Self-referencing health monitoring system for multi-beam SEM tools

Abstract

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

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Application No. 62 / 893,139, filed on August 28, 2019, and the entire contents of which are incorporated herein by reference. Technical Field

[0003] The description herein relates to the field of charged particle beam systems, and more particularly to health monitoring for multi-beam scanning electron microscope (SEM) systems. Background Art

[0004] During the manufacture of integrated circuits (ICs), unfinished or completed circuit components are inspected to ensure that they are manufactured according to design and are free of defects. Inspection systems utilizing optical microscopes or charged particle (e.g., electron) beam microscopes such as scanning electron microscopes (SEMs) can be employed. SEMs deliver low energy electrons (e.g., <1 keV) to a surface and use a detector to record secondary or backscattered electrons that leave the surface. By recording electrons for different excitation locations on the surface, images with nanometer-scale spatial resolution can be created.

[0005] SEMs can be single-beam systems or multi-beam systems. Single-beam SEMs use a single electron beam to scan a surface, while multi-beam SEMs use multiple electron beams to scan a surface simultaneously. Multi-beam systems can achieve higher imaging throughput than single-beam systems. However, multi-beam systems also have a more complex structure, so they lack certain structural flexibility compared to single-beam systems. In addition, due to their higher complexity, multi-beam SEMs are more prone to operating errors and problems than single-beam SEMs. Summary of the invention

[0006] Embodiments of the present disclosure provide systems and methods for image enhancement. In some embodiments, a method for monitoring the performance of a multi-beam system may include receiving a record of performance metrics for beams of the multi-beam system during imaging, each record associated with a beam. The method may also include determining whether an anomaly of the beam has occurred based on a baseline value determined using a portion of the record. The method may also include providing an anomaly indication in response to a determination that an anomaly has occurred.

[0007] In some embodiments, a system for monitoring the performance of a multi-beam system is disclosed. The system may include a controller having a circuit device for executing an instruction set. The controller may execute the instruction set to cause the system to receive a record of performance metrics for beams of the multi-beam system during imaging, each record associated with a beam. The controller may also execute the instruction set to cause the system to determine whether an anomaly of the beam has occurred based on a baseline value determined using a portion of the record. The controller may also execute the instruction set to cause the system to provide an anomaly indication in response to determining that an anomaly has occurred.

[0008] In some embodiments, a non-transitory computer-readable medium is disclosed. The non-transitory computer-readable medium may store a set of instructions executable by at least one processor of a device to cause the device to perform a method for monitoring performance of a multi-beam system. The method may include receiving a record of performance metrics for beams of the multi-beam system during imaging, each record associated with a beam. The method may also include determining whether an anomaly of the beam has occurred based on a baseline value determined using a portion of the record. The method may also include providing an anomaly indication in response to a determination that an anomaly has occurred.

[0009] In some embodiments, a method of monitoring the health of a multi-beam system may include receiving a record of performance metrics for beams of the multi-beam system during imaging. The method may also include comparing the record to determine whether a beam in the beams exhibits an anomaly. The method may also include triggering a notification when a beam exhibits an anomaly.

[0010] In some embodiments, an apparatus for monitoring the health of a multi-beam system is disclosed. The apparatus may include a controller having a circuit device that executes an instruction set. The controller may execute the instruction set to cause the system to receive a record of performance metrics for beams of the multi-beam system during imaging. The controller may also execute the instruction set to cause the system to compare the record to determine whether a beam in the beams exhibits an anomaly. The controller may also execute the instruction set to cause the system to trigger a notification when a beam exhibits an anomaly.

[0011] In some embodiments, a non-transitory computer-readable medium is disclosed. The non-transitory computer-readable medium may store a set of instructions executable by at least one processor of a device to cause the device to perform a method of monitoring the health of a multi-beam system. The method may include receiving a record of performance metrics for beams of the multi-beam system during imaging. The method may also include comparing the record to determine whether a beam in the beam exhibits an anomaly. The method may also include triggering a notification when the beam exhibits an anomaly.

[0012] In some embodiments, a method of monitoring the health of a multi-beam system may include receiving a record of performance metrics for a plurality of beams of the multi-beam system, the plurality of beams including a first beam and other beams. The method may also include comparing data derived from the record to determine whether the first beam exhibits abnormal performance compared to the other beams. The method may also include triggering a notification when it is determined based on the comparison that the first beam exhibits abnormal performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system consistent with embodiments of the present disclosure.

[0014] Figure 2 It is a diagram showing an embodiment consistent with the present disclosure. Figure 1 Schematic diagram of an exemplary multi-beam system that is a portion of an exemplary charged particle beam inspection system.

[0015] Figure 3 is a flow chart illustrating an exemplary method for monitoring performance of a multi-beam system consistent with embodiments of the present disclosure.

[0016] Figure 4 is a flow chart illustrating another exemplary method for monitoring performance of a multi-beam system consistent with embodiments of the present disclosure.

[0017] Figure 5 is a graphical representation of time-dependent values ​​of a performance metric for beams of a multi-beam system consistent with embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, wherein, unless otherwise specified, the same numbers in different drawings represent the same or similar elements. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with various aspects related to the subject matter recited in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the present disclosure is not limited thereto. Other types of charged particle beams may be similarly applied. In addition, other imaging systems such as optical imaging, photoelectric detection, X-ray detection, etc. may be used.

[0019] Electronic devices are made up of circuits formed on a piece of silicon called a substrate. Many circuits can be formed together on the same piece of silicon and are called an integrated circuit or IC. The size of these circuits has been reduced significantly so that more circuits can be mounted on a substrate. For example, the IC chip in a smartphone can be as small as a miniature Figure 1Although small, it may include more than 2 billion transistors, each less than 1 / 1000 the size of a human hair.

[0020] Manufacturing these extremely small ICs is a complex, time-consuming, and expensive process that often involves hundreds of individual steps. An error in even one step can cause a defect in the finished IC, rendering it useless. Therefore, one goal of the manufacturing process is to avoid such defects in order to maximize the number of functional ICs manufactured in the process, that is, to improve the overall yield of the process.

[0021] An integral part of improving yield is monitoring the chip manufacturing process to ensure that it is producing a sufficient number of functional integrated circuits. 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). The SEM can be used to image these extremely small structures, in effect taking a "photograph" of the wafer structure. The image can be used to determine whether the structure was formed correctly and in the correct location. If the structure is defective, the process can be adjusted to make the defect less likely to occur again.

[0022] The working principle of SEM is similar to that of a camera. A camera takes pictures by receiving and recording the brightness and color of light reflected or emitted from a person or object. SEM takes "pictures" by receiving and recording the energy or number of electrons reflected or emitted from a structure. Before taking such a "picture", an electron beam can be provided to the structure, and when the electrons are reflected or emitted ("emitted") from the structure, the detector of the SEM can receive and record the energy or number of those electrons to generate an image. In order to take such "pictures", some SEMs use a single electron beam (referred to as "single-beam SEMs"), while some SEMs use multiple electron beams (referred to as "multi-beam SEMs") to take multiple "pictures" of the wafer. By using multiple electron beams, the SEM can provide more electron beams on the structure to obtain these multiple "pictures", resulting in more electrons being emitted from the structure. Therefore, the detector can receive more emitted electrons at the same time and generate images of the wafer structure with higher efficiency and faster speed.

[0023] However, compared to a single-beam SEM, a multi-beam SEM includes more components, has a more complex design, and provides a less obvious signal on one or more of its electronic detectors. Monitoring the health of a multi-beam SEM is important because during operation, if even one beam is not functioning properly, the overall performance of the multi-beam SEM may be greatly affected. In addition, when performance issues occur, multi-beam SEMs require more time, cost, and manpower for technicians to diagnose due to their more complex design. However, due to their less obvious signal, health monitoring of a multi-beam SEM is more difficult than a single-beam SEM.

