beams used to scan the test sample, wherein the subset does not include the failed charged particle beam, such that the test sample is scanned only by the valid charged particle beams. In some embodiments, the subset of charged particle beams may not include the failed charged particle beam and may not include some valid charged particle beams.

[0101] For example, the system can generate a beammap 515 based on the generated beammap 510. In some embodiments, beammap 515 may include a valid charged particle beam 512 and an identified failed charged particle beam 514. In some embodiments, beammap 515 may include an identified valid charged particle beam 512a, which may correspond to a valid charged particle beam excluded from the optimized beammap 530. For example, the identified valid charged particle beam 512a may be excluded from beammap 520, and therefore from the optimized beammap 530, to minimize the overlap of valid charged particle beams during the scanning of the sample.

[0102] In some embodiments, the system can generate a beammap 520 that includes only the effective charged particle beam 512. The system can generate an optimized beammap 530 by arranging multiple beammaps 520 within the field of view (FOV) to optimize the FOV arrangement of the effective charged particle beams. For example, by excluding the identified failed charged particle beam 514 and the identified effective charged particle beams from the beammap 520, the system can arrange the beammaps 520 within the FOV to generate the optimized beammap 530. In some embodiments, the optimized beammap 530 can be used to minimize the overlap of effective charged particle beams (e.g., different effective charged particle beams scanning the same region of the sample) during sample scanning.

[0103] By scanning the test sample and identifying failed charged particle beams before scanning the inspection sample, a robust scanning strategy can be developed to deliver an effective charged particle beam to the inspection sample with minimal overlap. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0104] Now for reference Figure 6A An exemplary beam array 610a with position labels, consistent with embodiments of this disclosure, is shown. The beam array 610a includes position labels 1 to M, where 1 corresponds to a charged particle beam in a first ring at the center of the beam array 610a, and M corresponds to a charged particle beam in an Mth ring surrounding the beam array 610a. Lower position labels correspond to rings of charged particle beams closer to the center of the beam array 610a. For example, position label 2 corresponds to a charged particle beam in a second ring from the center of the beam array 610a, and position label 3 corresponds to a charged particle beam in a third ring from the center of the beam array 610a.

[0105] It should be understood that the beam array 610a may include any number of charged particle beam loops (e.g., M loops), and the number of loops in the beam array is not limited to [specific number]. Figure 6A The number shown.

[0106] Now for reference Figure 6B An exemplary optimized bundle diagram 630b consistent with embodiments of this disclosure.

[0107] In some embodiments, the system can be based on a multi-beam array (e.g. Figure 3 The failed charged particle beam in the array 310) (e.g. Figure 3 Failure of charged particle beam 314 Figure 5 The location of the failed charged particle beam 514 is used to generate an optimized beammap (e.g., Figure 5 Optimized bundle diagram 530 Figure 6BOptimized beam pattern 630b). When a failed charged particle beam is located at the periphery of the multi-beam array (e.g., at position label M of beam array 610a), the system can optimize the scan during inspection by using the generated optimized beam pattern. In this case, the generated optimized beam pattern can optimize the scan because the multi-beam arrays can be arranged adjacent to each other in the FOV, minimizing the number of regions on the sample that are scanned more than once by the effective charged particle beams.

[0108] For example, as Figure 6B shown, an optimized beam pattern 630b can be generated to compensate for the failed charged particle beam 614b by overlapping the effective beam set 616b of the charged particle beam array, which includes the failed charged particle beam 614b (e.g., if the failed charged particle beam is at position label M, then M - 1 beams overlap in the generated optimized beam pattern) and the identified effective charged particle beams 612b (e.g., Figure 5 the identified effective charged particle beams 512a).

[0109] In some embodiments, the optimized beam pattern can be generated according to the following equations. Total number of rings = M (1) Total number of beams = (2)

[0110] If the failed charged particle beam is located on ring N of the beam array and 1 < N ≤ M, then the number of effective charged particle beams that overlap in the generated optimized beam pattern can be calculated by the following equation (3).

[0111] Now refer to Figure 6C , exemplary beam pattern 630c. When the failed charged particle beam is closer to or within the center of the multi-beam array, then generating a re-arranged beam pattern may not optimize the scan. For example, if the failed charged particle beam 614c is located at the center of the beam array (e.g., Figure 6A position label 1), then the beam pattern 630c will have M , , Figure 6C ,

[0112] ,

[0111] , , Figure 6A , Figure 6C , 2 , overlapping effective charged particle beams. As Figure 6C shown, if a new beam pattern 630c is generated based on the re-arranged multi-beam array where the failed charged particle beam 614c is located at the center of the multi-beam array, then during inspection, the effective charged particle beams 612c will scan more regions of the sample more than once, reducing the inspection throughput. The overlapping regions 640c and 650c correspond to the overlapping effective charged particle beams that scan the sample region of the multi-beam array more than once.

[0112] In embodiments where the failed charged particle beam is located at the center of a multi-beam array, the scan can be optimized by first scanning the sample with the multi-beam array and then scanning the region of the sample corresponding to the failed charged particle beam with a single effective charged particle beam in a second scan (as opposed to using a multi-beam tool in the second scan).

