Self-referencing health monitoring system for multibeam SEM tools

Abstract

Systems and methods for image enhancement are disclosed. The method for enhancing an image can include receiving records (502, 506) of performance metrics for multiple beams of a multi-beam system during an imaging process, each record associated with a beam. The method can also include determining whether a beam anomaly has occurred based on a baseline value (504) determined using a portion of the records (502). The method can further include providing an indication of the anomaly in response to determining that an anomaly has occurred.

Description

Technical Field

[0001] Cross - reference to Related Applications

[0001] This application claims priority to U.S. Patent Application No. 62 / 893,139, filed on August 28, 2019, which is incorporated herein by reference in its entirety.

[0002]

[0002] The present description relates to the field of charged particle beam systems, and more specifically, to the integrity monitoring for a multi - beam scanning electron microscope (SEM) system.

Background Art

[0003]

[0003] In the manufacturing process of integrated circuits (ICs), unfinished or completed circuit components are inspected to ensure that they are manufactured as designed and are free of defects. Inspection systems using charged particle (e.g., electron) beam microscopes such as scanning electron microscopes (SEMs) or optical microscopes may be used. An SEM delivers low - energy electrons (e.g., less than 1 keV) to the surface and uses a detector to record secondary electrons or backscattered electrons leaving the surface. By recording such electrons at different excitation positions on the surface, an image can be generated with a spatial resolution on the order of nanometers.

[0004]

[0004] An SEM can be a single - beam system or a multi - beam system. A single - beam SEM uses a single electron beam to scan the surface, while a multi - beam SEM uses multiple electron beams to scan the surface simultaneously. A multi - beam system can achieve a higher imaging throughput compared to a single - beam system. However, a multi - beam system has a more complex structure, and thus lacks some structural flexibility compared to a single - beam system. Also, due to being more complex, a multi - beam SEM is more prone to operating errors and malfunctions compared to a single - beam SEM.

Summary of the Invention

[0005]

[0005] Embodiments of the present disclosure provide systems and methods for image improvement. In some embodiments, a method for monitoring the performance of a multibeam system may include receiving a record of performance metrics for multiple beams of the multibeam system during the imaging process, each record associated with a particular beam. The method may also include determining whether a beam anomaly is occurring based on a baseline value determined using a portion of the record. The method may further include providing an anomaly notification in response to a determination that an anomaly is occurring.

[0006]

[0006] In some embodiments, a system for monitoring the performance of a multibeam system is disclosed. This system may include a controller having a circuit for executing a set of commands. The controller can execute a set of commands to cause the system to receive a record of performance metrics for multiple beams of the multibeam system in the imaging process, each of which is associated with a particular beam. The controller can also execute a set of commands to cause the system to determine whether a beam anomaly has occurred, based on a baseline value determined using a portion of the records. The controller can also execute a set of commands in response to a determination that an anomaly has occurred to cause the system to provide an anomaly alert.

[0007]

[0007] In some embodiments, a non-temporary computer-readable medium is disclosed. The non-temporary computer-readable medium may store a set of instructions that can be executed by at least one processor of the device to cause the device to perform a method for monitoring the performance of a multibeam system. This method may include receiving a record of performance metrics for multiple beams of the multibeam system in an imaging process, each of which is associated with a particular beam. This method may also include determining whether a beam anomaly is occurring based on a baseline value determined using a portion of the record. This method may further include providing an anomaly notification in response to a determination that an anomaly is occurring.

[0008]

[0008] In some embodiments, a method for monitoring the health of a multibeam system may include receiving a record of performance metrics for multiple beams of the multibeam system during the imaging process. This method may also include comparing the records to determine whether any of the multiple beams is abnormal. This method may further include triggering a notification if a beam is abnormal.

[0009]

[0009] In some embodiments, a device for monitoring the health of a multibeam system is disclosed. This device may include a controller having a circuit for executing a set of commands. The controller can execute a set of commands to cause the system to receive a record of performance metrics for multiple beams of the multibeam system during the imaging process. The controller can also execute a set of commands to cause the system to compare the records and determine whether any of the multiple beams is abnormal. The controller can further execute a set of commands to trigger a notification to the system if a beam is abnormal.

[0010]

[0010] In some embodiments, a non-temporary computer-readable medium is disclosed. The non-temporary computer-readable medium may store a set of instructions that can be executed by at least one processor of the device to cause the device to perform a method for monitoring the health of a multibeam system. This method may include receiving a record of performance metrics for multiple beams of the multibeam system during the imaging process. This method may also include comparing the records to determine whether any of the multiple beams is showing abnormalities. This method may further include triggering a notification if a beam shows abnormalities.

[0011]

[0011] In some embodiments, a method for monitoring the health of a multibeam system may include receiving a record of performance metrics for multiple beams of the multibeam system, where the multiple beams include a first beam and other beams. The method may also include comparing data derived from the record to determine whether the first beam is exhibiting abnormal performance compared to the other beams. The method may further include triggering a notification if, based on the comparison, it is determined that the first beam is exhibiting abnormal performance. [Brief explanation of the drawing]

[0012] [Figure 1]

[0012] This is a schematic diagram showing an exemplary electron beam inspection (EBI) system consistent with embodiments of the present disclosure. [Figure 2]

[0013] This is a schematic diagram showing an exemplary multibeam system, which is part of the exemplary charged particle beam inspection system shown in Figure 1, consistent with embodiments of the present disclosure. [Figure 3]

[0014] This flowchart shows an exemplary method for monitoring the performance of a multibeam system, consistent with embodiments of the present disclosure. [Figure 4]

[0015] This flowchart shows another exemplary method for monitoring the performance of a multibeam system, consistent with embodiments of the present disclosure. [Figure 5]

[0016] This is an explanatory diagram illustrating the time-dependent values ​​of the beam performance metrics for a multi-beam system, consistent with the embodiments of this disclosure. [Modes for carrying out the invention]

[0013]

[0017] Herein, exemplary embodiments are described in detail. Examples of these embodiments are shown in the accompanying drawings. The following description refers to the accompanying drawings, and the same numbers in different drawings represent the same or similar elements unless otherwise noted. The embodiments described in the following description of exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects related to the subject matter enumerated in the accompanying claims. For example, some embodiments are described in the context of utilizing electron beams, but the present disclosure is not limited in that way. Other types of charged particle beams can be applied similarly. Furthermore, other imaging systems such as optical imaging, photodetection, and X-ray detection can be used.

[0014]

[0018] Electronic devices consist of circuits formed on a piece of silicon called a substrate. Numerous circuits can be formed together on the same silicon piece, and these are called integrated circuits or ICs. The dimensions of these circuits have been dramatically reduced to accommodate a large number of circuits on a substrate. For example, an IC chip in a smartphone can be as small as a thumbnail, yet it can contain over 2 billion transistors, each transistor being less than 1 / 1000th the size of a human hair.

[0015]

[0019] Manufacturing these extremely small ICs is a complex, time-consuming, and expensive process, often involving hundreds of individual steps. An error in just one step can result in a defect in the finished IC, rendering it unusable. Therefore, one of the goals of the manufacturing process is to avoid such defects and maximize the number of functional ICs produced in the process, thereby improving the overall yield of the process.

[0016]

[0020] One component that improves yield is monitoring the chip fabrication process to ensure that a sufficient number of functional integrated circuits are manufactured. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. Inspection can be performed using a scanning electron microscope (SEM). Using an SEM, it is possible to image these very small structures—in short, to take "photographs" of these structures on the wafer. This image can be used to determine whether the structures were formed properly and whether they were formed in the correct locations. If there are defects in the structures, the process can be adjusted to reduce the likelihood of the defects recurring.

[0017]

[0021] The operating principle of a scanning electron microscope (SEM) is similar to that of a camera. A camera takes a photograph by receiving and recording the brightness and color of light reflected or emitted from a person or object. A SEM takes a "photograph" by receiving and recording the energy or quantity of electrons reflected or emitted from a structure. Before taking such a "photograph," an electron beam may be provided over the structure, and as electrons are reflected or emitted (emitted) from that structure, the SEM's detector can receive and record the energy or quantity of those electrons to generate an image. To take such a "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 a wafer. By using multiple electron beams, the SEM can provide more electron beams over the structure to acquire these multiple "photographs," resulting in more electrons emitting from the structure. Therefore, the detector can receive more emitting electrons simultaneously, generating an image of the wafer structure with higher efficiency and at a faster rate.

[0018]

[0022] However, compared to single-beam SEMs, multi-beam SEMs contain more components, have a more complex design, and produce less clear signals on one or more electron detectors. Monitoring the health of a multi-beam SEM is important because even abnormal behavior in just one beam during operation can significantly impact the overall performance of the multi-beam SEM. Furthermore, if performance problems occur, the more complex design of a multi-beam SEM makes it more time-consuming, expensive, and labor-intensive for technicians to diagnose. Ultimately, due to the less clear signals, monitoring the health of a multi-beam SEM is more difficult than monitoring the health of a single-beam SEM.