[0024] The present disclosure describes, among other things, methods and systems for health monitoring of a multi-beam SEM. In some embodiments, to monitor health, a "baseline" condition may be used as a measure of performance data of a multi-beam SEM. Operating parameters or statistics of the performance data of the multi-beam SEM may be collected and compared to the baseline conditions. If an operating parameter or statistical anomaly occurs based on the comparison, an anomaly in the performance of the multi-beam SEM may be reported and further action may be taken. In one example, a multi-beam SEM may use a self-referencing scheme that may utilize historical performance data of the multi-beam SEM to determine a baseline condition based on history or utilize individual performance data of each beam to determine a baseline condition based on group performance. The baseline condition based on history or the baseline condition based on group performance is used as a reference for monitoring the health of the multi-beam SEM. Essentially, by using a baseline condition based on history, the current performance of a beam of the multi-beam SEM is compared to a previous ("historical") performance of the same beam measured at an earlier time when the multi-beam system was operating, and an anomaly is identified if the current performance outlie the historical performance. Using a benchmark based on group performance, the current performance of a beam of the multi-beam SEM is compared to the current performance of other beams measured while the multi-beam system is operating, and an anomaly is identified if the current performance of a beam exceeds the current performance of other beams.

[0025] By using a self-referencing scheme, the health of a multi-beam SEM can be continuously monitored without reference to external inputs, thereby adapting to changing operating conditions and automatically identifying anomalies with greater accuracy. As can be seen, the accuracy and validity of the reference conditions are important in health monitoring for a multi-beam SEM. If external inputs are relied upon to provide such reference conditions, health monitoring may require updating the external inputs when the operating conditions of the multi-beam SEM change, which would make it difficult to continuously monitor health, adapt to changes in operating conditions, and automatically identify anomalies with high accuracy.

[0026] For clarity, the relative dimensions of components in the drawings may be exaggerated. 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 the various embodiments are described.

[0027] As used herein, unless expressly stated otherwise, the term "or" encompasses all possible combinations unless not feasible. For example, if it is stated that a component can include A or B, then unless expressly stated otherwise 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.

[0028] Figure 1An exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure is illustrated. The EBI system 100 may be used for imaging. Figure 1 As shown in , 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 located in the main chamber 101. The EFEM 106 includes a first loading port 106a and a second loading port 106b. The EFEM 106 may include additional (multiple) loading ports. The first loading port 106a and the second loading port 106b receive a wafer front opening unified cabin (FOUP), which contains wafers (e.g., semiconductor wafers or wafers made of (multiple) other materials) or samples to be inspected (wafers and samples can be used interchangeably). A lot is a plurality of wafers that can be loaded in batches for processing.

[0029] One or more robots (not shown) in 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), which removes gas molecules in the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, one or more robots (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), which removes gas molecules in 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.

[0030] The controller 109 is electrically connected to the electron beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. Figure 1 106, but it will be appreciated that the controller 109 may be part of the structure.

[0031] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or specific electronic device capable of manipulating or processing information. For example, the processor may include any combination of any number of the following devices: a central processing unit (or "CPU"), a graphics processing unit (or "GPU"), an optical processor, a programmable logic controller, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a programmable logic array (PLA), a programmable array logic (PAL), a general array logic (GAL), a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a system on a chip (SoC), an application specific integrated circuit (ASIC), and any type of circuit capable of data processing. The processor may also be a virtual processor, which includes one or more processors distributed on multiple machines or devices coupled via a network.

[0032] In some embodiments, the controller 109 may also include one or more memories (not shown). The memory may be a general or specific electronic device capable of storing code and data accessible to the processor (e.g., via a bus). For example, the memory may include any combination of any number of the following devices: random access memory (RAM), read-only memory (ROM), optical disk, magnetic disk, hard drive, solid-state drive, flash drive, secure digital (SD) card, memory stick, compact flash (CF) card, or any type of storage device. The code may include an operating system (OS) and one or more applications (or "apps") for specific tasks. The memory may also be a virtual memory, which includes one or more memories distributed on multiple machines or devices coupled via a network.

[0033] Now to Figure 2 Reference is made to a schematic diagram illustrating an exemplary electron beam tool 104 consistent with an embodiment of the present disclosure, which includes a Figure 1 The multi-beam inspection tool of a part of the EBI system 100 of the present invention. The multi-beam electron beam tool 104 (also referred to as the device 104 in this article) includes an electron source 201, a gun hole plate 271, a condenser lens 210, a source conversion unit 220, a main projection system 230, a motorized stage 209, and a sample holder 207, which is supported by the motorized stage 209 to hold a sample 208 to be inspected (for example, a wafer or a photomask). The multi-beam electron beam tool 104 may also include a secondary projection system 250 and an electronic detection device 240. The main projection system 230 may include an objective lens 231. The electronic detection device 240 may include a plurality of detection elements 241, 242 and 243. A beam splitter 233 and a deflection scanning unit 232 may be positioned inside the main projection system 230.

[0034] The electron source 201, the aperture plate 271, the condenser lens 210, the source conversion unit 220, the beam splitter 233, the deflection scanning unit 232 and the primary projection system 230 may be aligned with the primary optical axis 204 of the apparatus 104. The secondary projection system 250 and the electron detection device 240 may be aligned with the secondary optical axis 251 of the apparatus 104.

[0035] The electron source 201 may include a cathode (not shown) and an extractor or an anode (not shown), wherein during operation, the electron source 201 is configured to emit primary electrons from the cathode, and the primary electrons are extracted or accelerated by the extractor and / or the anode to form a primary electron beam 202, which forms a primary electron beam cross (virtual or real) 203. The primary electron beam 202 can be visualized as being emitted from the primary electron beam cross 203.

[0036] The source conversion unit 220 may include an imaging element array (not shown), an aberration compensator array (not shown), a beam limiting aperture array (not shown), and a pre-bent micro-deflector array (not shown). In some embodiments, the pre-bent micro-deflector array deflects the multiple primary sub-beams 211, 212, 213 of the primary electron beam 202 so that they normally enter the beam limiting aperture array, the imaging element array, and the aberration compensator array. In some embodiments, the condenser lens 210 is designed to focus the primary electron beam 202 into a parallel beam and vertically incident on the source conversion unit 220. The imaging element array may include a plurality of micro-deflectors or micro-lenses to affect the multiple primary sub-beams 211, 212, 213 of the primary electron beam 202 and form a plurality of parallel images (virtual or real) of the primary beam cross 203, one micro-deflector or micro-lens for each of the primary sub-beams 211, 212, and 213. In some embodiments, the aberration compensator array may include a field curvature compensator array (not shown) and an astigmatism compensator array (not shown). The field curvature compensator array may include a plurality of micro lenses to compensate for field curvature aberrations of the primary beamlets 211, 212, and 213. The astigmatism compensator array may include a plurality of micro astigmatists to compensate for astigmatism aberrations of the primary beamlets 211, 212, and 213. The beam limiting aperture array may be configured to limit the diameters of the individual primary beamlets 211, 212, and 213. Figure 2 As an example, three primary sub-beams 211, 212 and 213 are shown, and it should be understood that the source conversion unit 220 can be configured to form any number of primary sub-beams. The controller 109 can be connected to Figure 1109. The controller 109 may be used to control various parts of the EBI system 100, such as the source conversion unit 220, the electronic detection device 240, the main projection system 230, or the motorized stage 209. In some embodiments, as explained in further detail below, the controller 109 may perform various image and signal processing functions. The controller 109 may also generate various control signals to govern the operation of the charged particle beam inspection system.

[0037] The condenser lens 210 is configured to focus the primary electron beam 202. The condenser lens 210 may also be configured to adjust the current of the primary sub-beams 211, 212, and 213 downstream of the source conversion unit 220 by changing the focusing power of the condenser lens 210. Alternatively, the current may be changed by changing the radial size of the beam-limiting aperture within the beam-limiting aperture array corresponding to the individual primary sub-beams. The current may be changed by changing the radial size of the beam-limiting aperture and the focusing power of the condenser lens 210. The condenser lens 210 may be a movable condenser lens, which may be configured so that the position of its first principal plane is movable. The movable condenser lens may be configured to be magnetic, which may cause the off-axis sub-beams 212 and 213 to illuminate the source conversion unit 220 at a rotation angle. The rotation angle changes with the focusing power of the movable condenser lens or the position of the first principal plane. The condenser lens 210 may be an anti-rotation condenser lens, which may be configured to keep the rotation angle unchanged while changing the focusing power of the condenser lens 210. In some embodiments, the condenser lens 210 may be a movable anti-rotation condenser lens, wherein the rotation angle does not change when its focusing power and the position of its first principal plane change.

[0038] The objective lens 231 can be configured to focus the sub-beams 211, 212, and 213 onto the sample 208 for inspection, and in the current embodiment, three probe spots 221, 222, and 223 can be formed on the surface of the sample 208. The aperture plate 271 is configured to block peripheral electrons of the primary electron beam 202 in operation to reduce the Coulomb effect. The Coulomb effect may expand the size of each probe spot 221, 222, and 223 of the primary sub-beams 211, 212, and 213, and thus degrade the inspection resolution.