[0113] For example, return to Figure 6A If the failed charged particle beam is located at position tag 1, a multi-beam array can be used to scan the region of the sample corresponding to the effective charged particle beam in ring 2-M, and a single-beam tool can be used to scan the region of the sample corresponding to position tag 1. This method minimizes the number of regions on the sample that are scanned more than once by the effective charged particle beam, thereby improving inspection throughput.

[0114] Now for reference Figure 6D An exemplary graph 600d consistent with embodiments of this disclosure.

[0115] Graph 600d includes axis 610d, which corresponds to the presence of a failed charged particle beam (e.g., Figure 3 Failure of charged particle beam 314 Figure 4 Failure of charged particle beam 414 Figure 5 Failure of charged particle beam 514 Figure 6B Failed charged particle beam 614b Figure 6C The ring position of the beam array of the failed charged particle beam 614c (e.g.) Figure 6A The label position is 1-M). The curve 600d includes axis 620d, which corresponds to the effective scan area of ​​the sample (e.g., the area of ​​the sample scanned by the effective charged particle beam) when a beam map is generated by arranging a beam array in the FOV.

[0116] As shown in curve 600d, and in conjunction with the above... Figure 6A , Figure 6B and Figure 6C Consistently, when the beam array includes a failed charged particle beam at or near the center of the beam array (e.g., at position 1 on axis 620d), the effective scan area of ​​the sample is lower than when the beam array has a failed charged particle beam at or near the periphery of the beam array (e.g., at position 31 on axis 6200d).

[0117] For example, when using Figure 6C When the beammap 630c scans the sample, data point 630d can correspond to the effective scanning area of ​​the sample, while when using... Figure 6B When the beam pattern 630b scans the sample, data point 640d can correspond to the effective scanning area of ​​the sample.

[0118] In some embodiments, by using identified failed charged particle beams and identified effective charged particle beams (e.g. Figure 5 The system calculates the predicted effective scanning area of ​​the identified effective charged particle beam 512a). The system can generate a beammap by arranging a beam array within the FOV, as described above for... Figure 5 and Figure 6B Described.

[0119] For example, if the predicted effective scan area is below a certain threshold (e.g., below a certain percentage of the effective scan area), the system may not generate a beammap by arranging a beam array within the FOV. For instance, the system could perform a first scan of the sample using a multi-beam array, and then in a second scan, scan the region of the sample corresponding to the failed charged particle beam using a single effective charged particle beam (e.g., using a multi-beam tool, using a single-beam tool, etc.). Figure 6C This can correspond to examples where the predicted effective scan area is below a certain threshold.

[0120] If the predicted effective scan area exceeds a certain threshold (e.g., exceeds a certain percentage of the effective scan area), the system may not generate an optimized beammap by arranging the beam array within the FOV. For example, Figure 5 and Figure 6B This can correspond to examples where the predicted effective scan area is higher than a certain threshold.

[0121] Now for reference Figure 7 An exemplary process 700 for optimizing sample scanning, consistent with embodiments of this disclosure.

[0122] In the first change of process 700, in step 702, the system (e.g.) Figure 1 The controller 109 can be provided with multiple charged particle beams (e.g., for scanning the first sample) to scan the first sample. Figure 2A The primary electron beam 202 is used to scan the first sample (e.g., Figure 2A Sample 208). The first sample can be a test sample with a predetermined pattern, and the second sample can be an inspection sample. For example, the test sample can have a predetermined pattern such that the system knows the pattern, and each charged particle beam scans the same feature on the test sample.

[0123] In step 704, the system can identify the array of charged particle beams (e.g., Figure 3 One or more failed charged particle beams (e.g., array 310) in the array 310 Figure 3 Failure of charged particle beam 314 Figure 5 Failure of charged particle beam 514 Figure 6B The failed charged particle beam 614b).

[0124] In step 706, the system may identify one or more valid charged particle beams (e.g., the identified ones) of the scanned test sample in the charged particle beam array. Figure 5 The effective charged particle beam 512a, as indicated Figure 6B The effective charged particle beam 612b). In some embodiments, the identified effective charged particle beam may correspond to an effective charged particle beam excluded from the optimized beammap. For example, some identified effective charged particle beams may be excluded from the optimized beammap to minimize the overlap of effective charged particle beams during sample scanning (e.g., Figure 6B The effective bundle set 616b).

[0125] In step 708, the system can generate a beammap based on one or more identified failed charged particle beams and one or more identified valid charged particle beams (e.g., Figure 3 Bundle diagram 300, Figure 5 (Bundle diagram 510), wherein the generated bundle diagram does not include one or more identified failed charged particle bundles.

[0126] In some embodiments, the system can identify one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam in the charged particle beam array. For example, since each pixel of an image of a test sample generated from a valid charged particle beam has a corresponding grayscale value, and since the pattern on the test sample is predetermined, the system can identify whether the image was generated from a valid charged particle beam and whether any charged particle beams failed based on the grayscale histogram generated for each charged particle beam.

[0127] For example, a charged particle beam may fail due to defocusing or obstruction. The beam may become defocused due to misalignment of the MEMS component, or it may be blocked by one or more particles obstructing one or more beams. In some instances, the charged particle beam may fail due to discharge between the MEMS component area and other reference surfaces in the inspection system, which can result in large spots on the generated image.