[0019]

[0023] This disclosure, in particular, describes methods and systems for monitoring the health of a multibeam SEM. Depending on the embodiment, “baseline” conditions may be used as criteria for evaluating the performance data of a multibeam SEM to monitor its health. Operating parameters or statistical performance data of the multibeam SEM may be collected and compared to the baseline conditions. If anomalies are found in the operating parameters or statistics based on the comparison, an anomaly in the performance of the multibeam SEM may be reported, and further action may be taken. In one example, the multibeam SEM may use a self-referencing scheme, which may use the hierarchical performance data of the multibeam SEM to determine a hierarchical baseline condition, or use the individual performance data of each beam to determine a group performance-based baseline condition. The hierarchical baseline condition or the group performance-based baseline condition is used as a reference for monitoring the health of the multibeam SEM. Essentially, by using a hierarchical baseline condition, the current performance of a beam in the multibeam SEM is compared to the previous ("history") performance of the same beam measured earlier in the operation of the multibeam system, and if the current performance deviates from the hierarchical performance, an anomaly is identified. By using a group performance-based baseline, the current performance of one beam in a multi-beam SEM is compared to the current performance of other beams measured during the operation of the multi-beam system, and an anomaly is identified if the current performance of that beam deviates from the current performance of the other beams.

[0020]

[0024] By using a self-reference method, the soundness of a multi-beam SEM can be continuously monitored without referring to external inputs, adapt to changes in operating conditions automatically, and automatically identify anomalies with higher accuracy. As can be seen, the accuracy and effectiveness of baseline conditions are important in the soundness monitoring of a multi-beam SEM. If the soundness monitoring depends on external inputs to provide such baseline conditions, it may be necessary to update the external inputs when the operating conditions of the multi-beam SEM change, which will make it difficult for continuous monitoring of the soundness, automatic adaptation to changes in operating conditions, and automatic identification of anomalies with high accuracy.

[0021]

[0025] The relative dimensions of components in the drawings can be exaggerated for ease of understanding. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only the differences with respect to individual embodiments are described.

[0022]

[0026] As used herein, unless otherwise specified, the term "or" includes all possible combinations except when it is not feasible. For example, if it is described that a component can include A or B, unless otherwise specified or not feasible, the component can include A, or B, or A and B. As a second example, if it is described that a component can include A, B, or C, unless otherwise specified or not feasible, the component can include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0023]

[0027] FIG. 1 shows an exemplary electron beam inspection (EBI) system 100 that is consistent with embodiments of the present disclosure. The EBI system 100 can be used for imaging. As shown in FIG. 1, the EBI system 100 includes a main chamber 101, a load / lock chamber 102, an electron beam tool 104, and an equipment front end module (EFEM) 106. The electron beam tool 104 is disposed inside the main chamber 101. The EFEM 106 includes a first loading port 106a and a second loading port 106b. The EFEM 106 can include additional loading ports. The first loading port 106a and the second loading port 106b receive a wafer front opening unified pod (FOUP) that houses a wafer to be inspected (e.g., a semiconductor wafer or a wafer made of other materials) or a sample (the wafer and the sample can be used interchangeably). A “lot” is a plurality of wafers that can be loaded for processing as a batch.

[0024]

[0028] One or more robot arms (not shown) within the EFEM 106 can transport the wafer to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) that removes gas molecules within the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, one or more robot arms (not shown) can transport the wafer from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pump system (not shown) that removes gas molecules within the main chamber 101 to reach a second pressure lower than the first pressure. After reaching the second pressure, the wafer is subjected to inspection by the electron beam tool 104. The electron beam tool 104 can be a single beam system or a multi-beam system.

[0025]

[0029] 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. In Figure 1, the controller 109 is shown as being outside the structure, which includes the main chamber 101, the loading / locking chamber 102, and the EFEM 106, but it will be understood that the controller 109 can also be part of this structure.

[0026]

[0030] Depending on the embodiment, the 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 (i.e., "CPUs"), graphics processing units (i.e., "GPUs"), optical processors, programmable logic control units, microcontrollers, microprocessors, digital signal processors, IP (intellectual property) cores, programmable logic arrays (PLAs), programmable array logic (PALs), general-purpose array logic (GALs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), systems-on-a-chip (SoCs), application-specific integrated circuits (ASICs), and any kind of circuitry capable of data processing. A processor may also be a virtual processor, including one or more processors distributed across multiple machines or devices connected via a network.

[0027]

[0031] Depending on the embodiment, the controller 109 may further include one or more memories (not shown). The memories may be general-purpose or specific electronic devices capable of storing code and data accessible by the processor (e.g., via a bus). For example, the memories may include any number of random-access memories (RAM), read-only memories (ROM), optical disks, magnetic disks, hard drives, solid-state drives, flash drives, security digital (SD) cards, memory sticks, compact flash (CF) cards, or any combination of any type of storage device. The code may include an operating system (OS) and one or more application programs (i.e., "apps") for a particular task. The memories may also be virtual memories, including one or more memories distributed across multiple machines or devices connected via a network.

[0028]

[0032] Referring here to Figure 2, Figure 2 is a schematic diagram showing an exemplary electron beam tool 104, which includes a multibeam inspection tool that is part of the EBI system 100 of Figure 1, consistent with embodiments of the present disclosure. The multibeam electron beam tool 104 (also referred to herein as apparatus 104) includes an electron source 201, a gun aperture plate 271, a focusing lens 210, a radiation source conversion unit 220, a primary projection system 230, a motorized stage 209, and a sample holder 207 supported by the motorized stage 209 for holding a sample 208 to be inspected (e.g., a wafer or photomask). The multibeam electron beam tool 104 may further 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 a plurality of detection elements 241, 242, and 243. A beam separator 233 and a deflection scanning unit 232 may be located inside the primary projection system 230.

[0029]

[0033] The electron source 201, gun aperture plate 271, focusing lens 210, radiation source conversion unit 220, beam separator 233, deflection scanning unit 232, and primary projection system 230 can be aligned with the primary optical axis 204 of the device 104. The secondary projection system 250 and electron detection device 240 can be aligned with the secondary optical axis 251 of the device 104.

[0030]

[0034] The electron source 201 may include a cathode (not shown) and an extractor or anode (not shown), and during operation, the electron source 201 is configured to emit primary electrons from the cathode, which are extracted or accelerated by the extractor and / or anode to form a primary electron beam 202, which forms a (virtual or real) primary beam crossover 203. The primary electron beam 202 can be visualized as being emitted from the primary beam crossover 203.

[0031]

[0035] The radiation 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-bending micro-deflector array (not shown). In some embodiments, the pre-bending micro-deflector array deflects a plurality of primary beamlets 211, 212, and 213 of the primary electron beam 202 so that they are incident perpendicular to the beam limiting aperture array, the image forming element array, and the aberration compensator array. In some embodiments, the focusing lens 210 is designed to focus the primary electron beam 202 into a parallel beam and cause it to be incident perpendicular to the radiation source conversion unit 220. The image forming element array may include a plurality of micro-deflectors or microlenses to affect a plurality of primary beamlets 211, 212, and 213 of the primary electron beam 202 and may form a plurality of (virtual or real) parallel images of the primary beam crossover 203, one for each of the primary beamlets 211, 212, and 213. Depending on the embodiment, 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 the field curvature of the primary beamlets 211, 212, and 213. The astigmatism compensator array may include a plurality of microastigmatism correctors to compensate for the astigmatism of the primary beamlets 211, 212, and 213. A beam limiting aperture array may be configured to limit the diameter of the individual primary beamlets 211, 212, and 213. Figure 2 shows three primary beamlets 211, 212, and 213 as an example, and it will be understood that the source conversion unit 220 may be configured to form any number of primary beamlets. The controller 109 may be connected to various parts of the EBI system 100 in Figure 1, such as the source conversion unit 220, the electron detection device 240, the primary projection system 230, or the motorized stage 209. Depending on the embodiment, the controller 109 may perform various image and signal processing functions, as will be described in more detail below. The controller 109 may also generate various control signals to control the operation of the charged particle beam inspection system.