[0039] The beam splitter 233 may be, for example, a device that generates an electrostatic dipole field and a magnetic dipole field ( Figure 2 The beam splitter 233 may be configured to apply electrostatic forces on individual electrons of the primary beamlets 211, 212, and 213 via an electrostatic dipole field. The electrostatic forces are equal in magnitude but opposite in direction to the magnetic forces applied on the individual electrons by the magnetic dipole field of the beam splitter 233. The primary beamlets 211, 212, and 213 may thus pass at least substantially straight through the beam splitter 233 at a deflection angle that is at least substantially zero.

[0040] The deflection scanning unit 232 is configured in operation to deflect the primary beamlets 211, 212 and 213 to scan the probe spots 221, 222 and 223 over individual scanning areas in a portion of the surface of the sample 208. In response to the primary beamlets 211, 212 and 213 or the probe spots 221, 222 and 223 incident on the sample 208, electrons are ejected from the sample 208 and three secondary electron beams 261, 262 and 263 are generated. Each secondary electron beam 261, 262, 263 and 263 typically includes secondary electrons (having an electron energy of ≤50 eV) and backscattered electrons (having an electron energy between 50 eV and the landing energy of the primary beamlets 211, 212 and 213). The beam splitter 233 is configured to deflect the secondary electron beams 261, 262 and 263 toward the sub-projection system 250. The secondary projection system 250 then focuses the secondary electron beams 261, 262 and 263 onto the detection elements 241, 242 and 243 of the electron detection device 240. The detection elements 241, 242 and 243 are arranged to detect the corresponding secondary electron beams 261, 262 and 263 and generate corresponding signals, which are sent to the controller 109 or a signal processing system (not shown), for example to construct a corresponding scan area of ​​the sample 208.

[0041] In some embodiments, the detection elements 241, 242, and 243 detect the corresponding secondary electron beams 261, 262, and 263, respectively, and generate corresponding intensity signals (not shown) to the image processing system (e.g., the controller 109). In some embodiments, each detection element 241, 242, and 243 may include one or more pixels. The intensity signal output of the detection element may be the sum of the signals generated by all pixels within the detection element.

[0042] In some embodiments, the controller 109 may include an image processing system, which includes an image collector (not shown), a storage device (not shown). The image collector may include one or more processors. For example, the image collector may include a computer, a server, a mainframe, a terminal, a personal computer, any type of mobile computing device, etc. or a combination thereof. The image collector may be communicatively coupled to the electronic detection device 240 of the device 104 through a medium such as an electrical conductor, a fiber optic cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, a radio, etc. or a combination thereof. In some embodiments, the image collector may receive a signal from the electronic detection device 240 and may construct an image. The image collector may thus collect an image of the sample 208. The image collector may also perform various post-processing functions, such as generating contours, superimposing indicators on the collected image, etc. The image collector may be configured to perform adjustments to the brightness and contrast of the collected image, etc. In some embodiments, the memory may be a storage medium such as a hard disk, a flash drive, a cloud storage device, a random access memory (RAM), other types of computer-readable memory, etc. The storage device may be coupled to the image collector and may be used to save the scanned native image data as an original image and a post-processed image.

[0043] In some embodiments, the image collector may acquire one or more images of the sample based on an imaging signal received from the electronic detection device 240. The imaging signal may correspond to a scanning operation for implementing charged particle imaging. The acquired image may be a single image including multiple imaging areas. The single image may be stored in a storage device. The single image may be an original image that can be divided into multiple areas. Each area may include an imaging area that contains features of the sample 208. The acquired image may include multiple images of a single imaging area of ​​the sample 208 sampled multiple times in a time series. Multiple images may be stored in a storage device. In some embodiments, the controller 109 may be configured to perform an image processing step on multiple images of the same position of the sample 208.

[0044] In some embodiments, the controller 109 may include a measurement circuit (e.g., an analog-to-digital converter) to obtain the distribution of the detected secondary electrons. The electron distribution data collected during the detection time window, combined with the corresponding scan path data of each primary beamlet 211, 212, and 213 incident on the wafer surface, can be used to reconstruct an image of the inspected wafer structure. The reconstructed image can be used to reveal various features of the internal or external structure of the sample 208, and thus can be used to reveal any defects that may be present in the wafer.

[0045] In some embodiments, the controller 109 may control the motorized stage 209 to move the sample 208 during the inspection of the sample 208. In some embodiments, the controller 109 may enable the motorized stage 209 to continuously move the sample 208 at a constant speed in one direction. In other embodiments, the controller 109 may enable the motorized stage 209 to change the movement speed of the sample 208 over time depending on the steps of the scanning process.

[0046] Although Figure 2 The apparatus 104 is shown using three primary electron beams, but it should be understood that the apparatus 104 may use two or more primary electron beams. The present disclosure does not limit the number of primary electron beams used in the apparatus 104.

[0047] Compared to SEM inspection tools that use a single electron beam (referred to as "single-beam systems"), multi-beam SEM inspection tools ("multi-beam systems") can use multiple electron beams to simultaneously apply electrons to a sample (e.g., Figure 2 The sample 208 in the image is imaged. The throughput of a multi-beam inspection tool may be higher than that of a single-beam system. However, due to their high complexity, multi-beam systems may lack some of the structural flexibility of single-beam systems. In addition, the signal-to-noise ratio (SNR) of the signal of a multi-beam system may be lower than that of a single-beam system, which may make it difficult to monitor its health. For example, the brightness of an SEM image generated by a multi-beam system may have accumulated drift. If this drift is not identified, it may propagate into the image analysis and cause errors. Therefore, it is very important to monitor the health of a multi-beam system and provide it with accurate and timely diagnostics, which can reduce manpower and maintenance costs as well as cycle time.

[0048] In some embodiments of the present disclosure, a self-referencing scheme may be used to monitor the health of a multi-beam system. The self-referencing scheme may monitor the performance metric of a beam using a baseline condition or a group performance metric as a reference. The performance metric may indicate at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam during imaging. The performance metric may indicate the performance of the imaging of the beam. The baseline condition may be a fixed benchmark, behavior, or specification of the performance metric. The baseline condition may depend on various conditions, such as the landing energy of the beam, the current of the beam, the pixel size of the sample, or the specification of the sample.

[0049] In some embodiments, a multi-beam system may select one or more performance metrics of multiple beams of the multi-beam system to record. The multi-beam system may record performance metrics of multiple beams and determine a baseline condition therefrom. Such a baseline condition may not be available in a single beam system. In some embodiments, the multi-beam system may begin recording values ​​at a predetermined point in time.

[0050] In some embodiments, the multi-beam system can determine whether a beam has an abnormality based on a comparison between a performance metric of the beam and a reference condition. In some embodiments, the multi-beam system can determine whether a beam has an abnormality based on a comparison between a performance metric of the beam and a group performance metric of multiple beams. By using various performance metrics in a self-referencing scheme, anomalies can be quickly diagnosed to facilitate subsequent analysis of the root cause, thereby improving the efficiency of troubleshooting.

[0051] In some embodiments, the multi-beam system can perform the above process in an automated manner. By doing so, the multi-beam system can be able to continuously monitor the health of the multi-beam system, detect anomalies, and diagnose one or more modules (e.g., hardware modules or software modules) causing the problem based on the detected anomalies.

[0052] Figure 3-Figure 4 1 is a flow chart illustrating an example of methods 300-400 consistent with embodiments of the present disclosure. Methods 300-400 may be performed by a monitor module that may be coupled to a charged particle beam device (e.g., EBI system 100). For example, a controller (e.g., Figure 1-Figure 2 The controller 109 in may include a monitoring module and may be programmed to implement methods 300-400.

[0053] Figure 3 is a flow chart illustrating an exemplary method for monitoring performance of a multi-beam system consistent with embodiments of the present disclosure.

[0054] At step 302, a controller (eg, Figure 1-Figure 2 The controller 109 in the embodiment of the present invention receives records of performance metrics for beams of the multi-beam system during imaging. In some embodiments, the controller can receive the records from a database coupled to the charged particle beam device (e.g., the EBI system 100). For example, the database can receive operational data of the multi-beam system and pre-process it into records of performance metrics for the beams. In some embodiments, each record can be associated with a beam. The imaging process can be performed by performing a multi-beam imaging on a sample (e.g., Figure 2 The process of scanning a beam over a surface of a sample 208 in the sample 208 to generate a SEM image. In some embodiments, the controller may receive a record for a plurality of performance metrics. In some embodiments, the controller may store the record of performance metrics in a database, such as a database in a memory accessible to the controller.