[0128] In some embodiments, a dull, monotonous grayscale or a low (e.g., gradient) gradient across the entire generated image may indicate one or more failed charged particle beams.

[0129] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of an image generated by the charged particle beam array. For example, an image of a test sample generated by a valid charged particle beam has a corresponding sharpness or resolution. Since the pattern on the test sample is predetermined, the system can identify whether the image was generated by a valid charged particle beam and whether any charged particle beams have failed based on the sharpness or resolution of the generated image.

[0130] In some embodiments, the generated image of the test sample having a sharpness or resolution different from the expected (e.g. known) sharpness or resolution can indicate one or more failed charged particle beams.

[0131] In some embodiments, as described above in step 706, effective charged particle beams can be excluded from the optimized beammap to minimize overlap of effective charged particle beams during sample scanning. By excluding identified failed charged particle beams and identifying effective charged particle beams from the beammap, the optimized beammap can be used to minimize overlap of effective charged particle beams during sample scanning.

[0132] In step 710, the system can formulate a scanning strategy based on the generated beammap to provide the inspection sample with one or more charged particle beams (e.g., excluding any failed charged particle beams and any identified valid charged particle beams) according to the scanning strategy. Figure 3 Effective charged particle beam 312 Figure 5 Effective charged particle beam 512 Figure 6B Effective charged particle beam 612b Figure 2A Primary electron beam 202, Figure 2B The electron beam 161 is used to scan and examine the sample (e.g., Figure 2A Sample 208 of the multi-beam tool 104 Figure 2B (Wafer 150 of the single-beam tool 100B). In some embodiments, the scanning strategy may include providing one or more charged particle beams to one or more regions of the test sample corresponding to identified failed charged particle beams. That is, the one or more charged particle beams used to scan the test sample may be a subset of the charged particle beams used to scan the test sample, wherein the subset does not include failed charged particle beams, such that the test sample is scanned only by valid charged particle beams.

[0133] By scanning the test sample and identifying failed charged particle beams before scanning the inspection sample, a robust scanning strategy can be developed to deliver an effective charged particle beam to the inspection sample with minimal overlap. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher quality images, detect defects in the sample, and improve the throughput of the inspection system.

[0134] In the second variation of process 700, in step 702, the system can provide a sample for scanning inspection (e.g. Figure 2A Multiple beams of charged particles (sample 208) are used to scan the inspection sample without pre-scanning the test sample or generating a beam pattern. The system may not know the pattern of the inspection sample.

[0135] In step 704, during the inspection scan of the sample, the system can identify the array of charged particle beams (e.g., Figure 3 One or more failed charged particle beams (e.g., array 310) in the array 310 Figure 3 The failed charged particle beam 314).

[0136] In step 706, the system can identify one or more valid charged particle beams of the scanned test sample in the charged particle beam array.

[0137] In step 708, the system can generate a beammap based on one or more identified failed charged particle beams and one or more identified valid charged particle beams (e.g., Figure 3 The generated beam diagram (300) does not include one or more identified failed charged particle beams.

[0138] In some embodiments, the system can identify one or more failed charged particle beams by generating grayscale histograms for multiple generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, the system can analyze one or more images from the same charged particle beam to determine whether the grayscale histogram changes at different locations on the inspected sample. Based on the system's analysis of one or more images from one or more charged particle beams, the system can identify valid charged particle beams and any failed charged particle beams.

[0139] In some embodiments, grayscale histograms showing little or no variation at different locations on an inspected sample for a charged particle beam can indicate one or more failed charged particle beams.

[0140] In some embodiments, the system can identify one or more failed charged particle beams by determining the sharpness or resolution of one or more generated images associated with one or more charged particle beams in an array of charged particle beams used during the inspection scan. For example, an image of an inspection sample generated due to a valid charged particle beam may have sharp edges (e.g., corresponding to a pattern scanned by a valid charged particle beam). An image of an inspection sample generated due to a failed charged particle beam may not have sharp edges.

[0141] In step 710, the system can formulate a scanning strategy based on the generated beammap to provide the inspection sample with one or more charged particle beams that do not include any failed charged particle beams (e.g., ...). Figure 3 An effective charged particle beam 312 is used to scan and inspect samples (e.g., Figure 2A Sample 208 Figure 2BThe wafer 150). In some embodiments, the scanning strategy may include providing one or more charged particle beams (e.g., to one or more regions of the sample being inspected that correspond to the identified failed charged particle beams) Figure 2A The primary electron beam 202 of the multi-beam tool 104 Figure 2B The single-beam tool 100B uses an electron beam 161 to rescan the inspection sample. That is, one or more charged particle beams used to rescan the inspection sample can be a subset of the charged particle beams used for the initial scan of the inspection sample, wherein this subset does not include failed charged particle beams, such that the inspection sample is scanned only by valid charged particle beams. In this variation of process 700, the second sample is the first sample.