[0032]

[0036] The focusing lens 210 is configured to focus the primary electron beam 202. The focusing lens 210 can be further configured to adjust the currents in the primary beamlets 211, 212, and 213 downstream of the radiation source conversion unit 220 by changing the focusing force of the focusing lens 210. Alternatively, the current can be changed by changing the size of the radius of the beam limiting aperture within the beam limiting aperture array corresponding to each primary beamlet. The current can be changed by changing both the size of the radius of the beam limiting aperture and the focusing force of the focusing lens 210. The focusing lens 210 may be a movable focusing lens, which can be configured so that the position of the first principle plane is movable. The movable focusing lens may be configured to be magnetic, as a result, the off-axis beamlets 212 and 213 may irradiate the radiation source conversion unit 220 with a rotation angle. The rotation angle changes with the focusing force or the position of the first principle plane of the movable focusing lens. The focusing lens 210 may be a rotation-preventing focusing lens that can be configured to maintain a constant rotation angle while the focusing force of the focusing lens 210 is changing. In some embodiments, the focusing lens 210 may be a movable rotation-preventing focusing lens in which the rotation angle does not change when the focusing force and the position of the first principal plane change.

[0033]

[0037] The objective lens 231 may be configured to focus the beamlets 211, 212, and 213 onto the sample 208 for inspection, and in the present embodiment, three probe spots 221, 222, and 223 may be formed on the surface of the sample 208. The gun aperture plate 271 is configured to reduce the Coulomb effect by blocking peripheral electrons of the primary electron beam 202 during operation. The Coulomb effect can enlarge the size of each of the probe spots 221, 222, and 223 of the primary beamlets 211, 212, and 213, and thus reduce the inspection resolution.

[0034]

[0038] The beam separator 233 may be, for example, a Wien filter including an electrostatic deflector that generates an electrostatic dipole field and a magnetic dipole field (not shown in Figure 2). When in operation, the beam separator 233 can be configured to exert an electrostatic force on the individual electrons of the primary beamlets 211, 212, and 213 by the electrostatic dipole field. The electrostatic force is equal in magnitude to the magnetic force exerted on the individual electrons by the magnetic dipole field of the beam separator 233, but in the opposite direction. Thus, the primary beamlets 211, 212, and 213 can pass through the beam separator 233 at least substantially straight with at least substantially zero deflection angle.

[0035]

[0039] During operation, the deflection scanning unit 232 is configured to deflect the primary beamlets 211, 212, and 213 to scan probe spots 221, 222, and 223 across individual scan areas within sections of the surface of sample 208. In response to the incidence of the primary beamlets 211, 212, and 213 or probe spots 221, 222, and 223 on sample 208, electrons emerge from sample 208, generating three secondary electron beams 261, 262, and 263. Each of the secondary electron beams 261, 262, and 263 typically contains secondary electrons (with electron energies of 50 eV or less) and backscattered electrons (with electron energies between 50 eV and the landing energies of the primary beamlets 211, 212, and 213). The beam separator 233 is configured to deflect the secondary electron beams 261, 262, and 263 toward the secondary projection system 250. Subsequently, the secondary projection system 250 focuses the secondary electron beams 261, 262, and 263 toward the detection elements 241, 242, and 243 of the electron detection device 240. The detection elements 241, 242, and 243 are configured to detect the corresponding secondary electron beams 261, 262, and 263 and generate corresponding signals that are transmitted to the controller 109 or a signal processing system (not shown) to construct an image of the corresponding scan area of ​​the sample 208, for example.

[0036]

[0040] In some embodiments, detection elements 241, 242, and 243 detect the corresponding secondary electron beams 261, 262, and 263, respectively, and generate corresponding intensity signal outputs (not shown) toward 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 a detection element may be the sum of the signals generated by all pixels within the detection element.

[0037]

[0041] Depending on the embodiment, the controller 109 may include an image processing system including an image acquirer (not shown) and storage (not shown). The image acquirer may include one or more processors. For example, the image acquirer may include a computer, server, mainframe host, terminal, personal computer, any type of portable computer device, or a combination thereof. The image acquirer may be communicatively coupled to the electronic detection device 240 of the device 104 via a medium, in particular a conductor, fiber optic cable, portable storage medium, IR, Bluetooth, the Internet, a wireless network, wireless communication, or a combination thereof. Depending on the embodiment, the image acquirer may receive signals from the electronic detection device 240 and 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 contour lines and overlaying indicators onto the acquired image. The image acquirer may be configured to perform adjustments such as brightness and contrast of the acquired image. Depending on the embodiment, the storage may be a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), or other types of computer-readable memory. The storage may be coupled to an image acquirer and can be used to store scanned raw image data as source images and to store processed images.

[0038]

[0042] In some embodiments, the image acquirer may acquire one or more images of a sample based on an imaging signal received from the electronic detection device 240. The imaging signal may correspond to a scan operation for performing charged particle imaging. The acquired image may be a single image containing multiple imaging areas. This single image may be stored in storage. This single image may be a source image that can be divided into multiple regions. Each of these regions may contain one imaging area encompassing a feature of sample 208. The acquired image may include multiple images of a single imaging area of ​​sample 208, sampled multiple times in time series. These multiple images may be stored in storage. In some embodiments, the controller 109 may be configured to perform image processing steps using multiple images of the same location of sample 208.

[0039]

[0043] Depending on the embodiment, the controller 109 may include a measurement circuit (e.g., an analog-to-digital converter) to acquire the distribution of detected secondary electrons. The electron distribution data collected during the detection time window can be used in combination with the corresponding scan data of the primary beamlets 211, 212, and 213 incident on the wafer surface to reconstruct an image of the wafer structure under inspection. The reconstructed image can be used to reveal various features of the internal or external structure of sample 208, thereby revealing any defects that may be present in the wafer.

[0040]

[0044] In some embodiments, the controller 109 may control the motorized stage 209 to move the sample 208 during inspection. In some embodiments, the controller 109 may allow the motorized stage 209 to move the sample 208 continuously at a constant speed in a certain direction. In other embodiments, the controller 109 may allow the motorized stage 209 to change the speed at which the sample 208 moves out of time depending on the step of the scanning process.

[0041]

[0045] Figure 2 shows that the apparatus 104 uses three primary electron beams, but it will be understood that the apparatus 104 may use two or more primary electron beams. This disclosure does not limit the number of primary electron beams used in the apparatus 104.

[0042]

[0046] Compared to SEM inspection tools that use a single electron beam (called "single-beam systems"), multi-beam SEM inspection tools ("multi-beam systems") can use multiple electron beams to image a sample (e.g., sample 208 in Figure 2) simultaneously. The throughput of multi-beam inspection tools can be higher than that of single-beam systems. However, due to their greater complexity, multi-beam systems may lack the structural flexibility of single-beam systems. Furthermore, the signal-to-noise ratio (SNR) of a multi-beam system's signal may be lower than that of a single-beam system, which can make monitoring its health difficult. For example, the brightness of SEM images generated by a multi-beam system may have accumulated drift (shift). If such drift is not identified, it can propagate into the image analysis and cause errors. Therefore, it is important to monitor the health of multi-beam systems and provide accurate and timely diagnoses, which can reduce maintenance effort and costs, as well as cycle time.

[0043]

[0047] In some embodiments of this disclosure, a self-referencing scheme may be used to monitor the health of a multibeam system. The self-referencing scheme may monitor beam performance metrics by using baseline conditions or group performance metrics as a reference. Performance metrics may represent at least one of the image quality or geometric distortion of images generated during the imaging process from the beam's field of view (FOV). Performance metrics may represent the imaging performance of the beam. Baseline conditions may be fixed benchmarks, behaviors, or specifications of performance metrics. Baseline conditions may depend on various conditions such as beam landing energy, beam current, sample pixel size, or sample specifications.

[0044]

[0048] In some embodiments, a multibeam system may select and record one or more performance metrics of the multiple beams of the multibeam system. The multibeam system may record performance metrics of multiple beams and determine baseline conditions from them. Such baseline conditions may not be available in a single-beam system. In some embodiments, the multibeam system may begin recording values ​​at a predetermined point in time.

[0045]

[0049] In some embodiments, a multi-beam system may determine whether a beam is abnormal based on a comparison of the beam's performance metrics with baseline conditions. In other embodiments, a multi-beam system may determine whether a beam is abnormal based on a comparison of the beam's performance metrics with group performance metrics for multiple beams. By using various performance metrics in a self-referencing manner, abnormalities can be diagnosed quickly toward root cause analysis, thus increasing the efficiency of troubleshooting.

[0046]

[0050] Depending on the embodiment, the multibeam system may perform the aforementioned processes in an automated manner. By doing so, the multibeam system can continuously monitor the health of the multibeam system, detect anomalies, and diagnose one or more modules (e.g., hardware modules or software modules) causing the problem based on the detected anomalies.

[0047]

[0051] Figures 3 and 4 show exemplary flowcharts illustrating methods 300 to 400, consistent with embodiments of the present disclosure. Methods 300 to 400 can be implemented by a monitoring module that can be coupled to a charged particle beam apparatus (e.g., EBI system 100). For example, a controller (e.g., controller 109 in Figures 1 and 2) may include a monitoring module and be programmed to implement methods 300 to 400.