[0055] In some embodiments, the performance metric may include any variable or parameter that indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam in the beam during imaging. For example, for each beam, the controller may control the generation of a SEM image for the FOV of the beam (e.g., by using Figure 2 In some embodiments, the performance metric indicating geometric distortion of the image may include a variable or parameter indicating or characterizing aberration, structural distortion, or change in geometry. In some embodiments, the performance metric indicating image quality may include a variable or parameter indicating, characterizing, or representing the noise level of the image, the brightness of the image, the contrast of the image, the sharpness of the image, or the resolution of the image.

[0056] For example, when the performance metric includes a variable or parameter indicating, characterizing or representing the noise level of the image, data indicating or characterizing the noise level of the image ("noise level data") may be derived or generated from one or more signals ("background signals") received by the electronic detection device 240 from an area without a design circuit pattern ("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 a design circuit pattern and a background area. The controller may then determine the signal level corresponding to the background area detected by the electronic detection device 240. In some embodiments, the controller may determine the area based on at least one of the layout data of the area of ​​the FOV or the image data of the image. For example, the design data may include a file in a Graphic Database System (GDS) format, a Graphic Database System II (GDSII) format, an Open Artwork System Interchange Standard (OASIS) format, a California Institute of Technology intermediate format (CIF), etc., which indicates the pattern distribution of the design circuit. The design data may include information indicating whether the area has a pattern. For another example, the background area may be determined by analyzing the image of the FOV, such as by performing feature extraction on the image. For example, a region where no features are extracted after feature extraction may be determined as a background region.

[0057] For another example, when the performance metric includes a variable or parameter indicating, characterizing, or representing the brightness or contrast of an image, the controller can determine at least one of the brightness or contrast based on the grayscale of the image. For example, the image of the FOV can be a grayscale SEM image. By analyzing the grayscale values ​​of the image pixels, the controller can determine the brightness, contrast, or both.

[0058] For another example, when the performance metric includes a variable or parameter indicating, characterizing or representing resolution or sharpness, the resolution or sharpness can be used to determine whether the beam is out of focus. In some embodiments, the controller can determine at least one of sharpness or resolution from the step distribution. Before doing so, the controller can extract the step distribution from the pattern edge of the image. The pattern edge can be the edge of the pattern in the image. For example, the controller can perform the above-mentioned feature extraction on the grayscale image of the FOV to extract the pattern. The controller can determine the edge from the pattern. The edge can correspond to the feature of the circuit with a wall structure, such as a groove, a groove, a hole, a line or a step. Ideally, the edge can have a 90° wall between two surfaces of different heights, and the grayscale value corresponding to the edge in the image can have a discontinuous jump. However, due to the actual lithography and etching processes in the manufacturing process and the uncertainty of SEM imaging, the edge may not have a 90° wall, and the grayscale value corresponding thereto may have a continuous change, which is called a "step distribution". In some embodiments, the controller can determine the rate of change of the grayscale value of the step distribution, where the greater the rate of change, the higher the sharpness or resolution may be.

[0059] Return to reference Figure 3 At step 304, the controller determines whether an abnormality of the beam occurs based on the parameter value determined from a portion of the usage record. The parameter value may be a historically based baseline value or a group performance-based baseline value. During a current operation cycle of the multi-beam SEM where the parameter value is based on a historically based baseline value, the controller may use performance data from a previous operation cycle of the multi-beam SEM to determine the parameter value, such as an "initial period" or "commissioning" phase of operation of the multi-beam SEM immediately after maintenance or calibration. During a current operation cycle of the multi-beam SEM where the parameter value is a group performance-based baseline value, the controller may use performance data from the current operation cycle of the multi-beam SEM to determine the parameter value.

[0060] In some embodiments, in order to determine whether an abnormality of a beam occurs, the controller may determine the parameter value as a historically based baseline value for the performance metric of the beam based on the recorded historical value. In some embodiments, the controller may determine the baseline value after a predetermined event. For example, the controller may determine the baseline value based on an initial record of the performance metric received after the predetermined event. In some embodiments, the predetermined event may include one of completing maintenance of the multi-beam system or calibration of the multi-beam system. For example, after completing maintenance or calibration, the multi-beam system may be run again and the performance metric value of each beam may be recorded. Such values ​​may be used as a reference for the performance metric and may be referred to as "initial recording". In some embodiments, the predetermined event may include that each value recorded changes an offset value, and the offset value does not deviate from the average value of the value in the record that exceeds a predetermined range. For example, after maintenance or calibration, for each beam, the controller may store the recorded value corresponding to the beam, and the value may fall within a predetermined tolerance range. For example, the tolerance range may be configured by a user of the multi-beam system. In some embodiments, the controller may also determine the test value of the beam using at least one value of the record of the beam received at multiple time points during the imaging process.

[0061] In some embodiments, to determine whether an abnormality of a beam occurs, the controller may determine the parameter value as a baseline value based on group performance, such as a statistic of the values ​​of a portion of the record. For example, the controller may compare data derived from the record of the beam to determine whether the first beam exhibits abnormal performance compared to the other beams. The controller may determine the statistics of the performance metrics of the other beams, and may determine that the difference between the performance metric for the first beam and the statistics is greater than or equal to a predetermined threshold. The statistics may include, for example, the mean value of the record, the median value of the record, the variance value of the record, the standard deviation value of the record, or the root mean square value of the record. In some embodiments, a portion of the record may be all records. In some embodiments, a portion of the record may be a subset of the record. For example, if a multi-beam system has 25 sub-beams, the controller may receive values ​​corresponding to the records of the 25 sub-beams. The controller may use multiple records (e.g., 5, 10, 18, 25, etc.) to further determine the statistics of the record. The controller may then determine the statistics as a baseline value based on group performance.

[0062] In some embodiments, after determining the parameter value (e.g., a baseline value based on history or a baseline value based on group performance), the controller may also determine whether the difference between the recorded test value and the baseline is within a predetermined range. The controller may then determine that an anomaly has occurred based on a 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 retrieve at least one of the record or the parameter value from the database before determining whether the difference is within the predetermined range.

[0063] Example benchmark values ​​can be found at Figure 5 is shown in . Figure 5 is a graphical representation of time-dependent values ​​of a performance metric for beams of a multi-beam system consistent with an embodiment of the present disclosure. Figure 5 In FIG. 1 , the horizontal axis represents the timeline (from left to right), and the vertical axis represents the value of the performance metric. The timeline includes several time points, including time points T0-T5. Since the multi-beam system includes multiple beams, Figure 5 A plurality of curves 502 are shown. Each of the curves 502 may represent time-dependent values ​​of a record, and each record corresponds to a beam of a multi-beam system. The curves 502 include a current curve 506. Figure 5 Also shown is a reference 504 that does not represent a time-dependent value. The controller may compare each of the curves 502 (including the current curve 506) to the reference 504 and determine that an anomaly has occurred in the curve if the difference between the curve and the reference 504 satisfies a predetermined condition. In some embodiments, the predetermined condition may be that the difference is not within a predetermined range. For example, Figure 5 , the predetermined range is shown as a range 508 including the reference 504. In some embodiments, the predetermined range can be symmetrical about the reference value. In some embodiments, the predetermined range can be symmetrical about the reference value. In some embodiments, the predetermined range can be asymmetrical about the reference value. Although the range 508 is shown as symmetrical about the reference 504, it can also be asymmetrical.

[0064] In some embodiments, the controller may determine the baseline 504 as a historically based baseline. For example, the controller may determine the baseline 504 using an initial record received before time point T0. The value of the baseline 504 may be constant from time point T0, and the controller may compare each curve of the curve 502 (e.g., the current curve 506) to the baseline 504. If the difference between the baseline 504 and the curve is outside the range 508, the controller may determine that an anomaly has occurred. For example, at Figure 5In the example, the reference 504 may be a historically based reference and have a constant value over time. At a time point between time points T1 and T2, the difference between the current curve 506 and the reference 504 exceeds a range 508, wherein the controller may determine that an anomaly has occurred at the beam corresponding to the current curve 506.