[0142] By scanning the sample during inspection and identifying failed charged particle beams during inspection, a high-throughput scanning strategy can be developed, allowing areas of the sample scanned with failed charged particle beams to be rescanned with effective charged particle beams. Therefore, the disclosed embodiments can reduce image capture rate loss and identify defects in the sample during inspection. As a result, the disclosed embodiments can generate higher-quality images, detect defects in samples, and improve the throughput of the inspection system.

[0143] Consistent with embodiments of this disclosure, a non-transitory computer-readable medium may be provided that stores information for a controller (e.g., Figure 1 The controller 109) contains instructions for its processor to control electron beam tools or other systems and other systems or components of the server. These instructions may allow one or more processors to perform image processing, data processing, beam scanning, graphic display, operation of charged particle beam devices or other imaging equipment, etc., to provide the above-mentioned... Figures 3 to 5 The operation is consistent with that described in 7. In some embodiments, a non-transitory computer-readable medium may be provided storing instructions for a processor to perform the steps of process 700. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, compact disc read-only memory (CD-ROM), any other optical data storage media, any physical media with a perforated pattern, random access memory (RAM), programmable read-only memory (PROM) and erasable programmable read-only memory (EPROM), flash memory EPROM or any other flash memory, non-volatile random access memory (NVRAM), cache, registers, any other memory chips or cassette tapes and their networking versions.