[0048]

[0052] Figure 3 is a flowchart illustrating an exemplary method for monitoring the performance of a multibeam system, consistent with embodiments of the present disclosure.

[0049]

[0053] In step 302, the controller (e.g., controller 109 in Figures 1-2) receives records of performance metrics for multiple beams of the multibeam system during the imaging process. In some embodiments, the controller may receive records from a database coupled to a charged particle beam apparatus (e.g., EBI system 100). For example, the database may receive operational data of the multibeam system and preprocess it to create records of performance metrics for multiple beams. In some embodiments, each record may be associated with a particular beam. The imaging process may be a process that generates an SEM image by scanning multiple beams across the entire surface of a sample (e.g., sample 208 in Figure 2). In some embodiments, the controller may receive records of multiple performance metrics. In some embodiments, the controller may store the records of performance metrics in a database, for example, in a memory accessible by the controller.

[0050]

[0054] In some embodiments, the performance metric may include any variable or parameter that indicates at least one of the image quality or geometric distortion of an image generated in the imaging process from the field of view (FOV) of one of several beams. For example, for each beam, the controller may be controlled to generate an SEM image of that beam's FOV (for example, by using the electronic detection device 240 in Figure 2). In some embodiments, the performance metric indicating geometric distortion of an image may include a variable or parameter that indicates or characterizes aberrations, structural distortions, or changes in geometric shape. In some embodiments, the performance metric indicating image quality may include a variable or parameter that indicates, characterizes, or represents the noise level of an image, the brightness of an image, the contrast of an image, the sharpness of an image, or the resolution of an image.

[0051]

[0055] For example, if the performance criterion includes a variable or parameter that indicates, characterizes, or represents the noise level of an image, data indicating or characterizing the noise level of the image ("noise level data") can be generated or derived from one or more signals ("background signals") received by the electronic detection device 240 from an area without the pattern of the designed circuit ("background area"). In some embodiments, the controller may determine the noise level data of the image using a portion of the image corresponding to the background area of ​​the FOV. For example, the FOV may cover an area with the intended circuit pattern and a background area. In this case, the controller may determine the signal level detected by the electronic detection device 240 corresponding to the background area. In some embodiments, the controller may determine this area based on at least one of layout data of the area of ​​the FOV or image data of the image. For example, design data may include files in formats such as GDS (Graphic Database System), GDS II (Graphic Database System II), OASIS (Open Artwork System Interchange Standard), and CIF (Caltech Intermediate Format), which show the distribution of patterns in the intended circuit. Design data may also include information indicating whether a pattern exists in a particular area. In another example, background areas can be determined by analyzing an image of the field of view (FOV), such as by performing feature extraction on the image. For example, areas where no features are extracted after feature extraction can be determined as background areas.

[0052]

[0056] In another example, if the performance metric includes a variable or parameter that indicates, characterizes, or represents the brightness or contrast of an image, the controller may determine at least one of the brightness or contrast based on the gray level of the image. For example, an image of an FOV may be a grayscale SEM image. By analyzing the grayscale values ​​of the pixels in the image, the controller may determine the brightness, contrast, or both.

[0053]

[0057] In another example, if the performance metric includes a variable or parameter that indicates, characterizes, or represents resolution or sharpness, then resolution or sharpness can be used to determine whether the beam is out of focus. Depending on the embodiment, the controller may determine at least one of sharpness or resolution from the step profile. Before doing so, the controller may extract the step profile from the pattern edges of the image. Pattern edges can be the edges of a pattern in the image. For example, the controller may extract a pattern by performing the aforementioned feature extraction on a grayscale image of the FOV. The controller can then determine the edges from the pattern. The edges may correspond to features of a circuit with wall structures, such as ditches, grooves, holes, lines, or steps. Ideally, an edge may have a 90° wall between two faces of different heights, and the grayscale value corresponding to the edge in the image may have a discontinuous jump. However, due to the actual lithography and etching processes during manufacturing, as well as uncertainties in SEM imaging, the edge may not have a 90° wall, and the corresponding grayscale value may have a continuous change called a “step profile”. Depending on the embodiment, the controller can determine the rate of change of the grayscale value of the step profile, and a larger rate of change can result in higher sharpness or resolution.

[0054]

[0058] Returning to Figure 3, in step 304, the controller determines whether a beam anomaly has occurred based on parameter values ​​determined using a portion of the records. The parameter values ​​may be history-based baseline values ​​or group performance-based baseline values. During the current operating period of the multibeam SEM, if the parameter values ​​are history-based baseline values, the controller may determine the parameter values ​​using performance data from previous operating periods of the multibeam SEM, such as an "initial period" or "test run" period in which the multibeam SEM operates immediately after maintenance or calibration. During the current operating period of the multibeam SEM, if the parameter values ​​are group performance-based baseline values, the controller may determine the parameter values ​​using performance data from the current operating period of the multibeam SEM.

[0055]

[0059] In some embodiments, to determine whether a beam anomaly has occurred, the controller may determine parameter values ​​as historical baseline values ​​for the performance metrics of multiple beams based on historical recorded values. In some embodiments, the controller may determine baseline values ​​after a predetermined event. For example, the controller may determine baseline values ​​based on initial records of performance metrics received after a predetermined event. In some embodiments, the predetermined event may include the completion of maintenance of the multibeam system or calibration of the multibeam system. For example, after the completion of maintenance or calibration, the multibeam system may be restarted and records values ​​for the performance metrics of each of the multiple beams. Such values ​​may be used as a reference for the performance metrics and may be called "initial records". In some embodiments, the predetermined event may include each recorded value changing by a shift value, and the shift value does not deviate from the average value of the recorded values ​​beyond a predetermined range. For example, after maintenance or calibration, for each beam, the controller may store the recorded value corresponding to that beam, and the value may fall within a predetermined tolerance range. The tolerance range may be set, for example, by the user of the multibeam system. Depending on the embodiment, the controller may also determine a test value for the beam using at least one value from beam recordings received at multiple points in time during the imaging process.

[0056]

[0060] In some embodiments, to determine whether a beam anomaly is occurring, the controller may determine parameter values ​​as group performance-based baseline values, such as statistics of a portion of the records. For example, the controller may compare data derived from records of multiple beams to determine whether a first beam is exhibiting abnormal performance compared to other beams. The controller may obtain statistics of the performance metrics of the other beams and determine that the difference between the performance metrics of the first beam and its statistics is greater than or equal to a predetermined threshold. Statistics may include, for example, the mean, median, variance, standard deviation, or root mean square of the records. In some embodiments, a portion of a record may be the entire record. In some embodiments, a portion of a record may be a subset of the records. For example, if a multi-beam system has 25 beamlets, the controller may receive record values ​​corresponding to the 25 beamlets. The controller may further determine record statistics using several records (e.g., 5, 10, 18, 25, etc.). Next, the controller can determine statistical values ​​as baseline values ​​based on group performance.

[0057]

[0061] In some embodiments, after determining a parameter value (e.g., a history-based baseline value or a group performance-based baseline value), the controller may further determine whether the difference between the recorded test value and the baseline value is within a predetermined range. The controller may then determine that an anomaly has occurred based on the determination that the difference is not within the predetermined range. In some embodiments, the controller may store at least one of the parameter value or the record in a database. In some embodiments, the controller may read at least one of the record or parameter value from the database before determining whether the difference is within the predetermined range.

[0058]

[0062] Exemplary baseline values ​​may be shown in Figure 5. Figure 5 is an explanatory diagram of time-dependent values ​​of performance metrics for multiple beams in a multibeam system, consistent with embodiments of the present disclosure. In Figure 5, the horizontal axis (from left to right) represents the timeline, and the vertical axis represents the values ​​of the performance metrics. The timeline includes several time points, including time points T0 to T5. Since a multibeam system includes multiple beams, Figure 5 shows multiple curves 502. Each curve in curve 502 may represent a time-dependent value of a record, and each record corresponds to one beam in the multibeam system. Curve 502 includes the current curve 506. Figure 5 also shows a baseline 504 that does not represent a time-dependent value. The controller may compare each of the curves 502 (including the current curve 506) with the baseline 504 and determine that an anomaly has occurred for that curve if the difference between the curve and the baseline 504 satisfies a predetermined condition. In some embodiments, the predetermined condition may be that the difference is not within a predetermined range. For example, in Figure 5, the predetermined range is shown as range 508, which includes the baseline 504. Depending on the embodiment, a predetermined range may be symmetric with respect to the baseline value. Depending on the embodiment, a predetermined range may be asymmetric with respect to the baseline value. Although range 508 is shown as symmetric with respect to baseline 504, it may be asymmetric.

