System and method for distortion adjustment during inspection

Abstract

A system, apparatus, and method for adjusting distortion in an image. Various embodiments include obtaining a plurality of images; determining alignment differences between a plurality of features on the plurality of images and corresponding features in the layout data corresponding to the plurality of images; modeling the alignment difference; and using the modeling to adjust at least one of: machine settings corresponding to obtaining the plurality of images; or at least one of the plurality of features on at least one of the plurality of images.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Application No. 63 / 347,984 filed on June 1, 2022 and U.S. Application No. 63 / 456,628 filed on April 3, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The description herein relates to the field of inspection systems, and more particularly to systems for adjusting for distortion in images during inspection. Background Art

[0004] During the manufacturing process of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to the design and are free of defects. Inspection systems that utilize optical microscopes typically have resolutions down to a few hundred nanometers; and the resolution is limited by the wavelength of light. As the physical size of IC components continues to decrease to below 100 nanometers or even below 10 nanometers, there is a need for inspection systems that can have higher resolution than systems that utilize optical microscopes.

[0005] Charged particle (e.g., electron) beam microscopes (such as scanning electron microscopes (SEMs) or transmission electron microscopes (TEMs)) capable of resolutions down to less than one nanometer are used as practical tools for inspecting IC components with feature sizes below 100 nanometers. Using an SEM, electrons from a single primary electron beam or electrons from multiple primary electron beams can be focused on a location of interest on the wafer being inspected. The primary electrons interact with the wafer, which may produce backscattering and may also cause the wafer to emit secondary electrons. The intensity of the electron beam, including backscattered electrons and secondary electrons, can vary based on the characteristics of the internal and external structures of the wafer, which can indicate whether the wafer has defects. Summary of the invention

[0006] Embodiments of the present disclosure provide apparatus, systems, and methods for adjusting distortion in images. In some embodiments, the systems, methods, and non-transitory computer-readable media may include: obtaining multiple images; determining alignment differences between multiple features on the multiple images and corresponding features in layout data corresponding to the multiple images; modeling the alignment differences; and using the modeling to adjust at least one of the following: a machine setting corresponding to obtaining the multiple images, or at least one feature of the multiple features on at least one of the multiple images.

[0007] In some embodiments, systems, methods, and non-transitory computer-readable media may include: obtaining a first plurality of images at a first machine setting; determining a first alignment difference between a plurality of features on the first plurality of images and corresponding features in layout data corresponding to the first plurality of images; modeling the first alignment difference using a first modeling; determining at least one measurement error associated with the first alignment difference; determining a second machine setting based on the at least one measurement error; obtaining a second plurality of images at a second machine setting; determining a second alignment difference between a plurality of features on the second plurality of images and corresponding features in layout data corresponding to the second plurality of images; modeling the second alignment difference using a second modeling; and using the second modeling to adjust at least one of the following: the second machine setting, or at least one feature of the plurality of features on at least one image in the second plurality of images.

[0008] In some embodiments, systems, methods, and non-transitory computer-readable media may include: obtaining multiple images; determining multiple position coordinates, wherein each of the multiple position coordinates corresponds to one of multiple features on the multiple images; determining multiple differences, wherein each of the multiple differences is between each of the multiple position coordinates and one of a plurality of predetermined position coordinates corresponding to the multiple features; modeling the multiple differences; and using the modeling to adjust at least one of the following: a machine setting corresponding to obtaining the multiple images, or at least one position coordinate corresponding to one of the multiple features. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system according to an embodiment of the present disclosure.

[0010] Figure 2 is a diagram illustrating an embodiment of the present disclosure, as Figure 1 Schematic diagram of an exemplary multi-beam system that is a portion of an exemplary charged particle beam inspection system.

[0011] Figure 3 is a schematic diagram illustrating an exemplary configuration of control circuitry associated with a segmented charged-particle beam deflector according to an embodiment of the present disclosure.

[0012] Figure 4 is a schematic diagram of an exemplary system for adjusting distortion in an image according to an embodiment of the present disclosure.

[0013] Figure 5 is a schematic diagram illustrating an exemplary alignment of an image of a region of a sample and corresponding layout data according to an embodiment of the present disclosure.

[0014] Figure 6 is a flow chart illustrating an exemplary process of adjusting distortion in an image according to an embodiment of the present disclosure.

[0015] Figure 7 is a schematic diagram illustrating an exemplary image of a region of a sample with corresponding layout data according to an embodiment of the present disclosure.

[0016] Figure 8 is a flow chart illustrating an exemplary process of adjusting distortion in an image according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Reference is now 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 indicated, the same numbers in different drawings represent the same or similar elements. The embodiments set forth in the following description of the exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with various aspects related to the subject matter as described 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. Similarly, other types of charged particle beams may be applied. Further, other imaging systems may be used, such as optical imaging, photoelectric detection, x-ray detection, extreme ultraviolet detection, deep ultraviolet detection, etc., wherein they generate images of corresponding types.

[0018] Electronic devices are made up of circuits formed on a silicon wafer called a substrate. Many circuits can be formed together on the same silicon wafer and are called an integrated circuit or IC. The size of these circuits has been reduced significantly so that many of them can fit on a substrate. For example, an IC chip in a smartphone can be as small as a thumbnail but can include over 2 billion transistors, each less than 1 / 1000 the size of a human hair.

[0019] Manufacturing these extremely small ICs is a complex, time-consuming, and expensive process that typically involves hundreds of individual steps. An error in even one step can result in 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 increase the overall yield of the process.

[0020] One component of improving yield is to monitor the chipmaking process to ensure that it produces a sufficient number of functional integrated circuits. One way to monitor the process is to inspect the chip circuit structure at various stages of its 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 "photo" of the structure of the wafer. The image can be used to determine whether the structure was formed in the proper manner and whether it was formed in the proper location. If the structure has defects, the process can be adjusted so that the defects are less likely to occur again. Defects may be generated during various stages of semiconductor processing. For the above reasons, it is important to detect defects as early as possible, accurately and efficiently.

[0021] The working principle of SEM is similar to that of a camera. A camera takes a picture by receiving and recording the brightness and color of light reflected or emitted from a person or object. SEM takes a "picture" 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 these electrons to generate an image. In order to take such a "picture", some SEMs use a single electron beam (referred to as a "single-beam SEM"), while some SEMs use multiple electron beams (referred to as a "multi-beam SEM") to take multiple "pictures" of the wafer. By using multiple electron beams, the SEM can provide more electron beams to the structure for obtaining these multiple "pictures", thereby generating more electrons emitted from the structure. Thus, the detector can receive more emitted electrons at the same time and generate images of the structure of the wafer with higher efficiency and faster speed.

[0022] During inspection, it is advantageous to generate an image (e.g., SEM image, optical image, x-ray image, photon image, etc.) with low distortion so that features on the sample in the image (e.g., contact holes, metal lines, gates, etc.) accurately represent the actual sample. To generate an image with low distortion, the image may be adjusted or modified to correct for distortion of features in the image.

[0023] In a typical inspection system, the distortion in the image can be characterized and corrected by using a polynomial. In a typical inspection system, the distortion in the image can be corrected so that the standard deviation ("σ") of the distortion is below a threshold (e.g., so that 3σ is less than the distortion threshold).

[0024] However, typical systems with distortion control are subject to constraints. An example of a constraint of a typical system is that they can only effectively correct distortions that can be characterized by a low-order polynomial expression (e.g., a first-order polynomial expression, a second-order polynomial expression, or a third-order polynomial expression). Low-order polynomial expressions may not accurately characterize some types of distortion. In contrast, high-order distortions are accurately characterized by high-order polynomial expressions (e.g., polynomial expressions greater than third order). For example, high-order distortions may be generated by inspecting a digital-to-analog converter ("DAC") that controls a deflector in a system. Low-order polynomial expressions may not be able to correct high-order distortions (e.g., a distortion threshold may be 3σ<0.18nm, but using a low-order polynomial expression to correct high-order distortions may result in 3σ=0.70nm), such that features on the sample in the image may not accurately represent the actual sample.

[0025] Some of the disclosed embodiments provide systems and methods that address some or all of these disadvantages by adjusting images for high-order distortion during inspection. The disclosed embodiments can determine alignment differences or position differences between features in an image and corresponding features in layout data, model the differences using a high-order model, and use the modeling to adjust the spatial positions of pixels in the image, thereby correcting high-order distortion in the image of the sample.