[0144] The embodiments may be further described using the following terms: 1. A method for optimizing sample scanning, comprising: Provides multiple beams of charged particles for scanning the first sample; Identify one or more failed charged particle beams among multiple charged particle beams; Identify one or more effective charged particle beams among multiple charged particle beams used to scan the first sample; A beammap is generated based on one or more identified failed charged particle beams and one or more identified valid charged particle beams, wherein the generated beammap does not include the one or more identified failed charged particle beams; and Based on the generated beammap, a scanning strategy is developed for scanning the first or second sample. 2. The method according to clause 1, wherein identifying one or more failed charged particle beams includes generating a grayscale histogram for each of a plurality of charged particle beams. 3. The method according to any one of clauses 1 to 2, wherein identifying one or more failed charged particle beams includes determining either the sharpness or resolution of an image generated by the plurality of charged particle beams. 4. The method according to clause 1, wherein identifying one or more failed charged particle beams includes generating grayscale histograms for multiple images associated with the first charged particle beam to determine whether the first charged particle beam is failed. 5. The method according to any one of clauses 1 or 4, wherein identifying one or more failed charged particle beams includes determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam to determine whether the first charged particle beam is failed. 6. The method according to any one of clauses 1 to 3, wherein the first sample has a predetermined pattern. 7. The method according to any one of clauses 1 to 3 or 6, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 8. The method according to any one of clauses 1 to 3 or 6 to 7, wherein the generated beammap is used to minimize the overlap of the effective charged particle beams during the scanning of the second sample. 9. The method according to any one of clauses 1 to 3 or 6 to 8, wherein the scanning strategy includes using the generated beammap to scan the second sample. 10. The method according to any one of clauses 1 to 3 or 6 to 9, wherein the generated beammap is used to maximize the scanning of the second sample. 11. The method according to any one of clauses 1 to 3 or 6 to 10, wherein the scanning strategy includes providing a charged particle beam to a region of the second sample corresponding to one or more identified failed charged particle beams. 12. The method according to any one of clauses 1, 4 or 5, wherein the scanning strategy includes providing a charged particle beam to a region of the first sample corresponding to one or more identified failed charged particle beams. 13. A method for optimizing sample scanning, comprising: Scan the first sample; Beammaps are generated based on one or more scanned failed charged particle beams and one or more scanned effective charged particle beams. Based on the generated beammap, a scanning strategy is formulated; and The first or second sample is scanned according to the scanning strategy. 14. The method according to clause 13 further includes identifying one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam associated with a scan of the first sample among a plurality of charged particle beams. 15. The method according to any one of clauses 13 to 14 further includes identifying one or more failed charged particle beams by determining either the sharpness or resolution of images generated from a plurality of charged particle beams associated with a scan of the first sample. 16. The method according to clause 13 further includes identifying one or more failed charged particle beams by generating grayscale histograms for multiple images associated with the first charged particle beam to determine whether the first charged particle beam has failed. 17. The method according to any one of clauses 13 or 16 further includes identifying one or more failed charged particle beams by determining whether the first charged particle beam is failed by determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam. 18. The method according to any one of clauses 13 to 15, wherein the first sample has a predetermined pattern. 19. The method according to any one of clauses 13 to 15 or 18, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 20. The method according to any one of clauses 13 to 15 or 18 to 19, wherein the generated beammap is used to minimize the overlap of the effective charged particle beams during the scanning of the second sample. 21. The method according to any one of clauses 13 to 15 or 18 to 20, wherein the scanning strategy includes using the generated beammap to scan the second sample. 22. The method according to any one of clauses 13 to 15 or 18 to 21, wherein the generated beammap is used to maximize the scanning of the second sample. 23. The method according to any one of clauses 13 to 15 or 18 to 22, wherein the scanning strategy includes providing a charged particle beam to a region of the second sample corresponding to one or more identified failed charged particle beams. 24. The method according to any one of clauses 13, 16 or 17, wherein the scanning strategy includes providing a charged particle beam to a region of the first sample corresponding to one or more identified failed charged particle beams. 25. A method for optimizing sample scanning, comprising: Identify one or more failed beams in a multi-beam system; Identify one or more valid beams in a multi-beam system; A bundle diagram is generated based on identifying one or more failed bundles and one or more valid bundles; and The scanning strategy for the sample is determined based on the generated beammap. 26. The method according to clause 25, wherein identifying one or more failed beams in a multi-beam system comprises generating a grayscale histogram for each of the plurality of beams associated with a scan of a first sample. 27. The method according to any one of clauses 25 to 26, wherein determining one or more failed beams in a multi-beam system comprises determining either the sharpness or resolution of images generated by the multiple beams associated with the scanning of the first sample. 28. The method according to clause 25, wherein determining one or more failed beams in a multi-beam system comprises generating grayscale histograms for multiple images associated with a first beam to determine whether the first beam is failed. 29. The method according to any one of clauses 25 or 28, wherein determining one or more failed beams in a multi-beam system comprises determining any one of the sharpness or resolution of a plurality of images associated with the first beam to determine whether the first beam is failed. 30. The method according to any one of clauses 25 to 27, wherein the sample has a predetermined pattern. 31. The method according to any one of clauses 25 to 27 or 30, wherein the generated bundle diagram does not include some of the failed bundles. 32. The method according to any one of clauses 25 to 27 or 30 to 31, wherein the generated beammap is used to minimize the overlap of effective beams during the scanning of the sample. 33. The method according to any one of clauses 25 to 27 or 30 to 32, wherein the scanning strategy includes scanning the sample using the generated beammap. 34. The method according to any one of clauses 25 to 27 or 30 to 33, wherein the generated beammap is used to maximize the scanning of the sample. 35. The method according to any one of clauses 25 to 27 or 30 to 33, wherein the scanning strategy includes providing a beam to a region of the sample corresponding to one or more identified failed beams. 36. The method according to any one of clauses 25, 28 or 29, wherein the scanning strategy includes providing a beam to a region of the sample corresponding to one or more identified failed beams. 37. A system for optimizing sample scanning, comprising a controller including circuitry configured to cause the system to perform the following operations: Provides multiple beams of charged particles for scanning the first sample; Identify one or more failed charged particle beams among multiple charged particle beams; Identify one or more effective charged particle beams among multiple charged particle beams used to scan the first sample; A beammap is generated based on one or more identified failed charged particle beams and one or more identified valid charged particle beams, wherein the generated beammap does not include the one or more identified failed charged particle beams; and Based on the generated beammap, a scanning strategy is developed for scanning the first or second sample. 38. A system according to clause 37, wherein identifying one or more failed charged particle beams includes generating a grayscale histogram for each of a plurality of charged particle beams. 39. A system according to any one of clauses 37 to 38, wherein identifying one or more failed charged particle beams includes determining either the sharpness or resolution of an image generated by the plurality of charged particle beams. 40. The system according to clause 37, wherein identifying one or more failed charged particle beams includes generating grayscale histograms for multiple images associated with the first charged particle beam to determine whether the first charged particle beam is failed. 41. A system according to any one of clauses 37 or 40, wherein identifying one or more failed charged particle beams includes determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam to determine whether the first charged particle beam is failed. 