[0059]

[0063] In some embodiments, the controller may determine baseline 504 as a history-based baseline. For example, the controller may determine baseline 504 using initial records received before time point T0. Starting at time point T0, the value of baseline 504 may be constant, and the controller may compare each curve of curve 502 (e.g., the current curve 506) with baseline 504. If the difference between baseline 504 and the curve is outside range 508, the controller may determine that an anomaly has occurred. For example, in Figure 5, baseline 504 is a history-based baseline and its value may be constant over time. At some point between time points T1 and T2, the difference between the current curve 506 and baseline 504 exceeds range 508, and the controller may determine that an anomaly has occurred in the beam corresponding to the current curve 506.

[0060]

[0064] In some embodiments, the controller may determine baseline 504 as a group performance-based baseline. For example, the controller may determine baseline 504 as a statistic for a portion of curve 502. A “statistic” can be a numerical value calculated from a sample of data and characterizing that sample. For example, a statistic can be the mean, median, standard deviation, standard error, or any other numerical value suitable for use as baseline 504. For example, a portion of curve 502 could be the entire curve 502 (e.g., including the current curve 506). In another example, a portion of curve 502 could be the entire curve 502 excluding the current curve 506. Note that any subset of curve 502 can be a portion of curve 502 used to determine the statistic. In some embodiments, the controller may determine the statistic at some point in time. If the controller determines the statistic at some point in time, the values ​​of baseline curve 504 and range 508 in Figure 5 may shift up or down over time. To simplify the explanation without ambiguity, if baseline 504 represents the group performance-based baseline in Figure 5, then baseline 504 represents the group performance-based baseline determined at a specific point in time.

[0061]

[0065] In some embodiments, the controller may determine a baseline 504 as a statistical value of a portion of curve 502 at a previous or current point in time. For example, at time T3, the controller may determine a statistical value using real-time values ​​of a portion of curve 502 received at times T3, T2, T1, T0, or at any point before or concurrently with time T3. In some embodiments, the controller may determine a baseline 504 as a statistical value of a portion of curve 502 over a time range. For example, at time T3, the controller may determine a statistical value using all values ​​of a portion of curve 5 received during the time range between times T0 and T1, between T1 and T2, between T2 and T3, between T0 and T2, between T1 and T3, or any time range including before or up to time T3.

[0062]

[0066] In some embodiments, the controller may dynamically update the baseline 504 as statistics for a portion of the curve 502 (e.g., periodically or in real time). As described above, the controller may update the baseline 504 using values ​​for a portion of the curve 502 at a previous or current point in time, or over a time range. In some embodiments, the controller may update the baseline 504 periodically or irregularly as statistics for a portion of the curve 502 at different points in time. For example, in Figure 5, over time, the controller may update the baseline 504 at any time points T0, T1, T2, T3, T4, and T5. In some embodiments, the controller may update the baseline 504 as statistics for a portion of the curve 502 over a sliding time window. For example, at time T3, the controller may determine the baseline 504 as a statistical value of a portion of curve 502 using the values ​​of a portion of curve 502 over a time window between time T1 and T2, between T2 and T3, between T0 and T1, between T0 and T2, between T1 and T3, or any time range including before or up to time T3. At time T4, the controller may determine the baseline 504 as a statistical value of a portion of curve 502 using the values ​​of a portion of curve 502 over a time window between time T2 and T3, between T3 and T4, between T1 and T2, between T1 and T3, between T2 and T4, or any time range including before or up to time T4, shifted by a time difference of (T4-T3) and having the same length as the time window used at time T3. Other methods may be used to determine and update the baseline 504, and these methods are not limited to the examples described above.

[0063]

[0067] It should be noted that when comparing the current curve 506 with the baseline 504, the controller may use either the real-time or instantaneous value of the current curve 506, or the statistical value of the current curve 506. Depending on the embodiment, the controller may use the real-time value of the current curve 506 for comparison, such as the real-time value received at the time the comparison is performed. For example, at time T3, the real-time value of the current curve 506 may be received at time T3. When the controller uses the real-time value of the current curve 506 for comparison, the controller may determine that an anomaly has occurred if the difference between the real-time value of the current curve 506 and the baseline 504 satisfies certain conditions. For example, in Figure 5, at some point between time T1 and T2, the current curve 506 has a real-time value shown as a first spike in the timeline that exceeds the upper limit of range 508. At that point, the controller may determine that an anomaly has occurred in the beam corresponding to the current curve 506.

[0064]

[0068] In some embodiments, the controller may use current statistics (e.g., mean, median, etc.) of the current curve 506 for comparison. The current statistics of the current curve 506 can be determined over a period of time using all the values ​​of the current curve 506 received during that period. The current statistics of the current curve 506 can be determined in a similar manner to the statistics of curve 502. For example, at time T4, the current statistics of the current curve 506 may be the average of all the values ​​of the current curve 506 received during the period between time T3 and T4. In another example, at time T5, the current statistics of the current curve 506 may be the median of all the values ​​of the current curve 506 received during the period between time T2 and T5. When the controller uses current statistics of the current curve 506 for comparison, the controller may determine that an anomaly has occurred if the difference between the current statistics of the current curve 506 and the baseline 504 satisfies certain conditions. For example, in Figure 5, at time T2, the current statistic may be the average of all values ​​of the current curve 506 received during the period between time T1 and T2. At some point between time T1 and T2, the current curve 506 has a real-time value that is shown as the first spike in the timeline, exceeding the upper limit of range 508. However, the current statistic between time T1 and T2 may fall within range 508, and the controller may not determine that an anomaly occurred at time T2. Nevertheless, at time T3, the current statistic may be the average of all values ​​of the current curve 506 received during the period between time T2 and T3, and the current statistic between time T2 and T3 may exceed range 508. Therefore, the controller may determine that an anomaly occurred in the beam corresponding to the current curve 506 at time T3.

[0065]

[0069] In some embodiments, an ensemble performance method can be used to determine whether an anomaly is occurring in the beam. In the ensemble performance method, the controller may determine a first regression parameter from regressions performed on recorded values ​​received at multiple time points, and a second regression parameter from regressions performed on each of the values ​​of portions of the recorded values ​​received at those multiple time points. Each of the second regression parameters may correspond to one portion of the recorded data.

[0066]

[0070] For example, Figure 5 shows curve 502 and the current curve 506, which will determine whether an anomaly exists. The current curve 506 corresponds to the recording of the beam for which the controller is detecting whether an anomaly has occurred. For example, from time T0, the controller may control the recording and storage of performance metric values ​​corresponding to multiple beams of a multi-beam system, each corresponding to one of the multiple curves 502. The values ​​can be recorded continuously or intermittently. A first regression parameter can be determined from a regression performed on the current curve 506. A second regression parameter can be determined from a regression performed on a subset of the multiple curves 502. In some embodiments, this subset may include the current curve 506. In some embodiments, this subset may not include the current curve 506. For example, the subset may include all curves 502 except the current curve 506. In another example, the subset may include some of the multiple curves 502 except the current curve 506. Each of the second regression parameters may correspond to one of the subsets of curves 502. In some embodiments, the regression may include linear regression, where the first and second regression parameters may include either a slope or an intercept. For example, by performing a linear regression on the current curve 506, the controller may determine at least one of the first slope or first intercept of the current curve 506. By performing a linear regression on each of the subsets of curve 502, the controller may determine at least one of the second slope or second intercept of each subset.

[0067]

[0071] In the ensemble performance method, depending on the embodiment, the controller may determine the parameter values ​​using a second regression parameter. For example, the parameter values ​​may be determined as the mean, median, variance, standard deviation, or root mean square of the second regression parameter. For example, if the second regression parameter includes a second set of slopes of multiple curves 502, the parameter values ​​may be determined as statistics of the second set of slopes (e.g., mean or median). In another example, if the second regression parameter includes a second set of intercepts of multiple curves 502, the parameter values ​​may be determined as statistics of the second set of intercepts (e.g., mean or median).

[0068]

[0072] In the ensemble performance method, the controller may further determine whether the difference between the first regression parameter and the parameter value is within a predetermined range. For example, the controller may determine whether the difference between the statistical values ​​of the first slope (or first intercept) and the second slope (or second intercept) is within a predetermined range. Depending on the embodiment, the predetermined range may be symmetric with respect to the statistical values ​​of the parameter values. Depending on the embodiment, the predetermined range may be asymmetric with respect to the statistical values ​​of the parameter values.

[0069]

[0073] In the ensemble performance method, the controller can then determine that an anomaly has occurred if the difference is not within a predetermined range. For example, in Figure 5, if the difference between the first slope of the current curve 506 and the average value of the second set of slopes of curve 502 is not within a predetermined range, the controller can determine that an anomaly has occurred in the current curve 506.