[0026] For the sake of clarity, the relative sizes of the 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, except those that are not feasible. For example, if it is stated that a component may include A or B, then unless expressly stated otherwise or not feasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component may include A, B, or C, then unless expressly stated otherwise or not feasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0028] Figure 1 An exemplary electron beam inspection (EBI) system 100 according to an embodiment of the present disclosure is illustrated. The EBI system 100 may be used for imaging. Figure 1As shown, 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 one or more additional loading ports. The first loading port 106a and the second loading port 106b receive a wafer front opening unification unit (FOUP), which contains wafers to be inspected (e.g., semiconductor wafers or wafers made of one or more 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 to be processed as a batch.

[0029] One or more robotic arms (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) that removes gas molecules in the load / lock chamber 102 to reach a first pressure below atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) can transport the wafer from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pump system (not shown) that removes gas molecules in the main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is inspected 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 should 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-purpose electronic device or a special-purpose electronic device capable of manipulating or processing information. For example, the processor may include any combination of any number of the following: 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 comprising one or more processors distributed across multiple machines or devices connected 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 electronic device or a special electronic device capable of storing code and data accessible by a processor (e.g., via a bus). For example, the memory may include any combination of any number of the following: random access memory (RAM), read-only memory (ROM), optical disk, disk, hard disk 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 "app") for a specific task. The memory may also be a virtual memory including one or more memories distributed across multiple machines or devices connected via a network.

[0033] Now, refer to Figure 2 ,Should Figure 2 is a diagram illustrating an embodiment according to the present disclosure including as Figure 1 Schematic diagram of an exemplary electron beam tool 104 of a multi-beam inspection tool that is part of an EBI system 100. In some embodiments, the electron beam tool 104 may be operated as Figure 1The single beam inspection tool of a part of the EBI system 100 of the multi-beam electron beam tool 104 (also referred to herein as the device 104) includes an electron source 201, a Coulomb aperture plate (or "gun aperture plate") 271, a focusing lens 210, a source conversion unit 220, a primary projection system 230, a motorized stage 209, and a sample holder 207 supported by the motorized stage 209 to hold a sample 208 to be inspected (e.g., 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 primary 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. The beam splitter 233 and the deflection scanning unit 232 may be located inside the primary projection system 230.

[0034] The electron source 201, the Coulomb aperture plate 271, the converging 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 (virtual or real) primary beam cross 203. The primary electron beam 202 can be visualized as being emitted from the primary beam cross 203.

[0036] The source conversion unit 220 may include an image forming element array (not shown), an aberration compensator array (not shown), a beam limiting aperture array (not shown), and a pre-bent micro-deflector array (not shown). In some embodiments, the pre-bent micro-deflector array deflects multiple primary beam waves 211, 212, 213 of the primary electron beam 202 to vertically enter the beam limiting aperture array, the image forming element array, and the aberration compensator array. In some embodiments, the device 104 can be operated as a single beam system so that a single primary beam wave is generated. In some embodiments, the converging lens 210 is designed to focus the primary electron beam 202 to become a parallel beam and vertically incident on the source conversion unit 220. The image forming element array may include a plurality of micro-deflectors or micro-lenses to affect the multiple primary beam waves 211, 212, 213 of the primary electron beam 202, and form a plurality of parallel images (virtual images or real images) of the primary beam cross 203, one parallel image for one of the primary beam waves 211, 212, and 213. In some embodiments, the aberration compensator array may include a field curvature compensator array (not shown) and an astigmatism compensator array (not shown). The field curvature compensator array may include a plurality of micro lenses to compensate for the field curvature aberration of the primary beam waves 211, 212, and 213. The astigmatism compensator array may include a plurality of micro stigmatizers to compensate for the astigmatism aberration of the primary beam waves 211, 212, and 213. The beam limiting aperture array may be configured to limit the diameters of the individual primary beam waves 211, 212, and 213. Figure 2 Three primary beams 211, 212 and 213 are shown as an example, and it should be appreciated that the source conversion unit 220 may be configured to form any number of primary beams. The controller 109 may be connected to Figure 1 Controller 109 may be used to control various components of the EBI system 100, such as source conversion unit 220, electronic detection device 240, primary projection system 230, or motorized stage 209. In some embodiments, as explained in further detail below, controller 109 may perform various image and signal processing functions. Controller 109 may also generate various control signals to govern the operation of the charged particle beam inspection system.

[0037] The converging lens 210 is configured to focus the primary electron beam 202. The converging lens 210 may also be configured to adjust the current of the primary beam waves 211, 212, 213 downstream of the source conversion unit 220 by varying the focal power of the converging lens 210. Alternatively, the current may be varied by changing the radial size of the beam limiting apertures within the beam limiting aperture array corresponding to the individual primary beam waves. The current may be varied by changing both the radial size of the beam limiting apertures and the focal power of the converging lens 210. The converging lens 210 may be an adjustable converging lens that may be configured such that the position of its first principal plane is movable. The adjustable converging lens may be configured magnetically, which may cause the off-axis beam waves 212 and 213 to illuminate the source conversion unit 220 at a rotation angle. The rotation angle varies with the focal power of the adjustable converging lens or the position of the first principal plane. The converging lens 210 may be an anti-rotation converging lens, which may be configured to maintain a rotation angle while changing the focal power of the converging lens 210. In some embodiments, the converging lens 210 may be an adjustable anti-rotation converging lens, wherein the rotation angle does not change when the focal power of the converging lens 210 and the position of its first principal plane change.

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

[0039] The beam splitter 233 may be, for example, a Wien filter ( Figure 2 2 ), the Wien filter includes an electrostatic deflector that generates an electrostatic dipole field and a magnetic dipole field. In operation, the beam splitter 233 can be configured to exert an electrostatic force on individual electrons of the primary beams 211, 212, and 213 through the electrostatic dipole field. The electrostatic force is equal in magnitude but opposite in direction to the magnetic force exerted on the individual electrons by the magnetic dipole field of the beam splitter 233. Therefore, the primary beams 211, 212, and 213 can pass through the beam splitter 233 at least approximately straight with at least approximately zero deflection angle.

[0040] In operation, the deflection scanning unit 232 is configured to deflect the primary beam waves 211, 212 and 213 to scan the probe spots 221, 222 and 223 across individual scanning areas in a segment of the surface of the sample 208. In response to the incident or detection spots 221, 222 and 223 of the primary beam waves 211, 212 and 213 on the sample 208, electrons emerge from the sample 208 and generate three secondary electron beams 261, 262 and 263. Each of the secondary electron beams 261, 262 and 263 typically includes secondary electrons (having an electron energy ≤ 50 eV) and backscattered electrons (having an electron energy between 50 eV and the landing energy of the primary beam waves 211, 212 and 213). The beam splitter 233 is configured to deflect the secondary electron beams 261, 262 and 263 toward the secondary 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 an image of the corresponding scanned 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 signal outputs (not shown) to an image processing system (e.g., controller 109). In some embodiments, each detection element 241, 242, and 243 may include one or more pixels. The intensity signal output of a 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 including an image acquisition device (not shown) and a storage device (not shown). The image acquisition device may include one or more processors. For example, the image acquisition device may include a computer, a server, a mainframe, a terminal, a personal computer, any kind of mobile computing device, or the like, or a combination thereof. The image acquisition device 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, or the like, or a combination thereof. In some embodiments, the image acquisition device may receive a signal from the electronic detection device 240 and may construct an image. Thus, the image acquisition device may acquire an image of the sample 208. The image acquisition device may also perform various post-processing functions, such as generating a contour, superimposing an indicator on the acquired image, and the like. The image acquisition device may be configured to perform adjustments to the brightness and contrast of the acquired image, and the like. In some embodiments, the storage device 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, and the like. The storage device may be coupled to the image acquirer and may be used to save the scanned raw image data as a raw image and a post-processed image.

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

[0044] In some embodiments, the controller 109 may include a measurement circuit system (e.g., an analog-to-digital converter) to obtain the distribution of the detected secondary electrons. During the detection time window, the collected electron distribution data can be used in combination with the corresponding scan path data of each of the primary beams 211, 212, and 213 incident on the wafer surface 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 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 appreciated 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. In some embodiments, the apparatus 104 may be a SEM for lithography. In some embodiments, the electron beam tool 104 may be a single beam system or a multi-beam system.

[0047] Embodiments of the present disclosure may provide a single charged particle beam imaging system ("single beam system"). Compared with the single charged particle beam imaging system ("single beam system"), a multi-charged particle beam imaging system ("multi-beam system") may be designed to optimize the yield for different scanning modes. Embodiments of the present disclosure provide a multi-beam system that can optimize the yield for different scanning modes by using beam arrays with different geometries and accommodating different yield and resolution requirements.