42. A system according to any one of clauses 37 to 39, wherein the first sample has a predetermined pattern. 43. A system according to any one of clauses 37 to 39 or 42, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 44. A system according to any one of clauses 37 to 39 or 42 to 43, wherein the generated beammap is used to minimize the overlap of the effective charged particle beams during scanning of the second sample. 45. A system according to any one of clauses 37 to 39 or 42 to 44, wherein the scanning strategy includes using the generated beammap to scan the second sample. 46. ​​A system according to any one of clauses 37 to 39 or 42 to 45, wherein the generated beammap is used to maximize the scanning of a second sample. 47. A system according to any one of clauses 37 to 39 or 42 to 46, wherein the scanning strategy includes providing a charged particle beam to a region of a second sample corresponding to one or more identified failed charged particle beams. 48. A system according to any one of clauses 37, 40 or 41, wherein the scanning strategy includes providing a charged particle beam to a region of a first sample corresponding to one or more identified failed charged particle beams. 49. A system for optimizing sample scanning, comprising a controller including circuitry configured to cause the system to perform the following operations: Scan the first sample; Beammaps are generated based on one or more scanned failed charged particle beams and one or more scanned effective charged particle beams. Based on the generated beammap, a scanning strategy is formulated; and The first or second sample is scanned according to the scanning strategy. 50. The system according to clause 49 further includes identifying one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam associated with a scan of the first sample among a plurality of charged particle beams. 51. The system according to any one of clauses 49 to 50 further includes identifying one or more failed charged particle beams by determining either the sharpness or resolution of images generated from a plurality of charged particle beams associated with a scan of the first sample. 52. The system according to clause 49 further includes identifying one or more failed charged particle beams by generating grayscale histograms for multiple images associated with the first charged particle beam to determine whether the first charged particle beam has failed. 53. The system according to any one of clauses 49 or 52 further includes identifying one or more failed charged particle beams by determining whether the first charged particle beam is failed by determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam. 54. The system according to any one of clauses 49 to 51, wherein the first sample has a predetermined pattern. 55. A system according to any one of clauses 49 to 51 or 54, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 56. A system according to any one of clauses 49 to 51 or 54 to 55, wherein the generated beammap is used to minimize the overlap of the effective charged particle beams during the scanning of the second sample. 57. A system according to any one of clauses 49 to 51 or 54 to 56, wherein the scanning strategy includes using the generated beammap to scan the second sample. 58. A system according to any one of clauses 49 to 51 or 54 to 57, wherein the generated beammap is used to maximize the scanning of the second sample. 59. A system according to any one of clauses 49 to 51 or 54 to 58, wherein the scanning strategy includes providing a charged particle beam to a region of a second sample corresponding to one or more identified failed charged particle beams. 60. A system according to any one of clauses 49, 52 or 53, wherein the scanning strategy includes providing a charged particle beam to a region of a first sample corresponding to one or more identified failed charged particle beams. 61. A system for optimizing sample scanning, comprising a controller including circuitry configured to cause the system to perform the following operations: Identify one or more failed beams in a multi-beam system; Identify one or more valid beams in a multi-beam system; A bundle diagram is generated based on identifying one or more failed bundles and one or more valid bundles; and The scanning strategy for the sample is determined based on the generated beammap. 62. The system according to clause 61, wherein determining one or more failed beams in a multi-beam system comprises generating a grayscale histogram for each of the plurality of beams associated with a scan of a first sample. 63. The system according to any one of clauses 61 to 62, wherein determining one or more failed beams in a multi-beam system includes determining either the sharpness or resolution of images generated by the multiple beams associated with the scanning of the first sample. 64. The system according to clause 61, wherein determining one or more failed beams in a multi-beam system comprises generating grayscale histograms for multiple images associated with a first beam to determine whether the first beam is failed. 65. A system according to any one of clauses 61 or 64, wherein determining the failure of one or more beams in a multi-beam system comprises determining the sharpness or resolution of any one of a plurality of images associated with the first beam to determine whether the first beam is failure. 66. A system according to any one of clauses 61 to 63, wherein the sample has a predetermined pattern. 67. A system according to any one of clauses 61 to 63 or 66, wherein the generated bundle diagram does not include some of the failed one or more bundles. 68. A system according to any one of clauses 61 to 63 or 66 to 67, wherein the generated beammap is used to minimize the overlap of effective beams during the scanning of the sample. 69. A system according to any one of clauses 61 to 64 or 66 to 68, wherein the scanning strategy includes using a generated beammap to scan the sample. 70. A system according to any one of clauses 61 to 63 or 66 to 69, wherein the generated beammap is used to maximize the scanning of the sample. 71. A system according to any one of clauses 61 to 63 or 66 to 69, wherein the scanning strategy includes providing a beam to a region of the sample corresponding to one or more identified failed beams. 72. A system according to any one of clauses 61, 64 or 65, wherein the scanning strategy includes providing a beam to a region of the sample corresponding to one or more identified failed beams. 73. A non-transitory computer-readable medium storing an instruction set executable by at least one processor of a computing device to cause the computing device to perform a method for optimizing a sample scan, the method comprising: Provides multiple beams of charged particles for scanning the first sample; Identify one or more failed charged particle beams among multiple charged particle beams; Identify one or more effective charged particle beams among multiple charged particle beams used to scan the first sample; A beammap is generated based on one or more identified failed charged particle beams and one or more identified valid charged particle beams, wherein the generated beammap does not include the one or more identified failed charged particle beams; and Based on the generated beammap, a scanning strategy is developed for scanning the first or second sample. 74. A non-transitory computer-readable medium according to clause 73, wherein identifying one or more failed charged particle beams includes generating a grayscale histogram for each of a plurality of charged particle beams. 75. A non-transitory computer-readable medium according to any one of clauses 73 to 74, wherein identifying one or more failed charged particle beams includes determining either the sharpness or resolution of an image generated by the plurality of charged particle beams. 76. A non-transitory computer-readable medium according to clause 73, wherein identifying one or more failed charged particle beams includes generating grayscale histograms for multiple images associated with a first charged particle beam to determine whether the first charged particle beam has failed. 77. A non-transitory computer-readable medium according to any one of clauses 73 or 76, wherein identifying one or more failed charged particle beams includes determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam to determine whether the first charged particle beam has failed. 78. A non-transitory computer-readable medium according to any one of clauses 73 to 75, wherein a first sample has a predetermined pattern. 79. A non-transitory computer-readable medium according to any one of clauses 73 to 75 or 78, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 80. A non-transient computer-readable medium according to any one of clauses 73 to 75 or 78 to 79, wherein the generated beammap is used to minimize the overlap of effective charged particle beams during scanning of a second sample. 81. A non-transitory computer-readable medium according to any one of clauses 73 to 75 or 78 to 80, wherein the scanning strategy includes using a generated beammap to scan a second sample. 82. A non-transitory computer-readable medium according to any one of clauses 73 to 75 or 78 to 81, wherein the generated beammap is used to maximize the scanning of a second sample. 83. A non-transitory computer-readable medium according to any one of clauses 73 to 75 or 78 to 82, wherein the scanning strategy includes providing a charged particle beam to a region of a second sample corresponding to one or more identified failed charged particle beams. 