[0070]

[0074] Continuing back to Figure 3, in step 306, the controller provides an anomaly notification in response to the determination that an anomaly has occurred. Depending on the embodiment, the anomaly notification may include a visual display, such as a warning displayed in a graphical user interface (GUI) (e.g., a pop-up window). Depending on the embodiment, the anomaly notification may include an audible display, such as a sound. Depending on the embodiment, the anomaly notification may include a message sent to the user of the multibeam system, such as a text message. Other forms of anomaly notifications may also be used. If a user receives an anomaly notification, the user can begin checking whether there is actually a problem with a module of the multibeam system (e.g., a hardware module or a software module). For example, the user may begin checking log files associated with the multibeam system.

[0071]

[0075] In some embodiments, after step 306, the controller may determine the cause of the anomaly in response to the determination that an anomaly has occurred. For example, the controller may trigger the execution of diagnostic software to determine the cause of the anomaly and whether the anomaly indicates an actual problem with the multibeam system.

[0072]

[0076] Figure 4 is a flowchart illustrating an exemplary method 400 for monitoring the performance of a multibeam system, consistent with embodiments of the present disclosure.

[0073]

[0077] In step 402, the controller (e.g., controller 109 in Figures 1-2) determines baseline values ​​for the performance metrics of multiple beams in the multibeam system during the imaging process. Depending on the embodiment, each record may be associated with one of the multiple beams. For example, the controller may determine the baseline value based on an initial record of the performance metrics received after a predetermined event. In another example, the controller may determine the baseline value as a statistical value of a portion of the performance metric records.

[0074]

[0078] In step 404, the controller stores at least one of the baseline value or record in the database. Depending on the embodiment, the database may be located in memory accessible to the controller.

[0075]

[0079] In step 406, the controller receives a record of performance metrics for multiple beams during the imaging process. Depending on the embodiment, the controller may perform step 406 in a manner similar to step 302. For example, the controller may store the received record in a database.

[0076]

[0080] In step 408, the controller reads at least one of the recorded or baseline values ​​from the database.

[0077]

[0081] In step 410, the controller determines whether a beam anomaly has occurred based on the parameter values ​​associated with the portion of the recording. Depending on the embodiment, the controller may carry out step 410 in a manner similar to step 304.

[0078]

[0082] In step 412, the controller provides an anomaly notification in response to a determination that an anomaly has occurred. Depending on the embodiment, the controller may carry out step 412 in a manner similar to that of step 306.

[0079]

[0083] In some embodiments, after step 412, the controller may determine the cause of the anomaly in response to the determination that an anomaly has occurred. For example, the controller may trigger the execution of diagnostic software to determine the cause of the anomaly and whether the anomaly indicates an actual problem with the multibeam system.

[0080]

[0084] A non-temporary computer-readable medium can be provided for storing instructions for a processor (e.g., the processor of controller 109 in Figure 1) for performing image processing, data processing, self-referencing schemes, database management, graphical display, operation of a charged particle beam apparatus or another imaging device, etc. Common forms of non-temporary media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tapes, or any other magnetic data recording media, CD-ROMs, any other optical data recording media, any physical medium having a pattern of holes, RAM, PROMs, and EPROMs, FLASH-EPROMs, or any other flash memory, NVRAMs, caches, registers, any other memory chips or cartridges, and networked versions of the aforementioned.

[0081]