[0065] In some embodiments, the controller may determine the benchmark 504 as a benchmark based on group performance. For example, the controller may determine the benchmark 504 as a statistic of a portion of the curve 502. A "statistic" may be a numerical value calculated from and characterizing a data sample. For example, a statistic may be a mean, a median, a standard deviation, a standard error, or any other numerical value suitable for use as a benchmark 504. For example, the portion of the curve 502 may be all of the curves 502 (e.g., including the current curve 506). For another example, the portion of the curve 502 may be all of the curves 502 except the current curve 506. It should be noted that any subset of the curves 502 may be the portion of the curve 502 used to determine the statistic. In some embodiments, the controller may determine the statistic at a point in time. Where the controller determines the statistic at a point in time, Figure 5 The values ​​of the base 504 and range 508 in the table may move up and down over time. Figure 5 When a group-based performance baseline is used in , it represents the group-based performance baseline determined at a specific point in time.

[0066] In some embodiments, the controller may determine the benchmark 504 as a statistic of the portion of the curve 502 at a previous or current time point. For example, at time point T3, the controller may determine the statistic using real-time values ​​of the portion of the curve 502 received at time point T3, T2, T1, T0, or at any time point before or concurrent with time point T3. In some embodiments, the controller may determine the benchmark 504 as a statistic of the portion of the curve 502 within a time range. For example, at time point T3, the controller may determine the statistic using all values ​​of the portion of the curve 502 received within a time range between time points T0 and T1, T1 and T2, T2 and T3, T0 and T2, T1 and T3, or within any time range before or including time point T3.

[0067] In some embodiments, the controller may dynamically (e.g., periodically or in real time) update the benchmark 504 to the statistics of the portion of the curve 502. As described above, the controller may update the benchmark 504 using the value of the portion of the curve 502 at a previous or current time point or within a time range. In some embodiments, the controller may periodically or aperiodically update the benchmark 504 to the statistics of the portion of the curve 502 at different time points. For example, at Figure 5 In the embodiment of the present invention, as time passes, the controller may update the reference 504 at any one of the time points T0, T1, T2, T3, T4, and T5. In some embodiments, the controller may update the reference 504 as a statistic of the portion of the curve 502 over a sliding time window. For example, at time point T3, the controller may determine the reference 504 as a statistic of the portion of the curve 502 using the value of the portion of the curve 502 over a time window between time points T1 and T2, T2 and T3, T0 and T1, T0 and T2, T1 and T3, or before time point T3, or including any time range up to time point T3. At time point T4, the controller may determine the benchmark 504 as a statistic of the portion of the curve 502 using the values ​​of the portion of the curve 502 over a time window between time points T2 and T3, T3 and T4, T1 and T2, T1 and T3, T2 and T4, or before time point T4, or including any time range up to time point T4, the time window being offset by the time difference (T4-T3) and having the same length as the time window used at time point T3. Other methods for determining and updating the benchmark 504 may be used and are not limited to the foregoing examples.

[0068] It should be noted that when comparing the current curve 506 and the benchmark 504, the controller may use the real-time or instantaneous value of the current curve 506 or the statistical data of the current curve 506. In some embodiments, 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 point when the comparison is to be performed. For example, at time point T3, the real-time value of the current curve 506 may be received at time point T3. When the controller uses the real-time value of the current curve 506 for comparison, the controller may determine that an abnormality has occurred when the difference between the real-time value of the current curve 506 and the benchmark 504 meets a predetermined condition. For example, at Figure 5 , at a time point between time points T1 and T2, current curve 506 has a real-time value shown as the first spike in the timeline that exceeds the upper limit of range 508. At that time point, the controller can determine that an anomaly occurs at the beam corresponding to current curve 506.

[0069] In some embodiments, the controller may use the current statistics (e.g., average, median, etc.) of the current curve 506 for comparison. The current statistics of the current curve 506 may be determined within a time period using all values ​​of the current curve 506 received within the time period. The current statistics of the current curve 506 may be determined in a manner similar to the statistics of the curve 502. For example, at time point T4, the current statistics of the current curve 506 may be the average of all values ​​of the current curve 506 received within the time period between time points T3 and T4. For another example, at time point T5, the current statistics of the current curve 506 may be the median of all values ​​of the current curve 506 received within the time period between time points T2 and T5. When the controller uses the current statistics of the current curve 506 for comparison, the controller may determine that an abnormality has occurred when the difference between the current statistics of the current curve 506 and the benchmark 504 meets a predetermined condition. For example, at Figure 5 508. In FIG. 50 , at time point T2, the current statistics may be the average of all values ​​of the current curve 506 received in the time period between time points T1 and T2. At the time points between time points T1 and T2, the current curve 506 has a real-time value shown as the first spike in the timeline that exceeds the upper limit of range 508. However, the current statistics between time points T1 and T2 may fall within range 508, and the controller may not be able to determine that an anomaly has occurred at time point T2. However, at time point T3, the current statistics may be the average of all values ​​of the current curve 506 received in the time period between time points T2 and T3, and the current statistics between time points T2 and T3 may exceed range 508. Therefore, the controller may determine that an anomaly has occurred in the beam corresponding to the current curve 506 at time point T3.

[0070] In some embodiments, in order to determine whether an abnormality of the beam occurs, an integrated performance method may be used. In the integrated performance method, the controller may determine a first regression parameter based on a regression performed on the values ​​of the records received at multiple time points, and determine a second regression parameter based on a regression performed on the values ​​of each record of a portion of the records received at the multiple time points. Each of the second regression parameters may correspond to a record in the portion of the records.

[0071] For example, Figure 5The curve 502 and the current curve 506 in which it is to be determined whether an anomaly exists are shown. The current curve 506 corresponds to a record of the beam from which the controller is detecting whether an anomaly occurs. For example, starting from time point T0, the controller can control the recording and storage of values ​​of performance metrics corresponding to beams of the multi-beam system, each beam corresponding to a curve in the curve 502. These values ​​can be recorded continuously or intermittently. The first regression parameter can be determined based on the regression performed on the current curve 506. The second regression parameter can be determined based on the regression performed on a subset of the curves 502. In some embodiments, the subset may include the current curve 506. In some embodiments, the subset may not include the current curve 506. For example, the subset may include all curves 502 except the current curve 506. For another example, the subset may include some curves 502 except the current curve 506. Each of the second regression parameters may correspond to one of the subset of curves 502. In some embodiments, the regression may include a linear regression, and the first regression parameter and the second regression parameter may include one of a slope or an intercept. For example, by performing a linear regression on the current curve 506, the controller can determine at least one of a first slope or a first intercept for the current curve 506. By performing a linear regression on each subset of the curves 502, the controller can determine at least one of a second slope or a second intercept for each subset.

[0072] In the integrated performance method, in some embodiments, the controller can also use the second regression parameter to determine the parameter value. For example, the parameter value can be determined as the mean value, median value, variance value, standard deviation value, or root mean square value of the second regression parameter. For example, if the second regression parameter includes the second slope of the curve 502, the parameter value can be determined as the statistics of the second slope (e.g., the mean value or median value). For another example, if the second regression parameter includes the second intercept of the curve 502, the parameter value can be determined as the statistics of the second intercept (e.g., the mean value or median value).

[0073] In the integrated performance method, the controller can also determine whether the difference between the first regression parameter and the parameter value is within a predetermined range. For example, the controller can determine whether the difference between the statistics of the first slope (or first intercept) and the second slope (or second intercept) is within a predetermined range. In some embodiments, the predetermined range can be symmetrical about the statistics of the parameter value. In some embodiments, the predetermined range can be asymmetrical about the statistics of the parameter value.

[0074] In the integrated performance approach, if the difference is not within a predetermined range, the controller can then determine that an abnormality has occurred. Figure 5If the difference between the first slope of the current curve 506 and the average value of the second slope of the curve 502 is not within a predetermined range, the controller may determine that an abnormality has occurred in the current curve 506 .

[0075] Still returns reference Figure 3 , at step 306, the controller provides an exception indication in response to determining that an exception has occurred. In some embodiments, the exception indication may include a visual indication, such as a warning (e.g., a pop-up window) displayed in a graphical user interface (GUI). In some embodiments, the exception indication may include an acoustic indication, such as a sound. In some embodiments, the exception indication may include a message sent to a user of the multi-beam system, such as a text message. Other forms of exception indications may also be used. When the user receives the exception indication, the user may begin to check whether any problems actually occur in a module (e.g., a hardware module or a software module) of the multi-beam system. For example, the user may begin to check a log file associated with the multi-beam system.