84. A non-transitory computer-readable medium according to any one of clauses 73, 76 or 77, wherein the scanning strategy includes providing a charged particle beam to a region of a first sample corresponding to one or more identified failed charged particle beams. 85. A non-transitory computer-readable medium storing an instruction set executable by at least one processor of a computing device to cause the computing device to perform a method for optimizing a sample scan, the method comprising: Scan the first sample; Beammaps are generated based on one or more scanned failed charged particle beams and one or more scanned effective charged particle beams. Based on the generated beammap, a scanning strategy is formulated; and The first or second sample is scanned according to the scanning strategy. 86. The non-transitory computer-readable medium according to clause 85 further includes identifying one or more failed charged particle beams by generating a grayscale histogram for each charged particle beam associated with a scan of the first sample among a plurality of charged particle beams. 87. The non-transitory computer-readable medium according to any one of clauses 85 to 86 further includes identifying one or more failed charged particle beams by determining any one of the sharpness or resolution of images generated from a plurality of charged particle beams associated with a scan of the first sample. 88. The non-transitory computer-readable medium according to clause 85 further includes identifying one or more failed charged particle beams by generating grayscale histograms for multiple images associated with the first charged particle beam to determine whether the first charged particle beam has failed. 89. The non-transitory computer-readable medium according to any one of clauses 85 or 88 further includes identifying one or more failed charged particle beams by determining whether the first charged particle beam is failed by determining any one of the sharpness or resolution of a plurality of images associated with the first charged particle beam. 90. A non-transitory computer-readable medium according to any one of clauses 85 to 87, wherein a first sample has a predetermined pattern. 91. A non-transitory computer-readable medium according to any one of clauses 85 to 87 or 90, wherein the generated beam diagram does not include some of the identified one or more valid charged particle beams. 92. A non-transient computer-readable medium according to any one of clauses 85 to 87 or 90 to 91, wherein the generated beammap is used to minimize the overlap of effective charged particle beams during scanning of a second sample. 93. A non-transitory computer-readable medium according to any one of clauses 85 to 87 or 90 to 92, wherein the scanning strategy includes using a generated beammap to scan a second sample. 94. A non-transitory computer-readable medium according to any one of clauses 85 to 87 or 90 to 93, wherein the generated beammap is used to maximize the scanning of a second sample. 95. A non-transitory computer-readable medium according to any one of clauses 85 to 87 or 90 to 94, wherein the scanning strategy includes providing a charged particle beam to a region of a second sample corresponding to one or more identified failed charged particle beams. 96. A non-transitory computer-readable medium according to any one of clauses 85, 88 or 89, wherein the scanning strategy includes providing a charged particle beam to a region of a first sample corresponding to one or more identified failed charged particle beams. 97. A non-transitory computer-readable medium storing an instruction set executable by at least one processor of a computing device to cause the computing device to perform a method for optimizing a sample scan, the method comprising: Identify one or more failed beams in a multi-beam system; Identify one or more valid beams in a multi-beam system; A bundle diagram is generated based on identifying one or more failed bundles and one or more valid bundles; and The scanning strategy for the sample is determined based on the generated beammap. 98. A non-transitory computer-readable medium according to clause 97, wherein determining one or more failed beams in a multi-beam system comprises generating a grayscale histogram for each of the plurality of beams associated with a scan of a first sample. 99. A non-transitory computer-readable medium according to any one of clauses 97 to 98, wherein determining the failure of one or more beams in a multi-beam system includes determining either the sharpness or resolution of images generated by the multiple beams associated with a scan of a first sample. 100. A non-transitory computer-readable medium according to clause 97, wherein determining one or more failed beams in a multi-beam system comprises generating grayscale histograms for a plurality of images associated with a first beam to determine whether the first beam has failed. 101. A non-transitory computer-readable medium according to any one of clauses 97 or 100, wherein determining the failure of one or more beams in a multi-beam system comprises determining any one of the sharpness or resolution of a plurality of images associated with a first beam to determine whether the first beam has failed. 102. A non-transitory computer-readable medium according to any one of clauses 97 to 99, wherein the sample has a predetermined pattern. 103. A non-transitory computer-readable medium according to any one of clauses 97 to 99 or 102, wherein the generated bundle diagram does not include some of the failed one or more bundles. 104. A non-transient computer-readable medium according to any one of clauses 97 to 99 or 102 to 103, wherein the generated beammap is used to minimize the overlap of effective beams during the scanning of the sample. 105. A non-transitory computer-readable medium according to any one of clauses 97 to 99 or 102 to 104, wherein the scanning strategy includes scanning the sample using a generated beammap. 106. A non-transient computer-readable medium according to any one of clauses 97 to 99 or 102 to 105, wherein the generated beammap is used to maximize the scanning of the sample. 107. A non-transitory computer-readable medium according to any one of clauses 97 to 99 or 102 to 105, wherein the scanning strategy includes providing a bundle to a region of the sample corresponding to one or more bundles of identified failure. 108. A non-transitory computer-readable medium according to any one of clauses 97, 100 or 101, wherein the scanning strategy includes providing a bundle to a region of the sample corresponding to one or more bundles of identified failure. 109. The method according to clause 18 further includes identifying a higher priority region and a lower priority region of the first sample based on a predetermined pattern. 110. The method according to clause 109, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed charged particle beams scan a lower priority region of the second sample, and one or more effective charged particle beams scan a higher priority region of the second sample. 111. The method according to clause 30 further includes identifying higher priority areas and lower priority areas of the sample based on a predetermined pattern. 112. The method according to clause 111, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed beams scan the lower priority regions of the sample, and one or more valid beams scan the higher priority regions of the sample. 113. The system according to clause 54, wherein the controller of the circuit system is configured to enable the system to further perform the identification of a higher priority region and a lower priority region of the first sample based on a predetermined pattern. 114. The system according to clause 113, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed charged particle beams scan a lower priority region of the second sample, and one or more effective charged particle beams scan a higher priority region of the second sample. 115. The system according to clause 60, wherein the controller of the circuit system is configured to enable the system to further perform the identification of higher priority areas and lower priority areas of the sample based on a predetermined pattern. 116. The system according to clause 115, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed beams scan the lower priority regions of the sample, and one or more valid beams scan the higher priority regions of the sample. 117. A non-transitory computer-readable medium according to clause 90, wherein a set of instructions executable by at least one processor of a computing device causes the computing device to further execute a higher priority region of a first sample and a lower priority region of the first sample based on a predetermined pattern. 118. A non-transitory computer-readable medium according to clause 117, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed charged particle beams scan a lower priority region of the second sample, and one or more effective charged particle beams scan a higher priority region of the second sample. 119. A non-transitory computer-readable medium according to clause 102, wherein a set of instructions executable by at least one processor of a computing device causes the computing device to further execute a higher priority region of a sample and a lower priority region of the sample based on a predetermined pattern. 120. A non-transitory computer-readable medium according to clause 119, wherein the scanning strategy includes adjusting the generated beammap such that one or more failed beams scan the lower priority regions of the sample, and one or more valid beams scan the higher priority regions of the sample.