[0048] Now, refer to Figure 3 ,Should Figure 3 An exemplary configuration of a deflection control unit 320 associated with a segmented charged particle beam deflector (e.g., primary electron beam deflectors 309-1 and 309-2) according to an embodiment of the present disclosure is illustrated. In some embodiments, each deflector may include a plurality of segments. Each of the plurality of segments may include a multipole structure including a plurality of electrodes configured to deflect the primary electron beam. Using a dedicated driver system or driver circuitry capable of supporting the scan frequency and driver linearity, each segment may be electronically driven to adequately deflect the beam to form a large FOV.

[0049] As illustrated, each primary electron beam deflector can be electronically driven by a corresponding driver system. As an example, the deflection control unit 320 may include: a driver system 325-1 associated with the primary electron beam deflector 309-1, and a driver system 325-2 associated with the primary electron beam deflector 309-2. The driver system 325-1 may include a scanning control unit 330, a DAC 334-1, a variable gain amplifier 340-1, and distributed output stages 351-1, 352-1, and 353-1. It should be appreciated that, although not shown, the driver system 325-1 may include other components and circuit systems, such as power supplies, timing circuits, etc., according to the appropriate requirements for manipulating the primary electron beam traveling along the main optical axis 300-1. In some embodiments, each electrode of the deflector may include its own corresponding DAC (for example, a deflector having eight electrodes may include eight DACs).

[0050] The scanning control unit 330 can be configured to generate and supply control signals 351-1a, 352-1a, and 353-1a, which are configured to activate an enabled state or a disabled state of the corresponding distributed output stage. The scanning control unit 330 can also be configured to generate a deflection signal 332-1, which is configured to be applied to one or more segments 309-1A, 309-1B, and 309-1C of the primary electron beam deflector 309-1. In some embodiments, the deflection control unit 320 may include a single scanning control unit 330, which is configured to generate and supply control signals and deflection signals for multiple driver systems (e.g., 325-1 and 325-2). The deflection signal 332-1 may include a voltage signal applied to one or more segments of the primary electron beam deflector.

[0051] In some embodiments, the driver system 325-1 may include a circuit system such as a DAC 334-1, which is configured to convert the digital deflection signal 332-1 into an analog deflection signal. The driver system 325-1 may also include a circuit system such as a variable gain amplifier 340-1, which is configured to receive the analog deflection signal and generate a tunable amplitude of the deflection signal. In general, a variable gain amplifier ("VGA") is a signal conditioning amplifier with an electronically settable voltage gain. VGA 340-1 may include an analog VGA, or a digital VGA, or any suitable circuit system. In some embodiments, the driver system 325-1 may also include a circuit system such as a distributed output stage, which is implemented as a plurality of direct-coupled amplifiers, or relays or other suitable circuit systems.

[0052] In such as Figure 3In an exemplary configuration of a deflection control unit 320 such as the deflection control unit illustrated, segments 309-1A, 309-1B, and 309-1C of a primary electron beam deflector 309-1 may be connected to distributed output stages 351-1, 352-1, and 353-1, respectively. The enabled or disabled states of the distributed output stages 351-1, 352-1, and 353-1 may be activated by control signals 351-1a, 352-1a, and 353-1a, respectively, supplied by a scanning control unit 330. A variable gain amplifier 340-1 may be configured to output a tunable amplitude of a deflection signal 332-1 applied to the primary electron beam deflector 309-1 while maintaining a low noise level. In an enabled mode (in which the distributed output stage is activated by a control signal supplied by the scanning control unit 330), the distributed output stage (e.g., 351-1, 352-1, or 353-1) can reproduce the output signal from the variable gain amplifier 340-1 to drive the corresponding segment of the primary electron beam deflector 309-1. As an example, the control signal 351-1a can activate the enabled state of the distributed output stage 351-1, so that the distributed output stage 351-1 can reproduce an adjusted output signal, which includes a tunable amplitude of the deflection signal 332-1 from the variable gain amplifier 340-1 to be applied to the segment 309-1A of the primary electron beam deflector 309-1. Based on the deflection signal applied to the segment 309-1A of the primary electron beam deflector 309-1, the primary electron beam can be deflected. In some embodiments, in a deactivated mode of a distributed output stage (e.g., 351-1, 352-1, or 353-1), the output signal can be grounded and the distributed output stage can be powered off. Among other advantages, this configuration can help reduce power consumption. The driver system 325-2 can be substantially similar to the driver system 325-1 and can perform substantially similar functions as the driver system 325-1 to control the primary electron beam deflector 309-2. It should be appreciated that the disclosed embodiments can include two or more primary electron beam deflectors and corresponding driver systems.

[0053] Figure 4 4 is a schematic diagram of a system for adjusting distortion in an image according to an embodiment of the present disclosure. System 400 may include inspection system 410 and image distortion adjustment component 420. Inspection system 410 and image distortion adjustment component 420 may be electrically coupled to each other in a physical manner (e.g., via a cable) or remotely (directly or indirectly). Inspection system 410 may be about Figure 1 , Figure 2 and Figure 3 The described method is used to obtain the wafer (see, for example, Figure 2In some embodiments, the components of system 400 may be implemented as one or more servers (e.g., where each server includes its own one or more processors). In some embodiments, the components of system 400 may be implemented as software that can extract data from one or more databases of system 400. In some embodiments, system 400 may include one server or multiple servers. In some embodiments, system 400 may include a controller (e.g., Figure 1 Controller 109, Figure 2 One or more modules implemented by the controller 109).

[0054] Inspection system 410 can obtain a sample (e.g., Figure 2 of a sample 208) (e.g., Figure 5 Each of the plurality of images may include features of the sample (eg, contact holes, metal lines, gates, etc.). The inspection system 410 may transmit data of the plurality of images of the region including the sample to the image distortion adjustment component 420.

[0055] Image distortion adjustment component 420 may include one or more processors (e.g., represented as processor 422, which may have one or more corresponding accelerators) and storage device 424. Image distortion adjustment component 420 may also include communication interface 426 to receive data from and send data to inspection system 410.

[0056] In some embodiments, the processor 422 can be configured to extract corresponding machine settings or parameters (e.g., deflectors, signal frequency of DACs, beam current, landing energy, pixel size, field of view size, etc.) associated with a plurality of images acquired by the inspection system 410. In some embodiments, the processor 422 can be configured to determine a plurality of position coordinates (e.g., x- and y-coordinates, Figure 5 ), wherein each of the plurality of position coordinates corresponds to one of the plurality of features on the acquired image (e.g., Figure 5 Corresponding to feature 512).

[0057] In some embodiments, processor 422 may be configured to obtain layout data corresponding to the obtained image (eg, Figure 5Layout data 514). In some embodiments, the layout data can be obtained by querying a database of layout data. For example, the resist pattern design can be stored in a layout file for a chip design. The layout file can be in a Graphic Database System (GDS) format, a Graphic Database System II (GDSII) format, an Open Artwork System Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), etc. The chip design may include a pattern or structure contained on the chip. The pattern or structure may be a mask pattern that is used to transfer features from a photolithography mask or mask to the chip. In some embodiments, the layout in the GDS or OASIS format may include feature information stored in a binary file format that represents plane geometry, text, and other information related to the chip design. In some embodiments, the resist pattern design may correspond to the field of view (FOV) of the inspection system 410 (for example, the FOV of the inspection system 410 may include one or more layout structures of the resist pattern design). That is, the layout data may include the expected positions of features of the sample (for example, x-coordinates and y-coordinates, Figure 5 position coordinates 514a, etc.).

[0058] In some embodiments, processor 422 may be configured to use the extracted machine settings (eg, parameters) and layout data to align the layout data of features with corresponding features in the acquired image.

[0059] In some embodiments, the processor 422 can be configured to determine the position (e.g., alignment) difference between the features in the acquired image and the corresponding features in the layout data, and model the difference. For example, the difference can include the difference between the position coordinates (e.g., x-coordinates and y-coordinates) of the features in the acquired image and the expected (e.g., target) position coordinates of the features according to the layout data.

[0060] In some embodiments, the processor 422 may be configured to determine a fingerprint of a positional difference between a feature in the obtained image and a corresponding feature in the layout data. That is, the processor 422 may determine a fingerprint of a plurality of alignment differences. In some embodiments, determining the fingerprint may include determining a rotation angle of a plurality of alignment differences. In some embodiments, the processor 422 may be configured to determine the rotation angle of the alignment difference by any of the following: extracting a corresponding machine setting, performing image analysis of a plurality of images, or fitting a model (e.g., a model different from the model used to model the difference between the feature in the obtained image and the corresponding feature in the layout data). In some embodiments, determining the rotation angle by extracting the corresponding machine setting may include extracting a voltage setting, extracting a DAC conversion factor, or any combination thereof to determine the rotation angle. In some embodiments, performing image analysis of a plurality of images may include deriving the rotation angle from the original image. In some embodiments, determining the rotation angle by fitting a model may include using a set of rotation angles; fitting a model; and calculating a difference (e.g., a residual); changing the rotation angle; and searching for a rotation angle that produces the lowest residual. In some embodiments, determining the rotation angle by fitting a model may include extending a cost function to include the rotation angle as a free fitting variable, and minimizing the cost function.