[0085] Embodiments can be further described using the following claims. 1. A method for monitoring the performance of a multibeam system, This involves receiving records of performance metrics for multiple beams of a multi-beam system operating during the imaging process, with each record associated with a specific beam. Determining whether a beam anomaly is occurring is based on baseline values ​​determined using a portion of the record, A method comprising providing a notification of an anomaly in response to a determination that an anomaly has occurred. 2. Determining whether or not a beam anomaly has occurred is, The test value of the beam is determined using at least one value from the beam recording received at multiple points in the imaging process, To determine whether the difference between the beam test value and the baseline value is within a predetermined range, The method of Clause 1, including determining that an anomaly has occurred based on the determination that the difference is not within a predetermined range. 3. The method described in any one of the clauses 1 to 2, further comprising storing at least one of a baseline value or record in a database. 4. The method of Clause 3, further comprising reading at least one of the records or baseline values ​​from the database before determining whether the difference is within a predetermined range. 5. The method described in any one of the clauses 1 to 4, further comprising determining a baseline value after a specified event. 6. Determining baseline values ​​after a specified event is The method according to Clause 5, which includes determining a baseline value based on an initial record of performance metrics received after a specified event. 7. The specified event is the method described in any one of the clauses 5 to 6, including the completion of either maintenance of the multibeam system or calibration of the multibeam system. 8. The method described in any one of clauses 5 to 7, wherein a specified event includes each value of the record changing by a shift value, and the shift value does not deviate from the average value of the record beyond a specified range. 9. The method described in any one of the clauses 1 to 4, further comprising determining a baseline value as a statistical value of a portion of the recorded values. 10. The test values ​​of the beam shall be as described in any one of the clauses 2 to 9, including a statistical value of at least one value of the beam record. 11. A statistical value is defined in any one of the following ways, including the mean, median, variance, standard deviation, or root mean square: 12. The beam test values ​​include the beam record values ​​received at one of several time points, as described in any one of clauses 2 to 9. 13. Determining whether or not a beam anomaly is occurring is The first regression parameter is determined from regressions performed on record values ​​received at multiple points in time, and the second regression parameter is determined from regressions performed on each of the values ​​of the portion of the record received at those multiple points in time, wherein each of the second regression parameters corresponds to one of the portions of the record. The baseline value is determined using the second regression parameter, Determining whether the difference between the first regression parameter and the baseline value is within a predetermined range, The method described in any one of the clauses 1 to 12, including determining that an anomaly has occurred based on the determination that the difference is not within a specified range. 14. The method according to clause 13, wherein each of the first and second regression parameters includes one of the slope or the intercept. 15. A portion of the record is the method described in any one of the provisions of Articles 13 to 14, excluding the record described above. 16. A portion of the record includes all records other than those mentioned above, as described in any one of the provisions 13 to 15. 17. Performance measurement criteria are those that indicate at least one of the image quality or geometric distortion of an image generated in the imaging process from the field of view (FOV) of one of multiple beams, as described in any one of Clauses 1 to 16. 18. Performance metrics as described in Clause 17, which indicate geometric distortion of an image and include at least one of aberration, structural distortion, or change in geometric shape. 19. The performance measurement criteria as described in any one of the provisions of 17 to 18, wherein the performance measurement criteria indicate image quality and include a variable representing at least one of the following: image noise level, image brightness, image contrast, image sharpness, or image resolution. 20. The method according to clause 19, further comprising using a portion of the image corresponding to the FOV region to determine data representing the noise level, wherein this region does not have a pattern. 21. The method according to clause 20, further comprising determining the region based on at least one of the region layout data or image data of the FOV. 22. The method described in any one of the clauses 19 to 21, further comprising determining at least one of brightness or contrast based on the gray level of the image. 23. Extracting step profiles from image pattern edges, The method according to any one of the provisions of 19 to 22, further comprising determining at least one of sharpness or resolution from a step profile. 24. The method described in any one of the clauses 1 to 23, further comprising determining the cause of an anomaly in response to a determination that an anomaly has occurred. 25. A system for monitoring the performance of a multibeam system, It is a controller that executes a set of instructions to the system. During the imaging process, records of performance metrics for multiple beams of a multi-beam system are received, and each record is associated with a specific beam. Determine whether a beam anomaly is occurring based on a baseline value determined using a portion of the recording. A system including a controller having a circuit for providing an anomaly notification in response to a determination that an anomaly has occurred. 26. A set of instructions that causes the system to determine whether or not a beam anomaly has occurred further instructs the system to: The test value of the beam is determined using at least one value from the beam recording received at multiple points in the imaging process. Determine whether the difference between the beam test value and the baseline value is within a predetermined range. The system described in Clause 25, which determines that an anomaly has occurred based on the determination that the difference is not within a predetermined range. 27. The system described in any one of the clauses 25 to 26, further comprising a database configured to store at least one of baseline values ​​or records. 28. A set of instructions further enables the system to: The system described in Clause 27, which reads at least one of the recorded or baseline values ​​from a database before determining whether the difference is within a predetermined range. 29. A set of instructions further enables the system to: A system described in any one of clauses 25-28 that determines a baseline value after a specified event. 30. A set of instructions that causes the system to determine a baseline value after a specified event further instructs the system to: The system described in Clause 29, which determines a baseline value based on an initial record of performance metrics received after a specified event. 31. The specified event includes the completion of either maintenance or calibration of the multibeam system, as described in any one of the clauses 29 to 30. 32. A system as described in any one of Clauses 29 to 31, wherein a specified event includes a change of each value in the record by a shift value, and the shift value does not deviate from the average value of the record beyond a specified range. 33. A set of instructions further enables the system to: A system described in any one of clauses 25 to 28, which determines a baseline value as a statistical value of a portion of the recorded values. 34. The beam test values ​​include statistical values ​​of at least one value of the beam record, as set forth in any one of the systems described in Clauses 26 to 33. 35. A statistical value is a system described in any one of clauses 33 to 34, including one of the following: mean, median, variance, standard deviation, or root mean square. 36. The beam test values ​​include the beam record values ​​received at one of several time points in the system as described in any one of clauses 26 to 33. 37. A set of instructions that causes the system to determine whether or not a beam anomaly has occurred further instructs the system to: A first regression parameter is determined from regressions performed on the values ​​of records received at multiple points in time, and a second regression parameter is determined from regressions performed on the values ​​of each portion of the records received at those multiple points in time, with each of the second regression parameters corresponding to one of the portions of the records. The second regression parameter is used to determine the baseline value. Determine whether the difference between the first regression parameter and the baseline value is within a predetermined range. A system described in any one of clauses 25 to 36 that determines that an anomaly has occurred based on the determination that the difference is not within a specified range. 38. The system described in Clause 37, wherein each of the first and second regression parameters includes either a slope or an intercept. 39. The performance measurement criteria are at least one of the following: image quality or geometric distortion of an image generated in the imaging process from the field of view (FOV) of one of multiple beams, as specified in any one of the systems described in any one of the clauses 25 to 38. 40. The performance measurement criteria for the system described in Clause 39 include at least one of the following: aberration, structural distortion, or change in geometric shape, indicating geometric distortion of the image. 41. A system as described in any one of clauses 39 to 40, wherein the performance measurement criteria indicate image quality and include a variable representing at least one of the following: image noise level, image brightness, image contrast, image sharpness, or image resolution. 42. A set of instructions further enables the system to: The system described in Clause 41 uses a portion of the image corresponding to the FOV region to determine data representing the noise level, wherein this region does not have a pattern. 43. A set of instructions further enables the system to: The system described in Clause 42, which determines the region based on at least one of the layout data or image data of the FOV region. 44. A set of instructions further enables the system to: A system described in any one of clauses 41 to 43, which determines at least one of brightness or contrast based on the gray level of an image. 45. A set of instructions further enables the system to: Extract a step profile from the pattern edges of the image. A system as described in any one of clauses 41 to 44, which determines at least one of sharpness or resolution from a step profile. 46. ​​A set of instructions further enables the system to: A system described in any one of clauses 25 to 45 that, in response to a determination that an anomaly has occurred, causes the cause of the anomaly to be determined. 47. A non-temporary computer-readable medium storing a set of instructions, executable by at least one processor of the device, to cause the device to perform a method for monitoring the performance of a multibeam system, the method being: In the imaging process, the recording of performance metrics for multiple beams of a multi-beam system, with each recording associated with a specific beam, Determining whether a beam anomaly is occurring is based on baseline values ​​determined using a portion of the record, A non-temporary computer-readable medium that includes providing a notification of an anomaly in response to a determination that an anomaly has occurred. 48. A method for monitoring the health of a multibeam device, In the imaging process, the recording of performance metrics for multiple beams of a multi-beam system, By comparing the records, it is possible to determine whether one of the multiple beams is showing abnormalities, A method including triggering a notification if the beam shows abnormal behavior. 49. The method according to Clause 48, wherein the plurality of beams includes three or more beams. 50. The method described in any one of the paragraphs 48 to 49, further comprising determining the cause of an anomaly in response to a determination that an anomaly has occurred. 51. The method described in any one of the clauses 48 to 50, wherein each record is associated with one of several beams, and each record includes multiple values ​​of performance metrics received at multiple points in time during the imaging process. 52. Comparing records to determine whether one of several beams shows abnormality is To determine the baseline values ​​for beam performance measurement criteria, The test value of the beam is determined using at least one value from the beam recording received at multiple points in the imaging process, To determine whether the difference between the beam test value and the baseline value is within a predetermined range, The method described in any one of the clauses 48 to 51, including determining that an anomaly has occurred based on the determination that the difference is not within a specified range. 53. The method according to clause 52, further comprising storing at least one of a baseline value or record in a database. 54. The method of the clause 53, further comprising reading at least one of the recorded or baseline values ​​from the database before determining whether the difference is within a predetermined range. 55. The method of any one of the clauses 52 to 54, further comprising determining a baseline value as a statistical value of a portion of the records. 56. The test value of a beam is the method described in any one of the clauses 52 to 55, including a statistical value of at least one value of the beam's record. 57. A statistical value is defined in any one of the following ways, including the mean, median, variance, standard deviation, or root mean square: 58. The test values ​​of a beam include the recorded values ​​of that beam received at one of several time points, as described in any one of the provisions 42 to 56. 59. Comparing records to determine whether one of several beams shows abnormality is, The first regression parameter is determined from regressions performed on record values ​​received at multiple points in time, and the second regression parameter is determined from regressions performed on each of the values ​​of the portion of the record received at those multiple points in time, wherein each of the second regression parameters corresponds to one of the portions of the record. The baseline value is determined using the second regression parameter, Determining whether the difference between the first regression parameter and the baseline value is within a predetermined range, The method described in any one of the clauses 48 to 58, including determining that an anomaly has occurred based on the determination that the difference is not within a specified range. 60. The method according to Clause 59, wherein each of the first and second regression parameters includes one of the slope or the intercept. 61. A portion of the record is the method described in any one of the provisions 59 to 60, which does not include the record described above. 62. A portion of the record includes all records other than those mentioned above, as described in any one of the provisions 59 to 61. 63. Performance measurement criteria are those that indicate at least one of the image quality or geometric distortion of an image generated in the imaging process from the field of view (FOV) of one beam among multiple beams, as described in any one of clauses 48 to 62. 64. Performance measurement criteria as described in Clause 63, which indicate geometric distortion of an image and include at least one of aberration, structural distortion, or change in geometric shape. 65. The method according to any one of the clauses 63 to 64, wherein the performance measurement criteria indicate image quality and include a variable representing at least one of the following: image noise level, image brightness, image contrast, image sharpness, or image resolution. 66. The method according to clause 65, further comprising using a portion of the image corresponding to the area of ​​the FOV to determine data representing the noise level, wherein this area has no pattern. 67. The method of the clause 66, further comprising determining the region based on at least one of the layout data of the FOV region or the image data of the image. 68. The method described in any one of the clauses 65 to 67, further comprising determining at least one of brightness or contrast based on the gray level of the image. 69. Extracting step profiles from image pattern edges, The method according to any one of the clauses 65 to 68, further comprising determining at least one of sharpness or resolution from a step profile. 70. A device for monitoring the health of a multibeam system, A controller that executes a set of commands to the device. During the imaging process, the system receives records of performance metrics for multiple beams in a multi-beam system. The records are compared to determine whether any of the multiple beams are showing abnormalities. A device including a controller having a circuit for triggering a notification if the beam shows abnormality. 71. The apparatus according to Clause 70, wherein the plurality of beams includes three or more beams. 72. A set of instructions is further given to the device, A device described in any one of clauses 70 to 71, which, in response to a determination that an abnormality has occurred, causes the cause of the abnormality to be determined. 73. The apparatus described in any one of clauses 70 to 72, wherein each record is associated with one of several beams, and each record includes multiple values ​​of performance metrics received at multiple points in time during the imaging process. 74. A set of instructions to cause the device to compare records and determine whether one of several beams is showing an abnormality further enables the system to: Determine the baseline value for the performance measurement criteria of that beam. The test value of the beam is determined using at least one value from the beam recording received at multiple points in the imaging process. Determine whether the difference between the beam test value and the baseline value is within a predetermined range. A device described in any one of clauses 70 to 73 that determines that an abnormality has occurred based on the determination that the difference is not within a specified range. 75. A set of instructions further sends to the device: The apparatus described in Clause 74, which causes at least one of baseline values ​​or records to be stored in a database. 76. A set of commands further instructs the device, The apparatus described in Clause 75, which causes the database to read at least one of the recorded or baseline values ​​before determining whether the difference is within a predetermined range. 77. A set of commands further instructs the device, An apparatus described in any one of clauses 74 to 76 for determining a baseline value as a statistical value of a portion of the recorded values. 78. The apparatus described in any one of clauses 74 to 77, wherein the test value of the beam includes a statistical value of at least one value of the record of that beam. 79. The apparatus described in any one of clauses 77 to 78, wherein the statistical values ​​include one of the mean, median, variance, standard deviation, or root mean square. 80. The apparatus described in any one of clauses 74 to 78, including the beam test value, which includes the recorded value of the beam received at one of several time points in time. 81. A set of instructions to cause the device to compare records and determine whether one of several beams is showing abnormalities further enables the system to: A first regression parameter is determined from regressions performed on the values ​​of records received at multiple points in time, and a second regression parameter is determined from regressions performed on the values ​​of each portion of the records received at those multiple points in time, with each of the second regression parameters corresponding to one of the portions of the records. The second regression parameter is used to determine the baseline value. Determine whether the difference between the first regression parameter and the baseline value is within a predetermined range. A device described in any one of clauses 70 to 80 that determines that an abnormality has occurred based on the determination that the difference is not within a specified range. 82. The apparatus according to Clause 81, wherein each of the first and second regression parameters includes one of the slope or the intercept. 83. A device described in any one of the clauses 81 to 82, in which part of the record does not include the record described above. 84. A device described in any one of the clauses 81 to 83, which includes all records other than those mentioned above. 85. The performance measurement criteria are at least one of the following: image quality or geometric distortion of an image generated in the imaging process from the field of view (FOV) of one of multiple beams, as specified in any one of the apparatus described in any one of the clauses 70 to 84. 86. The apparatus as described in Clause 85, whose performance measurement criteria indicate geometric distortion of an image and include at least one of aberration, structural distortion, or change in geometric shape. 87. The apparatus described in any one of clauses 85 to 86, wherein the performance measurement criteria indicate image quality and include a variable representing at least one of the following: image noise level, image brightness, image contrast, image sharpness, or image resolution. 88. A set of commands further instructs the device, The apparatus described in Clause 87 uses a portion of the image corresponding to the FOV region to determine data representing the noise level, wherein this region does not have a pattern. 89. A set of instructions further sends to the device: The apparatus according to Clause 88, which causes a region to be determined based on at least one of the layout data or image data of the FOV region. 90. A set of instructions is further given to the device, An apparatus according to any one of the clauses 87 to 89, which causes an image to determine at least one of brightness or contrast based on the gray level of the image. 91. A set of instructions further sends to the device: Extract a step profile from the pattern edges of the image. An apparatus according to any one of the clauses 87 to 90, which causes the step profile to determine at least one of sharpness or resolution. 92. A non-temporary computer-readable medium storing a set of instructions, executable by at least one processor of the device, to cause the device to perform a method for monitoring the health of a multibeam system, the method being: In the imaging process, the recording of performance metrics for multiple beams of a multi-beam system, By comparing the records, it is possible to determine whether one of the multiple beams is showing abnormalities, A non-transient, computer-readable medium that includes triggering a notification when the beam shows abnormality. 93. A method for monitoring the integrity of a multibeam scanning electron microscope (SEM), To receive records of performance metrics for multiple beams of a multi-beam system, wherein the multiple beams include the first beam and other beams. By comparing the data derived from the records, we can determine whether the first beam exhibits abnormal performance compared to the other beams, A method including, if, based on a comparison, it is determined that the first beam is exhibiting abnormal performance, a notification is triggered. 94. Comparing data is further, To determine the statistical values ​​for performance measurement criteria of other beams, The method according to Clause 93, which includes determining that the difference between the performance metric and statistical value of the first beam is greater than or equal to a predetermined threshold. 95. The method in Clause 93, wherein the statistical value includes one of the mean, median, variance, standard deviation, or root mean square of other beam performance metrics.