[0076] In some embodiments, after step 306, the controller may determine the source of the anomaly in response to determining that the anomaly has occurred. For example, the controller may trigger the running of diagnostic software to identify the source of the anomaly and determine whether the anomaly indicates an actual problem with the multi-beam system.

[0077] Figure 4 is a flow chart illustrating an exemplary method 400 for monitoring performance of a multi-beam system consistent with embodiments of the present disclosure.

[0078] At step 402, a controller (eg, Figure 1-Figure 2 The controller 109 in the embodiment of the present invention determines a baseline value of a performance metric for the beams of the multi-beam system during imaging. In some embodiments, each record can be associated with one of the beams. For example, the controller can determine the baseline value based on an initial record of the performance metric received after a predetermined event. For another example, the controller can determine the baseline value as a statistic of the values ​​of the portion of the record of the performance metric.

[0079] At step 404, the controller stores at least one of the reference value or the record in a database. In some embodiments, the database may be included in a memory accessible to the controller.

[0080] At step 406, the controller receives a record of performance metrics for the beam during imaging. In some embodiments, 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.

[0081] At step 408 , the controller retrieves at least one of a record or a reference value from a database.

[0082] At step 410 , the controller determines whether an anomaly of the beam has occurred based on the parameter value associated with a portion of the record. In some embodiments, the controller may perform step 410 in a manner similar to step 304 .

[0083] At step 412 , the controller provides an anomaly indication in response to determining that an anomaly has occurred. In some embodiments, the controller may perform step 412 in a manner similar to step 306 .

[0084] In some embodiments, after step 412, the controller may determine the source of the anomaly in response to determining that the anomaly has occurred. For example, the controller may trigger the running of diagnostic software to identify the source of the anomaly and determine whether the anomaly indicates an actual problem with the multi-beam system.

[0085] A non-transitory computer-readable medium may be provided that stores for use by a processor (e.g., Figure 1 The processor of the controller 109 of the embodiment of the present invention) can execute instructions to perform image processing, data processing, self-referencing schemes, database management, graphic display, operation of charged particle beam devices or other imaging equipment, etc. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tapes or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a pattern of holes, RAM, PROM and EPROM, FLASH-EPROM or any other flash memory, NVRAM, cache, registers, any other memory chip or cartridge memory, and network versions thereof.

[0086] The embodiments may be further described using the following terms:

[0087] 1. A method for monitoring the performance of a multi-beam system, the method comprising:

[0088] receiving records of performance metrics of beams of a multi-beam system operated during an imaging procedure, each record being associated with a beam;

[0089] determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; and

[0090] Responsive to a determination that an anomaly has occurred, an anomaly indication is provided.

[0091] 2. The method of clause 1, wherein determining whether an abnormality of the beam occurs comprises: determining a test value of the beam using at least one value of a record of the beam received at a plurality of time points during the imaging process;

[0092] determining whether a difference between a test value and a reference value of the beam is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0093] 3. A method according to any of the preceding clauses, further comprising:

[0094] At least one of the reference value or the record is stored in a database.

[0095] 4. The method according to clause 3, further comprising:

[0096] Prior to determining whether the difference is within a predetermined range, at least one of a record or a reference value is retrieved from a database.

[0097] 5. A method according to any of the preceding clauses, further comprising:

[0098] The baseline value is determined after a predetermined event.

[0099] 6. The method of clause 5, wherein determining the baseline value after the predetermined event comprises:

[0100] The baseline value is determined based on an initial record of the performance metric received after a predetermined event.

[0101] 7. The method of any of clauses 5-6, wherein the predetermined event comprises one of completing maintenance of the multi-beam system or calibration of the multi-beam system.

[0102] 8. A method according to any of clauses 5-7, wherein the predetermined event comprises: each value of the record changes by an offset value, wherein the offset value does not deviate from an average value of values ​​in the record that exceed a predetermined range.

[0103] 9. The method according to any one of clauses 1 to 4, further comprising:

[0104] A base value is determined as a statistic of values ​​that are part of the record.

[0105] 10. A method according to any of clauses 2-9, wherein the test value of the beam comprises a statistic of at least one recorded value of the beam.

[0106] 11. A method according to any of clauses 9-10, wherein the statistic comprises one of a mean, a median, a variance, a standard deviation or a root mean square value.

[0107] 12. A method according to any of clauses 2-9, wherein the test value of the beam comprises a recorded value of the beam received at one of the plurality of time points.

[0108] 13. The method according to any of the preceding clauses, wherein determining whether an anomaly of the beam occurs comprises:

[0109] determining first regression parameters based on a regression performed on values ​​of records received at a plurality of points in time, determining second regression parameters based on a regression performed on values ​​of each record of a portion of records received at the plurality of points in time, each of the second regression parameters corresponding to a record in the portion of records;

[0110] The second regression parameter was used to determine the baseline value;

[0111] determining whether a difference between the first regression parameter and the reference value is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0112] 14. The method of clause 13, wherein each of the first regression parameter and the second regression parameter comprises one of a slope or an intercept.

[0113] 15. A method according to any of clauses 13-14, wherein the portion of the record does not include the record.

[0114] 16. A method according to any of clauses 13-15, wherein the portion of the record includes all records other than the record.

[0115] 17. A method according to any of the preceding clauses, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam in the beam during the imaging process.

[0116] 18. The method of clause 17, wherein the performance metric is indicative of geometric distortion of the image and comprises at least one of aberrations, structural distortions, or changes in geometry.

[0117] 19. A method according to any of clauses 17-18, wherein the performance metric is indicative of image quality and comprises a variable representing at least one of a noise level of the image, brightness of the image, contrast of the image, sharpness of the image or resolution of the image.

[0118] 20. The method according to clause 19, further comprising:

[0119] Data representing the noise level is determined using a portion of the image corresponding to an area of ​​the FOV where the pattern is absent.

[0120] 21. The method according to clause 20, further comprising:

[0121] The area is determined based on at least one of layout data of the area of ​​the FOV or image data of the image.

[0122] 22. The method according to any one of clauses 19-21, further comprising:

[0123] At least one of brightness or contrast is determined based on a grayscale level of the image.

[0124] 23. The method according to any one of clauses 19-22, further comprising:

[0125] Extracting step distribution from pattern edges of the image; and

[0126] At least one of sharpness or resolution is determined based on the step profile.

[0127] 24. A method according to any of the preceding clauses, further comprising:

[0128] Responsive to a determination that an exception has occurred, a source of the exception is determined.

[0129] 25. A system for monitoring the performance of a multi-beam system, the system comprising:

[0130] A controller having circuitry for executing a set of instructions that cause the system to:

[0131] receiving records of performance metrics for beams of a multi-beam system during an imaging process, each record being associated with a beam;

[0132] determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; and

[0133] Responsive to a determination that the anomaly has occurred, an anomaly indication is provided.

[0134] 26. The system of clause 25, wherein the set of instructions that causes the system to determine whether an anomaly of the beam has occurred further causes the system to:

[0135] determining a test value of the beam using at least one value of the record of the beam received at a plurality of time points during the imaging process;

[0136] determining whether a difference between a test value and a reference value of the beam is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0137] 27. A system according to any of clauses 25-26, further comprising:

[0138] The database is configured to store at least one of the reference value or the record.

[0139] 28. The system of clause 27, wherein the set of instructions further causes the system to:

[0140] Prior to determining whether the difference is within a predetermined range, at least one of a record or a reference value is retrieved from a database.

[0141] 29. The system of any of clauses 25-28, wherein the set of instructions further causes the system to: determine a baseline value after a predetermined event.

[0142] 30. The system of clause 29, wherein the set of instructions that causes the system to determine the baseline value after a predetermined event further causes the system to:

[0143] The baseline value is determined based on an initial record of the performance metric received after a predetermined event.

[0144] 31. The system of any of clauses 29-30, wherein the predetermined event comprises: completing one of maintenance of the multi-beam system or calibration of the multi-beam system.

[0145] 32. The system of any of clauses 29-31, wherein the predetermined event comprises: each value of the record changes by an offset value, wherein the offset value does not deviate from an average value of values ​​in the record that exceed a predetermined range.

[0146] 33. The system of any of clauses 25-28, wherein the set of instructions further causes the system to: determine the baseline value as a statistic of values ​​of the recorded portion.

[0147] 34. The system of any of clauses 26-33, wherein the test value of the beam comprises a statistics of the at least one recorded value of the beam.