[0061] In some embodiments, the rotation angle may be zero (ie, the rotational displacement of the fingerprint is substantially zero).

[0062] In some embodiments, the processor 422 can be configured to use a model based on corresponding machine settings. For example, the corresponding machine settings can include multiple deflector signal frequencies in a range corresponding to high-order distortion (e.g., distortion characterized by a polynomial expression order greater than three).

[0063] In some embodiments, higher order distortion can be modeled using an expression (e.g., a mathematical model) that can describe the distortion from the DAC, such as an expression that is suitable for correcting the distortion using polynomial terms greater than third order and consistent with the physical processes that cause the higher order distortion. An exemplary expression for modeling the linear displacement of a feature (e.g., displacement in the x-direction and the y-direction) can include the following expression (1):

[0064] F(x,y)=∑ i，j f i，j (x,y) (1)

[0065] Where i is the iteration corresponding to the machine setting (e.g., for the first DAC signal frequency in the frequency range corresponding to high-order distortion, i=1), j indicates the number of functions required to capture the high-order distortion corresponding to the machine setting, f is a mathematical function that can be expressed as a power series (e.g., an infinite power series) where the power is greater than 3, x is the x-component of the position coordinate of the feature in the xy coordinate system, and y is the y-component of the position coordinate of the feature in the xy coordinate system.

[0066] In some embodiments, two models can be used to model the linear difference. For example, the first model can correspond to the displacement in a first direction (e.g., the x-direction), and the second model can correspond to the displacement in a second direction (e.g., the y-direction). It should be understood that the above expression (1) is exemplary, and other expressions can be used to model the differences in the disclosed embodiments.

[0067] In some embodiments, the model coefficients (eg, mathematical function f i，j In some embodiments, the machine setting may be a DAC signal frequency. In some embodiments, a lower limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV. In some embodiments, an upper limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV divided by the number of cycles determined by electrical measurements of the DAC. In some embodiments, the frequency range may be determined based on a 3σ dependence of the residual of the displacement error on the number of frequencies used in the model.

[0068] In some embodiments, the output of the model (e.g., the sum as shown in expression (1)) can be the net linear displacement of any point in the obtained image. For example, the output of the model can be: a value corresponding to the distortion of the obtained image, or a value required to correct the distortion of the obtained image (e.g., how much the pixels of the obtained image need to be adjusted in the x-direction and y-direction to correct the distortion).

[0069] In some embodiments, an exemplary expression for modeling a rotational displacement (eg, a displacement in an angular direction) of a fingerprint for a linear displacement (eg, an alignment difference) may include the following expression (2):

[0070]

[0071] where i is an iteration corresponding to a machine setting (e.g., i=1 for the first DAC signal frequency in a frequency range corresponding to high-order distortion), f is a mathematical function that can be expressed as any series (e.g., any infinite power series, an infinite power series where the power is greater than 3, a Fourier series, a Taylor series, a trigonometric series, a power series, a geometric series, etc.), is the rotation angle of the fingerprint of the linear displacement, x is the x component of the vector of the rotation angle of the fingerprint in the xy coordinate system, and y is the y component of the vector of the rotation angle of the fingerprint in the xy coordinate system.

[0072] It should be understood that the above expression (2) is exemplary and other expressions may be used to model fingerprint differences in the disclosed embodiments.

[0073] In some embodiments, the model coefficients (eg, mathematical function f i In some embodiments, the machine setting may be a DAC signal frequency. In some embodiments, a lower limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV. In some embodiments, an upper limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV divided by the number of cycles determined by electrical measurements of the DAC. In some embodiments, the frequency range may be determined based on a 3σ dependence of the residual of the displacement error on the number of frequencies used in the model.

[0074] In some embodiments, the output of the model (e.g., the sum as shown in expression (2)) can be the net rotational displacement of the fingerprint for the alignment difference in the obtained image. For example, the output of the model can be a value corresponding to the distortion of the fingerprint, or a value that needs to be corrected for the fingerprint distortion.

[0075] In some embodiments, the processor 422 may be configured to determine at least one measurement error associated with the position (e.g., alignment) difference determined above. For example, the processor 422 may use the output of the model (e.g., associated with expression (1) or expressions (1) and (2)) to determine the measurement error and determine which machine settings (e.g., parameters, hardware correctable parameters, etc.) may be changed to correct the measurement error. For example, the processor 422 may: determine at least one new machine setting or parameter (e.g., a different machine setting or parameter), and repeat one or more steps described above using the at least one new machine setting (e.g., a different parameter value) so that the model outputs a different value and corrects at least one measurement error. In some embodiments, some measurement errors (e.g., errors that cannot be corrected by hardware) may be determined and corrected by software modifications.

[0076] In some embodiments, processor 422 may be configured to use modeling to adjust or correct at least one position coordinate corresponding to a feature. For example, processor 422 may be configured to use modeling to adjust or correct pixels of an image so that the adjustment is a distortion correction of the image.

[0077] In some embodiments, the processor 422 may be configured to extract at least one measurement (e.g., line width, line roughness, contact hole diameter, contact hole shape, etc.) from the adjusted image or the corrected image. For example, the processor 422 may be configured to extract measurements from the adjusted image for use in inspecting a sample.

[0078] Now, refer to Figure 5 , Figure 5 is a sample (for example, Figure 2 Schematic diagram 500 of an exemplary alignment of an image 510 of an area of ​​a sample 208) with corresponding layout data.

[0079] As described above, the inspection system (e.g., Figure 4 The inspection system 410 of the embodiment of the present invention can obtain multiple images of an area of ​​the sample. For example, the aligned images 510 can include features 512 of the sample (e.g., contact holes, metal lines, gates, etc.). In some embodiments, each feature 512 can include a corresponding position coordinate 512a (depicted as a center point of the feature 512) having an x-axis coordinate 512x and a y-axis coordinate 512y.

[0080] In some embodiments, the layout data 514 corresponding to the features 512 of the aligned image 510 may include an expected (e.g., target) position coordinate 514a (depicted as a center point of the layout data 514) having an x-axis coordinate 514x and a y-axis coordinate 514y. That is, the layout data 514 may include the expected location of the features 512 of the sample. It should be understood that the layout data 514 may not be depicted in the image 510 in practice, but is shown here for illustration purposes.

[0081] In some embodiments, a processor (e.g., Figure 4 The processor 422 of the embodiment can be configured to determine and model the position (e.g., alignment) difference between the feature 512 and the corresponding feature in the layout data 514. For example, the difference can include the difference between the position coordinate 512a of the feature 512 and the expected position coordinate 514a of the feature 512 according to the layout data 514.

[0082] In some embodiments, the processor can be configured to use a model based on the corresponding machine settings. In some embodiments, two models can be used to model the difference. For example, a first model can correspond to a displacement between feature 512 and layout data 514 in a first direction (e.g., in the x-direction), while a second model can correspond to a displacement between feature 512 and layout data 514 in a second direction (e.g., in the y-direction).

[0083] In some embodiments, the output of the model (e.g., the sum as shown in expression (1) above) can be the net displacement of any point in the obtained image. For example, the output of the model can be: a value corresponding to the distortion of feature 512 in image 510, or a value required to correct the distortion of image 510 (e.g., how much the pixels of feature 512 in image 510 need to be adjusted in the x-direction and y-direction to correct the distortion).

[0084] Now, refer to Figure 6 , Figure 6 6 is a flowchart illustrating an exemplary process 600 for adjusting distortion in an image according to an embodiment of the present disclosure. For illustrative purposes, the steps of the method 600 may be performed by a system (e.g., Figure 4 400) is executed on a computing device (e.g., Figure 1 109) or otherwise using a computing device (e.g., Figure 1 It should be appreciated that the illustrated method 600 may be modified to modify the order of steps and include additional steps.

[0085] At step 601, the system (e.g., Figure 4 The inspection system 410) can obtain a sample (e.g., Figure 2 of a sample 208) (e.g., Figure 5 Each of the plurality of images may include features of the sample (e.g., contact holes, metal lines, gates, etc.). The inspection system may transmit data of the plurality of images including the region of the sample to an image distortion adjustment component (e.g., Figure 4 The image distortion adjustment component 420).