[0145] It should be understood that the embodiments of this disclosure are not limited to the precise constructions described above and illustrated in the accompanying drawings, and various modifications and changes may be made without departing from its scope.

Claims

1. A system for optimizing sample scanning, comprising a controller, the controller including circuitry configured to cause the system to perform the following operations: Provides multiple beams of charged particles for scanning the first sample; Identify one or more failed charged particle beams among the plurality of charged particle beams; Identify one or more effective charged particle beams among the plurality of charged particle beams that scan the first sample; A beammap is generated based on one or more identified failed charged particle beams and one or more identified valid charged particle beams, wherein the generated beammap does not include the one or more identified failed charged particle beams; and A scanning strategy for scanning the first or second sample is formulated based on the generated beammap.

2. The system of claim 1, wherein identifying the one or more failed charged particle beams comprises: Generate a grayscale histogram for each of the plurality of charged particle beams.

3. The system of claim 1, wherein identifying the one or more failed charged particle beams comprises: Determine either the sharpness or resolution of the image generated by the plurality of charged particle beams.

4. The system of claim 1, wherein identifying the one or more failed charged particle beams comprises: Gray-scale histograms are generated for multiple images associated with the first charged particle beam to determine whether the first charged particle beam has failed.

5. The system of claim 1, wherein identifying the one or more failed charged particle beams comprises: Determine the sharpness or resolution of any one of the multiple images associated with the first charged particle beam to determine whether the first charged particle beam has failed.

6. The system according to claim 1, wherein the first sample has a predetermined pattern.

7. The system of claim 1, wherein generating the beammap does not include some of the identified effective charged particle beams from one or more effective charged particle beams.

8. The system of claim 1, wherein the generated beammap is used to minimize the overlap of effective charged particle beams during scanning of the second sample.

9. The system of claim 1, wherein the scanning strategy includes scanning the second sample using the generated beammap.

10. The system of claim 1, wherein the generated beammap is used to maximize the scanning of the second sample.

11. The system according to claim 1, wherein the scanning strategy includes: A charged particle beam is provided to the region of the second sample corresponding to one or more identified failed charged particle beams.

12. The system according to claim 1, wherein the scanning strategy includes: A charged particle beam is provided to the region of the first sample corresponding to one or more identified failed charged particle beams.

13. A system for optimizing sample scanning, comprising a controller, the controller including circuitry configured to cause the system to perform the following operations: Scan the first sample; A beammap is generated based on one or more failed charged particle beams and one or more effective charged particle beams from the scan. The scanning strategy is formulated based on the generated beammap; as well as The first sample or the second sample is scanned according to the scanning strategy.

14. The system of claim 13, further comprising identifying the one or more failed charged particle beams by generating a grayscale histogram for each of the plurality of charged particle beams associated with the scan of the first sample.

15. The system of claim 13, further comprising identifying the one or more failed charged particle beams by determining any one of the following: the sharpness or resolution of the image generated due to the plurality of charged particle beams associated with the scan of the first sample.

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