[0148] 35. A system according to any of clauses 33-34, wherein the statistic comprises one of a mean, a median, a variance, a standard deviation or a root mean square value.

[0149] 36. A system according to any of clauses 26-33, wherein the test value of the beam comprises a recorded value of the beam received at one of the plurality of time points.

[0150] 37. A system according to any of clauses 25-36, wherein the set of instructions that causes the system to determine whether an anomaly of the beam has occurred further causes the system to:

[0151] determining first regression parameters based on a regression performed on values ​​of records received at a plurality of points in time, determining second regression parameters based on a regression performed on values ​​of each record of a portion of records received at the plurality of points in time, each of the second regression parameters corresponding to a record in the portion of records;

[0152] The second regression parameter was used to determine the baseline value;

[0153] determining whether a difference between the first regression parameter and the reference value is within a predetermined range; and determining that an abnormality occurs based on a determination that the difference is not within the predetermined range.

[0154] 38. The system of clause 37, wherein each of the first regression parameter and the second regression parameter comprises one of a slope or an intercept.

[0155] 39. A system according to any of clauses 25-38, wherein the performance metric is indicative of at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam in the beam during the imaging process.

[0156] 40. The system of clause 39, wherein the performance metric is indicative of geometric distortion of the image and comprises at least one of aberrations, structural distortions, or changes in geometry.

[0157] 41. A system according to any of clauses 39-40, wherein the performance metric indicates image quality and includes a variable representing at least one of a noise level of the image, brightness of the image, contrast of the image, sharpness of the image, or resolution of the image.

[0158] 42. The system of clause 41, wherein the set of instructions further causes the system to:

[0159] Data representing the noise level is determined using a portion of the image corresponding to an area of ​​the FOV where the pattern is absent.

[0160] 43. The system of clause 42, wherein the set of instructions further causes the system to:

[0161] The area is determined based on at least one of layout data of the area of ​​the FOV or image data of the image.

[0162] 44. The system of any of clauses 41-43, wherein the set of instructions further causes the system to: determine at least one of brightness or contrast based on a grayscale level of the image.

[0163] 45. The system of any of clauses 41-44, wherein the set of instructions further causes the system to: extract a step distribution from a pattern edge of the image; and

[0164] At least one of sharpness or resolution is determined based on the step distribution.

[0165] 46. ​​The system of any of clauses 25-45, wherein the set of instructions further causes the system to: in response to determining that an exception has occurred, determine a source of the exception.

[0166] 47. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a device to cause the device to perform a method for monitoring performance of a multi-beam system, the method comprising:

[0167] receiving records of performance metrics for beams of a multi-beam system during an imaging process, each record being associated with a beam;

[0168] determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; and

[0169] Responsive to a determination that an anomaly has occurred, an anomaly indication is provided.

[0170] 48. A method of monitoring the health of a multi-beam device, the method comprising:

[0171] receiving a record of performance metrics for beams of the multi-beam device during an imaging process;

[0172] comparing the records to determine if the beams in the beam exhibit anomalies; and

[0173] Trigger a notification when a beam behaves abnormally.

[0174] 49. The method of clause 48, wherein the beam comprises three or more beams.

[0175] 50. The method according to any of clauses 48-49, further comprising:

[0176] Responsive to a determination that an exception has occurred, a source of the exception is determined.

[0177] 51. A method according to any of clauses 48-50, wherein each record is associated with one of the beams and each record comprises a plurality of values ​​of the performance metric received at a plurality of time points during the imaging process.

[0178] 52. A method according to any of clauses 48-51, wherein comparing the record to determine whether a beam in the beam exhibits an anomaly comprises:

[0179] determining a baseline value for a performance metric for the beam;

[0180] determining a test value of the beam using at least one value of the record of the beam received at a plurality of time points during the imaging process;

[0181] determining whether a difference between a test value and a reference value of the beam is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0182] 53. The method according to clause 52, further comprising:

[0183] At least one of the reference value or the record is stored in a database.

[0184] 54. The method according to clause 53, further comprising:

[0185] Prior to determining whether the difference is within a predetermined range, at least one of a record or a reference value is retrieved from a database.

[0186] 55. The method according to any of clauses 52-54, further comprising:

[0187] The reference value is determined as a statistic of the values ​​of the recorded portion.

[0188] 56. A method according to any of clauses 52-55, wherein the test value of the beam comprises a statistic of at least one recorded value of the beam.

[0189] 57. A method according to any of clauses 55-56, wherein the statistic comprises one of a mean, a median, a variance, a standard deviation or a root mean square value.

[0190] 58. A method according to any of clauses 42-56, wherein the test value of the beam comprises a recorded value of the beam received at one of the plurality of time points.

[0191] 59. A method according to any of clauses 48-58, wherein comparing the record to determine whether a beam in the beam exhibits an anomaly comprises:

[0192] determining first regression parameters based on a regression performed on values ​​of records received at a plurality of points in time, determining second regression parameters based on a regression performed on values ​​of each record of a portion of records received at the plurality of points in time, each of the second regression parameters corresponding to a record in the portion of records;

[0193] The second regression parameter was used to determine the baseline value;

[0194] determining whether a difference between the first regression parameter and the reference value is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0195] 60. The method of clause 59, wherein each of the first regression parameter and the second regression parameter comprises one of a slope or an intercept.

[0196] 61. A method according to any of clauses 59-60, wherein the portion of the record does not include the record.

[0197] 62. A method according to any of clauses 59-61, wherein the portion of the record includes all records other than the record.

[0198] 63. A method according to any of clauses 48-62, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam in the beam during the imaging process.

[0199] 64. A method according to clause 63, wherein the performance metric is indicative of geometric distortion of the image and includes at least one of aberrations, structural distortions, or changes in geometry.

[0200] 65. A method according to any of clauses 63-64, wherein the performance metric indicates image quality and includes a variable representing at least one of the noise level of the image, the brightness of the image, the contrast of the image, the sharpness of the image, or the resolution of the image.

[0201] 66. The method according to clause 65, further comprising:

[0202] Data representing the noise level is determined using a portion of the image corresponding to an area of ​​the FOV where the pattern is absent.

[0203] 67. The method according to clause 66, further comprising:

[0204] The area is determined based on at least one of layout data of the area of ​​the FOV or image data of the image.

[0205] 68. A method according to any of clauses 65-67, further comprising:

[0206] At least one of brightness or contrast is determined based on a grayscale level of the image.

[0207] 69. A method according to any of clauses 65-68, further comprising:

[0208] Extracting step distribution from pattern edges of the image; and

[0209] At least one of sharpness or resolution is determined based on the step profile.

[0210] 70. An apparatus for monitoring the health of a multi-beam system, the apparatus comprising:

[0211] A controller having circuit means for executing a set of instructions that cause the device to:

[0212] receiving a record of performance metrics for beams of the multi-beam system during an imaging process;

[0213] comparing the records to determine if the beams in the beam exhibit anomalies; and

[0214] Trigger a notification when a beam behaves abnormally.

[0215] 71. An apparatus according to clause 70, wherein the beam comprises three or more beams.

[0216] 72. An apparatus according to any of clauses 70-71, wherein the set of instructions further causes the apparatus to: in response to determining that an exception has occurred, determine a source of the exception.

[0217] 73. An apparatus according to any of clauses 70-72, wherein each record is associated with one of the beams and each record comprises multiple values ​​of the performance metric received at multiple time points during the imaging process.

[0218] 74. An apparatus according to any of clauses 70-73, wherein the set of instructions that causes the apparatus to compare records to determine whether a beam in the beam exhibits an anomaly further causes the system to:

[0219] determining a baseline value for a performance metric for the beam;

[0220] determining a test value of the beam using at least one value of the record of the beam received at a plurality of time points during the imaging process;

[0221] determining whether a difference between a test value and a reference value of the beam is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0222] 75. An apparatus according to clause 74, wherein the set of instructions further causes the apparatus to:

[0223] At least one of the reference value or the record is stored in a database.

[0224] 76. An apparatus according to clause 75, wherein the set of instructions further causes the apparatus to:

[0225] Prior to determining whether the difference is within a predetermined range, at least one of a record or a reference value is retrieved from a database.

[0226] 77. An apparatus according to any of clauses 74-76, wherein the set of instructions further causes the apparatus to: determine the baseline value as a statistic of values ​​of the recorded portion.