[0086] At step 602, a processor (e.g., Figure 4 The processor 422 of the embodiment can be configured to extract corresponding machine settings or parameters associated with a plurality of images obtained by the inspection system (e.g., deflectors, signal frequency of DAC, beam current, landing energy, pixel size, field of view size, etc.). In some embodiments, the processor can be configured to determine a plurality of position coordinates (e.g., x- and y-coordinates, Figure 5), wherein each of the plurality of position coordinates corresponds to one of the plurality of features on the acquired image (e.g., Figure 5 Corresponding to feature 512).

[0087] At step 603a, the processor may be configured to obtain layout data corresponding to the obtained image (eg, Figure 5 Layout data 514). In some embodiments, the layout data can be obtained by querying a database of layout data. For example, the resist pattern design can be stored in a layout file for a chip design. The layout file can be in a Graphic Database System (GDS) format, a Graphic Database System II (GDSII) format, an Open Artwork System Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), etc. The chip design may include a pattern or structure contained on the chip. The pattern or structure may be a mask pattern used to transfer features from a photolithography mask or mask to a chip. In some embodiments, the layout in the GDS or OASIS format may include feature information stored in a binary file format, which represents plane geometry, text, and other information related to the chip design. In some embodiments, the resist pattern design may correspond to the field of view (FOV) of the inspection system 410 (for example, the FOV of the inspection system 410 may include one or more layout structures of the resist pattern design). That is, the layout data may include the expected positions of features of the sample (for example, x-coordinates and y-coordinates, Figure 5 position coordinates 514a, etc.).

[0088] At step 603b, the processor may be configured to use the extracted machine settings (e.g., parameters) and layout data to align the layout data of the features with the corresponding features in the acquired image. In some embodiments, the processor may be configured to proceed directly from step 602 to step 603b (rather than from step 602 to step 603a to step 603b). For example, when the layout data is the same for multiple iterations of acquiring the image, the processor may be configured to proceed from step 602 to step 603b.

[0089] At step 604, the processor may be configured to determine a position (e.g., alignment) difference between a feature in the acquired image and a corresponding feature in the layout data, and model the difference. For example, the difference may include a difference between a position coordinate (e.g., x-coordinate and y-coordinate) of the feature in the acquired image and an expected (e.g., target) position coordinate of the feature according to the layout data.

[0090] In some embodiments, the processor can be configured to use a model based on corresponding machine settings. For example, the corresponding machine settings can include multiple deflector signal frequencies in a range corresponding to high-order distortion (e.g., distortion characterized by a polynomial expression with an order greater than three).

[0091] In some embodiments, high-order distortion can be modeled using an expression (e.g., a mathematical model) that can describe the distortion from the DAC, such as an expression that is suitable for correcting the distortion using polynomial power terms greater than third order and consistent with the physical processes that cause the high-order distortion. Exemplary expressions may include expression (1):

[0092] F(x,y)=∑ i，j f i，j (x,y) (1)

[0093] Where i is the iteration corresponding to the machine setting (e.g., for the first DAC signal frequency in the frequency range corresponding to high-order distortion, i=1), j indicates the number of functions required to capture the high-order distortion corresponding to the machine setting, f is a mathematical function that can be expressed as a power series (e.g., an infinite power series) where the power is greater than 3, x is the x-component of the position coordinate of the feature in the xy coordinate system, and y is the y-component of the position coordinate of the feature in the xy coordinate system.

[0094] In some embodiments, two models can be used to model the difference. For example, the first model can correspond to the displacement in a first direction (e.g., the x-direction), and the second model can correspond to the displacement in a second direction (e.g., the y-direction). It should be understood that the above expression (1) is exemplary, and other expressions can be used to model the difference in the disclosed embodiments.

[0095] In some embodiments, the model coefficients (eg, mathematical function f i，j In some embodiments, the machine setting may be a DAC signal frequency. In some embodiments, a lower limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV. In some embodiments, an upper limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV divided by the number of cycles determined by electrical measurements of the DAC. In some embodiments, the frequency range may be determined based on a 3σ dependence of the residual of the displacement error on the number of frequencies used in the model.

[0096] In some embodiments, the output of the model (e.g., the sum as shown in expression (1)) can be the net displacement of any point in the obtained image. For example, the output of the model can be: a value corresponding to the distortion of the obtained image, or a value required to correct the distortion of the obtained image (e.g., how much the pixels of the obtained image need to be adjusted in the x-direction and y-direction to correct the distortion).

[0097] At step 605a, the processor may be configured to determine at least one measurement error associated with the position (e.g., alignment) difference determined above. For example, the processor may use the output of the model to determine the measurement error and determine which machine settings (e.g., parameters, hardware correctable parameters, etc.) may be changed to correct the measurement error. For example, at step 605b, the processor may be configured to determine at least one new machine setting or parameter (e.g., a different machine setting or parameter), and may repeat steps 601 to 604 using the at least one new machine setting (e.g., a different parameter value) so that the model outputs a different value and corrects the at least one measurement error.

[0098] For example, the images obtained may be a first plurality of images obtained at a first machine setting, and the alignment difference may be a first alignment difference between a plurality of features on the first plurality of images and corresponding features in the layout data corresponding to the first plurality of images. The modeling may be a first modeling of the first alignment difference. In some embodiments, the processor may use the first modeling to adjust (e.g., step 607 below) at least one feature of the plurality of features on at least one of the first plurality of images. In some embodiments, the processor may determine at least one measurement error associated with the first alignment difference, and determine a second machine setting based on the at least one measurement error. The processor may obtain a second plurality of images at a second machine setting, determine a second alignment difference between a plurality of features on the second plurality of images and corresponding features in the layout data corresponding to the second plurality of images, and use the second modeling to model the second alignment difference. In some embodiments, the processor may use the second modeling to adjust at least one feature of the plurality of features on at least one of the second plurality of images.

[0099] At step 606, some measurement errors (eg, errors that cannot be corrected with hardware, errors other than those determined in step 605a, etc.) may be determined and corrected through software modification.

[0100] At step 607, the processor may be configured to adjust or correct at least one position coordinate corresponding to the feature using modeling. For example, the processor may be configured to adjust or correct pixels of the image using modeling such that the adjustment is a distortion correction of the image.

[0101] At step 608, the processor may be configured to extract at least one measurement (e.g., line width, line roughness, contact hole diameter, contact hole shape, etc.) from the adjusted image or the corrected image. For example, the processor may be configured to extract measurements from the adjusted image for use in inspecting a sample.

[0102] Now, refer to Figure 7 , Figure 7 is a diagram illustrating a sample with corresponding layout data according to an embodiment of the present disclosure (eg, Figure 2 208), and a sample with corresponding layout data (e.g., Figure 2 Schematic diagram 700 of image 710b of an area of ​​sample 208).

[0103] As described above, the inspection system (e.g., Figure 4 The inspection system 410 of the embodiment of the present invention can obtain multiple images of an area of ​​the sample. For example, the aligned image 710a can include features 712a of the sample (e.g., contact holes, metal lines, gates, Figure 5 In some embodiments, each feature 712a may include a feature having an x-axis coordinate (e.g., Figure 5 The x-axis coordinate 512x) and the y-axis coordinate (e.g., Figure 5 The corresponding position coordinates of the y-axis coordinate 512y) (for example, Figure 5 In some embodiments, the processor may generate feature 712a and layout data 714a (e.g., Figure 5 710a). It should be understood that layout data 714a may not be depicted in image 710a in practice, but is shown here for illustrative purposes.

[0104] In some embodiments, one or more fingerprints of feature 712a may include corresponding rotation angles between the fingerprint and a reference fingerprint. In some embodiments, the rotation angle of the fingerprint may be zero (i.e., the rotational displacement of the fingerprint is substantially zero). For example, the rotation angle of one or more fingerprints of feature 712a is Possibly zero.

[0105] In some embodiments, similar to image 710a, aligned image 710b may include features 712b of the sample (eg, contact holes, metal lines, gates, Figure 5 In some embodiments, each feature 712b may include a feature having an x-axis coordinate (e.g., Figure 5The x-axis coordinate 512x) and the y-axis coordinate (e.g., Figure 5 The corresponding position coordinates of the y-axis coordinate 512y) (for example, Figure 5 In some embodiments, the processor may generate feature 712b and layout data 714b (e.g., Figure 5 710b). It should be understood that layout data 714b may not be depicted in image 710b in practice, but is shown here for illustrative purposes.

[0106] In some embodiments, one or more fingerprints of feature 712b may include corresponding rotation angles between the fingerprint and a reference fingerprint. In some embodiments, the rotation angle of the fingerprint May be non-zero (i.e., a rotation angle of -45° may correspond to a rotational displacement of -45° of the fingerprint. For example, the rotation angle of one or more fingerprints of feature 712b may correspond to a rotational displacement of -45° of the fingerprint. It may be -45°. That is, the rotation angle of the model of the alignment difference of feature 712b is It may be -45°, while the actual displacement of feature 712b in image 710b may be in the x-direction or the y-direction.