[0082]

[0086] The block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware or software products according to various exemplary embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for performing a specified logical function. It should be understood that in some alternative embodiments, the functions shown within a block may be performed in an order other than that shown in the figure. For example, two consecutively shown blocks may be executed substantially simultaneously or performed simultaneously, or, depending on the functions involved, two blocks may sometimes be executed in reverse order. Some blocks may be omitted. It should be understood that each block in the block diagram, and combinations of blocks, may be implemented by a special-purpose hardware-based system or by a combination of special-purpose hardware and computer instructions for performing a specified function or operation.

[0083]

[0087] The embodiments of this disclosure are not limited to those described above and illustrated in the accompanying drawings, and it should be understood that various modifications and changes can be made without departing from the scope of the present invention.

Claims

1. A method for monitoring the performance of a multibeam system, Receiving records of performance metrics for multiple beams of the multi-beam system operating during the imaging process, wherein each record is associated with a particular beam. Whether or not a beam anomaly has occurred is determined based on a baseline value determined using a portion of the aforementioned record, In response to the aforementioned determination that the aforementioned abnormality has occurred, to provide a notification of the abnormality, Includes, The performance measurement criterion indicates the image quality of the image generated in the imaging process from the field of view (FOV) of one of the multiple beams. The image quality includes a variable representing the noise level of the image, The aforementioned method, Determining data representing the noise level using a portion of the image corresponding to the FOV region, wherein the region does not have a pattern. Based on the layout data of the region of the FOV, the region is determined. Methods that further include the above.

2. A system for monitoring the performance of a multibeam system, A controller that executes a set of instructions to the system During the imaging process, the system receives records of performance measurement criteria for multiple beams of the multi-beam system, and each record is associated with a specific beam. Whether or not a beam anomaly has occurred is determined based on a baseline value determined using a portion of the aforementioned record. A controller is included, which has a circuit for providing a notification of the abnormality in response to the determination that the aforementioned abnormality has occurred. The performance measurement criterion indicates the image quality of the image generated in the imaging process from the field of view (FOV) of one of the multiple beams. The image quality includes a variable representing the noise level of the image, The aforementioned set of instructions further transmits to the system: Using a portion of the image corresponding to the FOV region, data representing the noise level is determined, and the region does not have a pattern. A system for determining the region based on the layout data of the region of the FOV.

3. The set of instructions for causing the system to determine whether the abnormality in the beam has occurred further causes the system to: The test value of the beam is determined using at least one value of the beam recording received at multiple points in time during the imaging process. Determine whether the difference between the test value and the baseline value of the beam is within a predetermined range. The system according to claim 2, which determines that the abnormality has occurred based on the determination that the difference is not within the predetermined range.

4. The system according to claim 2, further comprising a database configured to store at least one of the baseline value or the record.

5. The system further includes a database configured to store at least one of the baseline value or the record, The aforementioned set of instructions further transmits to the system: The system according to claim 3, wherein, before determining whether the difference is within the predetermined range, at least one of the records or the baseline value is read from the database.

6. The aforementioned set of instructions further transmits to the system: The system according to claim 2, wherein the baseline value is determined after a predetermined event.

7. The set of instructions causing the system to determine the baseline value after the predetermined event further involves the system: The system according to claim 6, which determines the baseline value based on an initial record of the performance measurement criteria received after the predetermined event.

8. The system according to claim 6, wherein the predetermined event includes the completion of one of the following: maintenance of the multibeam system or calibration of the multibeam system.

9. The system according to claim 6, wherein the predetermined event includes each value of the record changing by a shift value, and the shift value does not deviate from the average value of the records beyond a predetermined range.

10. The aforementioned set of instructions further transmits to the system: The system according to claim 2, wherein the baseline value is determined as a statistical value of the value of the portion of the record.

11. The system according to claim 3, wherein the test value of the beam includes a statistical value of at least one of the records of the beam.

12. The system according to claim 10, wherein the statistical value includes one of the mean, median, variance, standard deviation, or root mean square.

13. The system according to claim 3, wherein the test value of the beam includes the recorded value of the beam received at one of the plurality of time points.

14. The set of instructions for causing the system to determine whether the abnormality in the beam has occurred further causes the system to: A first regression parameter is determined from regressions performed on the values ​​of the record received at multiple points in time, and a second regression parameter is determined from regressions performed on each of the values ​​of the portion of the record received at multiple points in time, wherein each of the second regression parameters corresponds to one of the portions of the record. The baseline value is determined using the second regression parameter described above. Determine whether the difference between the first regression parameter and the baseline value is within a predetermined range. The system according to claim 2, which determines that the abnormality has occurred based on the determination that the difference is not within the predetermined range.

15. A non-temporary computer-readable medium storing a set of instructions that can be executed by at least one processor of the device to cause the device to perform a method for monitoring the performance of a multibeam system, the method being: In the imaging process, the recording of performance measurement criteria for multiple beams of the multi-beam system, where each recording is associated with a specific beam, Whether or not a beam anomaly has occurred is determined based on a baseline value determined using a portion of the aforementioned record, In response to the aforementioned determination that the aforementioned abnormality has occurred, to provide a notification of the abnormality, Includes, The performance measurement criterion indicates the image quality of the image generated in the imaging process from the field of view (FOV) of one of the multiple beams. The image quality includes a variable representing the noise level of the image, The aforementioned method, Determining data representing the noise level using a portion of the image corresponding to the FOV region, wherein the region does not have a pattern. Based on the layout data of the region of the FOV, the region is determined. Non-temporary computer-readable media, including the above.

Images