[0227] 78. Apparatus according to any of clauses 74-77, wherein the test value of the beam comprises a statistic of at least one recorded value of the beam.

[0228] 79. An apparatus according to any of clauses 77-78, wherein the statistic comprises one of a mean, a median, a variance, a standard deviation or a root mean square value.

[0229] 80. Apparatus according to any of clauses 74 to 78, wherein the test value of the beam comprises a recorded value of the beam received at one of the plurality of time points.

[0230] 81. An apparatus according to any of clauses 70-80, wherein the set of instructions that causes the apparatus to compare records to determine whether a beam in the beam exhibits an anomaly further causes the system to:

[0231] determining first regression parameters based on a regression performed on values ​​of records received at a plurality of points in time, determining second regression parameters based on a regression performed on values ​​of each record of a portion of records received at the plurality of points in time, each of the second regression parameters corresponding to a record in the portion of records;

[0232] The second regression parameter was used to determine the baseline value;

[0233] determining whether a difference between the first regression parameter and the reference value is within a predetermined range; and determining that an abnormality has occurred based on a determination that the difference is not within the predetermined range.

[0234] 82. An apparatus according to clause 81, wherein each of the first regression parameter and the second regression parameter comprises one of a slope or an intercept.

[0235] 83. An apparatus as described in any of clauses 81-82, wherein the portion of the record does not include the record.

[0236] 84. An apparatus according to any of clauses 81-83, wherein the portion of the record includes all records other than the record.

[0237] 85. An apparatus according to any of clauses 70-84, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam in the beam during the imaging process.

[0238] 86. An apparatus according to clause 85, wherein the performance metric is indicative of geometric distortion of the image and includes at least one of aberrations, structural distortions, or changes in geometry.

[0239] 87. An apparatus according to any of clauses 85-86, wherein the performance metric is indicative of image quality and includes a variable representing at least one of a noise level of the image, brightness of the image, contrast of the image, sharpness of the image, or resolution of the image.

[0240] 88. An apparatus according to clause 87, wherein the set of instructions further causes the apparatus to:

[0241] Data representing the noise level is determined using a portion of the image corresponding to an area of ​​the FOV where the pattern is absent.

[0242] 89. An apparatus according to clause 88, wherein the set of instructions further causes the apparatus to:

[0243] The area is determined based on at least one of layout data of the area of ​​the FOV or image data of the image.

[0244] 90. An apparatus according to any of clauses 87-89, wherein the set of instructions further causes the apparatus to: determine at least one of brightness or contrast based on a grayscale level of the image.

[0245] 91. An apparatus according to any of clauses 87-90, wherein the set of instructions further causes the apparatus to: extract a step distribution from a pattern edge of the image; and

[0246] At least one of sharpness or resolution is determined based on the step profile.

[0247] 92. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a device to cause the device to perform a method for monitoring the health of a multi-beam system, the method comprising:

[0248] receiving a record of performance metrics for beams of the multi-beam system during an imaging process;

[0249] comparing the records to determine if the beams in the beam exhibit anomalies; and

[0250] Trigger a notification when a beam behaves abnormally.

[0251] 93. A method of monitoring the health of a multi-beam scanning electron microscope (SEM), the method comprising:

[0252] receiving a record of performance metrics for a plurality of beams of a multi-beam system, the plurality of beams including a first beam and other beams;

[0253] comparing data derived from the records to determine whether the first beam exhibits abnormal performance compared to the other beams; and

[0254] A notification is triggered when it is determined based on the comparison that the first beam exhibits abnormal performance.

[0255] 94. The method of clause 93, wherein comparing the data further comprises:

[0256] determining statistics of performance metrics for other beams; and

[0257] It is determined that a difference between the performance metric for the first beam and the statistical data is greater than or equal to a predetermined threshold.

[0258] 95. The method of clause 93, wherein the statistic comprises one of a mean, a median, a variance, a standard deviation, or a root mean square value of the performance metric for the other beams.

[0259] The block diagram in the figure illustrates the possible implementation architecture, functionality and operation of the system, method and computer hardware or software product according to various exemplary embodiments of the present disclosure. In this regard, each block in the flow chart or block diagram can represent a part of a module, segment or code, which includes one or more executable instructions for implementing a specified logical function. It should be understood that in some alternative implementations, the functions indicated in the block may not occur in the order marked in the figure. For example, two blocks shown in succession can be performed or implemented substantially at the same time, or two blocks can sometimes be performed in reverse order, depending on the functionality involved. Some blocks can also be omitted. It should also be understood that each block of the block diagram and the combination of blocks can be implemented by a dedicated hardware-based system that performs a specified function or action, or by a combination of dedicated hardware and computer instructions.

[0260] It should be understood that the embodiments of the present disclosure are not limited to the exact structures that have been described above and illustrated in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for monitoring the performance of a multi-beam system, the method comprising: receiving records of performance metrics of beams of the multi-beam system operated during an imaging procedure, each record being associated with a beam; determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; as well as providing an exception indication in response to a determination that the exception has occurred, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam during imaging, wherein the image quality comprises a variable representing at least one of a noise level of the image, a sharpness of the image, or a resolution of the image, and The performance metric has a time-dependent value.

2. A system for monitoring the performance of a multi-beam system, the system comprising: A controller having circuitry for executing a set of instructions that cause the system to: receiving records of performance metrics for beams of the multi-beam system during imaging, each record being associated with a beam; determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; as well as providing an exception indication in response to a determination that the exception has occurred, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam during imaging, wherein the image quality comprises a variable representing at least one of a noise level of the image, a sharpness of the image, or a resolution of the image, and The performance metric has a time-dependent value.

3. The system of claim 2, wherein the set of instructions for causing the system to determine whether an anomaly of the beam has occurred further causes the system to: determining a test value of the beam using at least one value of the record of the beam received at a plurality of time points during the imaging process; determining whether a difference between the test value and the reference value of the beam is within a predetermined range; as well as Based on the determination that the difference is not within the predetermined range, it is determined that the abnormality has occurred.

4. The system according to claim 3, further comprising: A database is configured to store at least one of the reference value or the record.

5. The system of claim 4, wherein the set of instructions further causes the system to: Before determining whether the difference is within the predetermined range, at least one of the record or the reference value is retrieved from the database.

6. The system of claim 2, wherein the set of instructions further causes the system to: The reference value is determined after a predetermined event.

7. The system of claim 6, wherein the set of instructions for causing the system to determine the baseline value after the predetermined event further causes the system to: The baseline value is determined based on an initial record of the performance metric received after the predetermined event.

8. The system of claim 6, wherein the predetermined event comprises: One of maintenance of the multi-beam system or calibration of the multi-beam system is performed.

9. The system of claim 6, wherein the predetermined event comprises: Each value of the record is changed by an offset value, wherein the offset value does not deviate from an average value of values ​​in the record that exceed a predetermined range.

10. The system of claim 2, wherein the set of instructions further causes the system to: The reference value is determined as a statistic of values ​​of the portion of the records.

11. The system of claim 3, wherein the test value of the beam comprises a statistic of the at least one value of the record of the beam.

12. The system of claim 10, wherein the statistic comprises one of a mean value, a median value, a variance value, a standard deviation value, or a root mean square value.

13. The system of claim 3, wherein the test values ​​of the beam include: The recorded value of the beam received at one of the plurality of time points.

14. The system of claim 2, wherein the set of instructions for causing the system to determine whether an anomaly of the beam has occurred further causes the system to: determining first regression parameters based on a regression performed on values ​​of the record received at a plurality of points in time, and determining second regression parameters based on a regression performed on values ​​of each record in the portion of the record received at a plurality of points in time, each of the second regression parameters corresponding to a record in the portion of the record; determining the reference value using the second regression parameter; determining whether a difference between the first regression parameter and the reference value is within a predetermined range; as well as Based on the determination that the difference is not within the predetermined range, it is determined that the abnormality occurs.

15. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a device to cause the device to perform a method for monitoring performance of a multi-beam system, the method comprising: receiving records of performance metrics for beams of the multi-beam system during imaging, each record being associated with a beam; determining whether an abnormality of the beam occurs based on a reference value determined using a portion of the record; as well as providing an exception indication in response to a determination that the exception has occurred, wherein the performance metric indicates at least one of image quality or geometric distortion of an image generated from a field of view (FOV) of the beam during imaging, wherein the image quality comprises a variable representing at least one of a noise level of the image, a sharpness of the image, or a resolution of the image, and The performance metric has a time-dependent value.

Images