[0107] In some embodiments, the fingerprint of feature 712b of aligned image 710b may include an expected (eg, target) orientation.

[0108] In some embodiments, a processor (e.g., Figure 4 The processor 422 of the embodiment can be configured to model the linear alignment difference described above by determining a rotation (e.g., rotational alignment) difference between the fingerprint and the reference fingerprint and model the difference. For example, the difference can include the expected orientation of the fingerprint according to the reference fingerprint and the rotation angle between the fingerprint. difference.

[0109] In some embodiments, the processor may be configured to use a model based on a corresponding machine setting.

[0110] In some embodiments, the output of the model (eg, the sum as shown in expression (2) above) may be the net rotational displacement of the fingerprint for the alignment difference. For example, the output of the model may be a value corresponding to the distortion of fingerprint 712b.

[0111] Now, refer to Figure 8 , Figure 88 is a flowchart illustrating an exemplary process 800 for adjusting distortion in an image according to an embodiment of the present disclosure. For illustrative purposes, the steps of the method 800 may be performed by a system (e.g., Figure 4 400) is executed on a computing device (e.g., Figure 1 109) or otherwise using a computing device (e.g., Figure 1 It should be appreciated that the illustrated method 800 may be modified to modify the order of steps and include additional steps.

[0112] At step 801, the system (e.g., Figure 4 The inspection system 410) obtains a sample (e.g., Figure 2 of a sample 208) (e.g., Figure 5 Each of the plurality of images may include features of the sample (e.g., contact holes, metal lines, gates, etc.). The inspection system may transmit data of the plurality of images including the region of the sample to an image distortion adjustment component (e.g., Figure 4 The image distortion adjustment component 420).

[0113] At step 802, a processor (eg, Figure 4 The processor 422 of the embodiment can be configured to extract corresponding machine settings or parameters associated with a plurality of images obtained by the inspection system (e.g., deflectors, signal frequency of DAC, beam current, landing energy, pixel size, field of view size, etc.). In some embodiments, the processor can be configured to determine a plurality of position coordinates (e.g., x- and y-coordinates, Figure 5 ), wherein each of the plurality of position coordinates corresponds to one of the plurality of features on the acquired image (e.g., Figure 5 Corresponding to feature 512).

[0114] At step 803a, the processor may be configured to obtain layout data corresponding to the obtained image (eg, Figure 5Layout data 514). In some embodiments, the layout data can be obtained by querying a database of layout data. For example, the resist pattern design can be stored in a layout file for a chip design. The layout file can be in a Graphic Database System (GDS) format, a Graphic Database System II (GDSII) format, an Open Artwork System Interchange Standard (OASIS) format, a Caltech Intermediate Format (CIF), etc. The chip design may include a pattern or structure contained on the chip. The pattern or structure may be a mask pattern used to transfer features from a photolithography mask or mask to a chip. In some embodiments, the layout in the GDS or OASIS format may include feature information stored in a binary file format, which represents plane geometry, text, and other information related to the chip design. In some embodiments, the resist pattern design may correspond to the field of view (FOV) of the inspection system 410 (for example, the FOV of the inspection system 410 may include one or more layout structures of the resist pattern design). That is, the layout data may include the expected positions of features of the sample (for example, x-coordinates and y-coordinates, Figure 5 position coordinates 514a, etc.).

[0115] At step 803b, the processor may be configured to use the extracted machine settings (e.g., parameters) and layout data to align the layout data of the features with the corresponding features in the acquired image. In some embodiments, the processor may be configured to proceed directly from step 802 to step 803b (rather than from step 802 to step 803a to step 803b). For example, when the layout data is the same for multiple iterations of acquiring the image, the processor may be configured to proceed from step 802 to step 803b.

[0116] At step 804, the processor may be configured to determine a position (e.g., alignment) difference between a feature in the acquired image and a corresponding feature in the layout data, and model the difference. For example, the difference may include a difference between a position coordinate (e.g., x-coordinate and y-coordinate) of the feature in the acquired image and an expected (e.g., target) position coordinate of the feature according to the layout data.

[0117] In some embodiments, the processor can be configured to use a model based on corresponding machine settings. For example, the corresponding machine settings can include multiple deflector signal frequencies in a range corresponding to high-order distortion (e.g., distortion characterized by a polynomial expression with an order greater than three).

[0118] In some embodiments, high-order distortion can be modeled using an expression (e.g., a mathematical model) that can describe the distortion from the DAC, such as an expression that is suitable for correcting the distortion using polynomial terms greater than third order and consistent with the physical processes that cause the high-order distortion. An exemplary expression may include the expression (1):

[0119] F(x,y)=∑ i，j f i，j (x,y) (1)

[0120] Wherein i is the iteration corresponding to the machine setting (e.g., for the first DAC signal frequency in the frequency range corresponding to high-order distortion, i=1), j indicates the number of functions required to capture the high-order distortion corresponding to the machine setting, f is a mathematical function that can be expressed as any series (e.g., any infinite power series, an infinite power series with a power greater than 3, a Fourier series, a Taylor series, a trigonometric series, a power series, a geometric series, etc.), x is the x-component of the position coordinate of the feature in the xy coordinate system, and y is the y-component of the position coordinate of the feature in the xy coordinate system.

[0121] In some embodiments, two models can be used to model the difference. For example, the first model can correspond to the displacement in a first direction (e.g., the x-direction), and the second model can correspond to the displacement in a second direction (e.g., the y-direction). It should be understood that the above expression (1) is exemplary, and other expressions can be used to model the difference in the disclosed embodiments.

[0122] In some embodiments, the model coefficients (eg, mathematical function f i，j In some embodiments, the machine setting may be a DAC signal frequency. In some embodiments, a lower limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV. In some embodiments, an upper limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV divided by the number of cycles determined by electrical measurements of the DAC. In some embodiments, the frequency range may be determined based on a 3σ dependence of the residual of the displacement error on the number of frequencies used in the model.

[0123] In some embodiments, the output of the model (e.g., the sum as shown in expression (1)) can be the net displacement of any point in the obtained image. For example, the output of the model can be: a value corresponding to the distortion of the obtained image, or a value required to correct the distortion of the obtained image (e.g., how much the pixels of the obtained image need to be adjusted in the x-direction and y-direction to correct the distortion).

[0124] At step 805, the processor may be configured to determine a rotational angle difference (eg, alignment difference) between the fingerprint of the linear displacement (eg, alignment difference) and a reference fingerprint. Figure 7 Rotation angle ). In some embodiments, the rotation angle may be zero (ie, the rotational displacement of the fingerprint is substantially zero).

[0125] For example, the difference may include a difference in rotation angle between a fingerprint having a certain orientation and an expected (e.g., target) orientation of the fingerprint according to a reference fingerprint. In some embodiments, the processor may be configured to determine the rotational alignment difference (e.g., the rotation angle between the fingerprint of the alignment difference and the reference fingerprint) by any of the following: extracting corresponding machine settings, performing image analysis of multiple images, or fitting a model (e.g., a model different from the model used to model the difference between the features in the obtained image and the corresponding features in the layout data). In some embodiments, determining the rotation angle by extracting the corresponding machine settings may include: extracting voltage settings, extracting DAC conversion factors, or any combination thereof to determine the rotation angle. In some embodiments, performing image analysis of multiple images may include: deriving the rotation angle from the original image. In some embodiments, determining the rotation angle by fitting a model may include: using a set of rotation angles; fitting a model; and calculating a difference (e.g., a residual); changing the rotation angle; and searching for a rotation angle that produces the lowest residual. In some embodiments, determining the rotation angle by fitting a model may include: extending the cost function to include the rotation angle as a free fitting variable, and minimizing the cost function.

[0126] At step 806 , the processor may be configured to model the rotational displacement (eg, the displacement of the alignment difference).

[0127] In some embodiments, the processor can be configured to use a model based on corresponding machine settings. For example, the corresponding machine settings can include multiple deflector signal frequencies in a range corresponding to high-order distortion (e.g., distortion characterized by a polynomial expression with an order greater than three).

[0128] In some embodiments, high-order distortion can be modeled using an expression (e.g., a mathematical model) that can describe the distortion from the DAC, such as an expression that is suitable for correcting the distortion using polynomial terms greater than third order and consistent with the physical processes that cause the high-order distortion. An exemplary expression may include expression (2):

[0129]

[0130] where i is an iteration corresponding to a machine setting (e.g., i=1 for the first DAC signal frequency in a frequency range corresponding to high-order distortion), f is a mathematical function that can be expressed as a power series (e.g., an infinite power series) where the power is greater than 3, is the rotation angle between the alignment difference fingerprint and the reference fingerprint, x is the x-component of the vector of rotation angles of the fingerprint in the xy coordinate system, and y is the y-component of the vector of rotation angles of the fingerprint in the xy coordinate system.

[0131] It should be understood that expression (2) above is exemplary and that other expressions may be used to model the differences among the disclosed embodiments.

[0132] In some embodiments, the model coefficients (eg, mathematical function f i In some embodiments, the machine setting may be a DAC signal frequency. In some embodiments, a lower limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV. In some embodiments, an upper limit on the DAC signal frequency may be determined by determining a frequency corresponding to the number of pixels in the FOV divided by the number of cycles determined by electrical measurements of the DAC. In some embodiments, the frequency range may be determined based on a 3σ dependence of the residual of the displacement error on the number of frequencies used in the model.

[0133] In some embodiments, the output of the model (e.g., the sum as shown in expression (2)) can be the net rotational displacement of the fingerprint for the alignment difference in the obtained image. For example, the output of the model can be a value corresponding to the distortion of the fingerprint, or a value that requires distortion correction of the fingerprint.

[0134] At step 807a, the processor may be configured to determine at least one measurement error associated with the position (e.g., alignment) difference determined above. For example, the processor may use the output of the model (e.g., associated with expression (1) or expressions (1) and (2)) to determine the measurement error and determine which machine settings (e.g., parameters, hardware correctable parameters, etc.) may be changed to correct the measurement error. For example, at step 807b, the processor may be configured to determine at least one new machine setting or parameter (e.g., a different machine setting or parameter), and may repeat steps 801 to 806 using the at least one new machine setting (e.g., a different parameter value) to cause the model to output a different value and correct the at least one measurement error.

[0135] For example, the images obtained may be a first plurality of images obtained at a first machine setting, and the alignment difference may be a first alignment difference between a plurality of features on the first plurality of images and corresponding features in layout data corresponding to the first plurality of images. The modeling (e.g., associated with expression (1) or expression (2)) may be a first modeling of the first alignment difference. In some embodiments, the processor may use the first modeling to adjust (e.g., step 809 below) at least one feature of the plurality of features on at least one of the first plurality of images. In some embodiments, the processor may determine at least one measurement error associated with the first alignment difference, and determine a second machine setting based on the at least one measurement error. The processor may obtain a second plurality of images at a second machine setting, determine a second alignment difference between a plurality of features on the second plurality of images and corresponding features in layout data corresponding to the second plurality of images, and use the second modeling to model the second alignment difference. In some embodiments, the processor may use the second modeling to adjust at least one feature of the plurality of features on at least one of the second plurality of images.

[0136] At step 808, some measurement errors (eg, errors that cannot be corrected with hardware, errors other than those determined in step 807a, etc.) may be determined and corrected through software modification.

[0137] At step 809, the processor may be configured to adjust or correct at least one position coordinate corresponding to the feature using modeling. For example, the processor may be configured to adjust or correct pixels of the image using modeling such that the adjustment is a distortion correction of the image.

[0138] At step 810, the processor may be configured to extract at least one measurement (e.g., line width, line roughness, contact hole diameter, contact hole shape, etc.) from the adjusted image or the corrected image. For example, the processor may be configured to extract measurements from the adjusted image for use in inspecting a sample.

[0139] A non-transitory computer-readable medium according to an embodiment of the present disclosure may be provided, the non-transitory computer-readable medium storing a controller (eg, Figure 1 109) for controlling an electron beam tool or for controlling a processor of another system or server (e.g., Figure 4Processor 422). These instructions can allow one or more processors to perform image processing, data processing, beam scanning, database management, graphic display, operation of a charged particle beam device or another imaging device, etc. In some embodiments, a non-transitory computer readable medium storing instructions for the processor to perform the steps of process 600 can be provided. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, tapes or any other magnetic data storage media, compact disk read-only memories (CD-ROMs), any other optical data storage media, any physical media with a pattern of holes, random access memories (RAMs), programmable read-only memories (PROMs) and erasable programmable read-only memories (EPROMs), FLASH-EPROMs or any other flash memory, non-volatile random access memories (NVRAMs), caches, registers, any other memory chips or boxes, and networked versions thereof.

[0140] The embodiments may also be described using the following terms.

[0141] 1. A method for distortion adjustment, the system comprising:

[0142] Acquire multiple images;

[0143] determining alignment differences between a plurality of features on the plurality of images and corresponding features in layout data corresponding to the plurality of images;

[0144] modeling the alignment differences; and

[0145] Using the modeling to adjust at least one of the following:

[0146] machine settings corresponding to obtaining the plurality of images; or

[0147] At least one feature of the plurality of features on at least one image of the plurality of images.

[0148] 2. The method of clause 1, wherein obtaining the plurality of images further comprises: extracting corresponding machine settings.

[0149] 3. A method according to any one of clauses 1 to 2, wherein determining the alignment difference comprises: using corresponding the machine settings and the layout data to align the plurality of features on the plurality of images with the corresponding features in the layout data.

[0150] 4. A method according to any of clauses 1 to 2, wherein modeling the alignment differences comprises using a model based on corresponding machine settings.

[0151] 5. The method of clause 4, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0152] 6. A method according to clause 4, wherein the model characterizes higher order distortions.

[0153] 7. The method of clause 4, wherein the model comprises a plurality of models, the plurality of models comprising at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0154] 8. A method according to any one of clauses 1 to 7, wherein the adjustment is a distortion correction.

[0155] 9. The method of any one of clauses 1 to 8, further comprising determining a plurality of metrology errors associated with the alignment difference, and tuning the modeling based on the plurality of metrology errors.

[0156] 10. The method of any of clauses 1 to 9, further comprising extracting a plurality of measurements from the adjusted at least one image.

[0157] 11. The method of any one of clauses 1 to 10, further comprising: determining a fingerprint of the alignment difference.

[0158] 12. The method of clause 11, wherein determining the fingerprint of the alignment difference comprises determining a rotation angle of the alignment difference.

[0159] 13. The method of clause 12, wherein determining the rotation angle of the alignment difference comprises any of: extracting machine settings, image analysis of the plurality of images, or fitting a model.

[0160] 14. A method according to any of clauses 1 to 13, wherein modelling the alignment difference comprises: a rotational adjustment of the fingerprint of the alignment difference.

[0161] 15. A system for distortion adjustment, the system comprising:

[0162] A controller comprising circuitry configured to cause the system to perform:

[0163] Acquire multiple images;

[0164] determining alignment differences between a plurality of features on the plurality of images and corresponding features in layout data corresponding to the plurality of images;

[0165] modeling the alignment differences; and

[0166] Using the modeling to adjust at least one of the following:

[0167] machine settings corresponding to obtaining the plurality of images; or

[0168] At least one feature of the plurality of features on at least one image of the plurality of images.

[0169] 16. The system of clause 15, wherein obtaining the plurality of images further comprises extracting corresponding machine settings.

[0170] 17. The system of any of clauses 15 to 16, wherein determining the alignment differences comprises aligning the plurality of features on the plurality of images with the corresponding features in the layout data using corresponding the machine settings and the layout data.

[0171] 18. The system of any of clauses 15 to 16, wherein modeling the alignment differences comprises using a model based on corresponding machine settings.

[0172] 19. The system of clause 18, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0173] 20. The system of clause 18, wherein the model characterizes higher order distortion.

[0174] 21. The system of clause 18, wherein the model comprises a plurality of models, the plurality of models comprising at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0175] 22. A system according to any of clauses 15 to 21, wherein the adjustment is distortion correction.

[0176] 23. The system of any of clauses 15 to 22, wherein the circuit system is further configured to cause the system to perform: determining a plurality of measurement errors associated with the alignment difference and tuning the modeling based on the plurality of measurement errors.

[0177] 24. The system of any of clauses 15 to 23, wherein the circuitry is further configured to cause the system to perform: extracting a plurality of measurements from the adjusted at least one image.

[0178] 25. The system of any of clauses 15 to 24, wherein the controller comprises circuitry further configured to cause the system to perform: determining a fingerprint of the alignment difference.

[0179] 26. The system of clause 25, wherein determining the fingerprint of the alignment difference comprises determining a rotation angle of the alignment difference.

[0180] 27. The system of clause 26, wherein determining the rotation angle of the alignment difference comprises any of: extracting machine settings, image analysis of the plurality of images, or fitting a model.

[0181] 28. The system of any of clauses 15 to 27, wherein modeling the alignment difference comprises: a rotational adjustment of the fingerprint of the alignment difference.

[0182] 29. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for distortion adjustment, the method comprising:

[0183] Acquire multiple images;

[0184] determining alignment differences between a plurality of features on the plurality of images and corresponding features in layout data corresponding to the plurality of images;

[0185] modeling the alignment differences; and

[0186] Using the modeling to adjust at least one of the following:

[0187] machine settings corresponding to obtaining the plurality of images; or

[0188] At least one feature of the plurality of features on at least one image of the plurality of images.

[0189] 30. The non-transitory computer-readable medium of clause 29, wherein obtaining the plurality of images further comprises extracting corresponding machine settings.

[0190] 31. A non-transitory computer-readable medium according to any one of clauses 29 to 30, wherein determining the alignment difference comprises: using corresponding machine settings and the layout data to align the multiple features on the multiple images with the corresponding features in the layout data.

[0191] 32. The non-transitory computer-readable medium of any of clauses 29 to 30, wherein modeling the alignment differences comprises using a model based on corresponding machine settings.

[0192] 33. The non-transitory computer-readable medium of clause 32, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0193] 34. The non-transitory computer-readable medium of clause 32, wherein the model characterizes higher-order distortion.

[0194] 35. A non-transitory computer-readable medium as described in clause 32, wherein the model includes a plurality of models, the plurality of models including at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0195] 36. The non-transitory computer-readable medium of any one of clauses 29 to 35, wherein the adjustment is a distortion correction.

[0196] 37. A non-transitory computer-readable medium according to any one of clauses 29 to 36, wherein the set of instructions is executable by at least one processor of a computing device so that the computing device also performs: determining multiple measurement errors associated with the alignment difference and tuning the modeling based on the multiple measurement errors.

[0197] 38. The non-transitory computer-readable medium of any of clauses 29 to 37, wherein the set of instructions is executable by at least one processor of a computing device such that the computing device further performs: extracting a plurality of measurements from the adjusted at least one image.

[0198] 39. The non-transitory computer-readable medium of any of clauses 29 to 38, further comprising: determining a fingerprint of the alignment difference.

[0199] 40. The non-transitory computer-readable medium of clause 39, wherein determining the fingerprint of the alignment difference comprises determining a rotation angle of the alignment difference.

[0200] 41. The non-transitory computer-readable medium of clause 40, wherein determining the rotation angle of the alignment difference comprises any of: extracting machine settings, image analysis of the plurality of images, or fitting a model.

[0201] 42. The non-transitory computer-readable medium of any of clauses 29 to 41, wherein modeling the alignment difference comprises: a rotational adjustment of the fingerprint of the alignment difference.

[0202] 43. A method for distortion adjustment, the method comprising:

[0203] acquiring a first plurality of images at a first machine setting;

[0204] determining first alignment differences between a plurality of features on the first plurality of images and corresponding features in layout data corresponding to the first plurality of images;

[0205] modeling the first alignment difference using a first modeling;

[0206] determining at least one measurement error associated with the first alignment difference;

[0207] determining a second machine setting based on the at least one measurement error;

[0208] obtaining a second plurality of images at the second machine setting;

[0209] determining second alignment differences between a plurality of features on the second plurality of images and corresponding features in layout data corresponding to the second plurality of images;

[0210] modeling the second alignment difference using a second modeling; and

[0211] Using the second modeling, at least one of the following is adjusted:

[0212] the second machine setting; or

[0213] At least one feature of the plurality of features on at least one image of the second plurality of images.

[0214] 44. A system for distortion adjustment, the system comprising:

[0215] A controller comprising circuitry configured to cause the system to perform:

[0216] acquiring a first plurality of images at a first machine setting;

[0217] determining first alignment differences between a plurality of features on the first plurality of images and corresponding features in layout data corresponding to the first plurality of images;

[0218] modeling the first alignment difference using a first modeling;

[0219] determining at least one measurement error associated with the first alignment difference;

[0220] determining a second machine setting based on the at least one measurement error;

[0221] obtaining a second plurality of images at the second machine setting;

[0222] determining second alignment differences between a plurality of features on the second plurality of images and corresponding features in layout data corresponding to the second plurality of images;

[0223] modeling the second alignment difference using a second modeling; and

[0224] Using the second modeling, at least one of the following is adjusted:

[0225] the second machine setting; or

[0226] At least one feature of the plurality of features on at least one image of the second plurality of images.

[0227] 45. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for distortion adjustment, the method comprising:

[0228] acquiring a first plurality of images at a first machine setting;

[0229] determining first alignment differences between a plurality of features on the first plurality of images and corresponding features in layout data corresponding to the first plurality of images;

[0230] modeling the first alignment difference using a first modeling;

[0231] determining at least one measurement error associated with the first alignment difference;

[0232] determining a second machine setting based on the at least one measurement error;

[0233] obtaining a second plurality of images at the second machine setting;

[0234] determining second alignment differences between a plurality of features on the second plurality of images and corresponding features in layout data corresponding to the second plurality of images;

[0235] modeling the second alignment difference using a second modeling; and

[0236] Using the second modeling, at least one of the following is adjusted:

[0237] the second machine setting; or

[0238] At least one feature of the plurality of features on at least one image of the second plurality of images.

[0239] 46. ​​A method for distortion adjustment, the method comprising:

[0240] Acquire multiple images;

[0241] determining a plurality of position coordinates, wherein each position coordinate of the plurality of position coordinates corresponds to one of a plurality of features on the plurality of images;

[0242] determining a plurality of differences, wherein each difference in the plurality of differences is between each position coordinate in the plurality of position coordinates and a predetermined position coordinate in a plurality of predetermined position coordinates corresponding to the plurality of features;

[0243] modeling the plurality of differences; and

[0244] Using the modeling to adjust at least one of the following:

[0245] machine settings corresponding to obtaining the plurality of images; or

[0246] At least one position coordinate corresponding to a feature of the plurality of features.

[0247] 47. The method of clause 46, wherein obtaining the plurality of images further comprises extracting corresponding machine settings.

[0248] 48. A method according to any one of clauses 46 to 47, wherein determining the plurality of differences comprises aligning the plurality of position coordinates with the corresponding features in the plurality of predetermined position coordinates using the corresponding machine settings and the plurality of predetermined position coordinates.

[0249] 49. A method according to any of clauses 46 to 47, wherein modelling the plurality of differences comprises using a model based on corresponding settings of the machine.

[0250] 50. The method of clause 49, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0251] 51. The method of clause 49, wherein the model characterizes higher order distortions.

[0252] 52. A method according to clause 49, wherein the model includes a plurality of models, the plurality of models including at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0253] 53. A method according to any of clauses 46 to 52, wherein the adjustment is a distortion correction.

[0254] 54. The method of any of clauses 46 to 53, further comprising determining a plurality of measurement errors associated with the plurality of differences, and tuning the modeling based on the plurality of measurement errors.

[0255] 55. The method of any of clauses 46 to 54, further comprising extracting a plurality of measurements from the adjusted at least one image.

[0256] 56. A system for distortion adjustment, the system comprising:

[0257] A controller comprising circuitry configured to cause the system to perform:

[0258] Acquire multiple images;

[0259] determining a plurality of position coordinates, wherein each position coordinate of the plurality of position coordinates corresponds to one of a plurality of features on the plurality of images;

[0260] determining a plurality of differences, wherein each difference in the plurality of differences is between each position coordinate in the plurality of position coordinates and a predetermined position coordinate in a plurality of predetermined position coordinates corresponding to the plurality of features;

[0261] modeling the plurality of differences; and

[0262] Using the modeling to adjust at least one of the following:

[0263] machine settings corresponding to obtaining the plurality of images; or

[0264] At least one position coordinate corresponding to a feature of the plurality of features.

[0265] 57. The system of clause 56, wherein obtaining the plurality of images further comprises extracting corresponding settings of the machine.

[0266] 58. A system according to any one of clauses 56 to 57, wherein determining the plurality of differences comprises aligning the plurality of position coordinates with the corresponding features in the plurality of predetermined position coordinates using the corresponding machine settings and the plurality of predetermined position coordinates.

[0267] 59. The system of any of clauses 56 to 57, wherein modeling the plurality of differences comprises using a model based on corresponding settings of the machine.

[0268] 60. The system of clause 59, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0269] 61. The system of clause 59, wherein the model characterizes higher order distortion.

[0270] 62. A system according to clause 59, wherein the model includes a plurality of models, the plurality of models including at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0271] 63. A system according to any of clauses 56 to 62, wherein the adjustment is distortion correction.

[0272] 64. The system of any of clauses 56 to 63, wherein the circuit system is further configured to cause the system to perform: determining a plurality of measurement errors associated with the plurality of differences and tuning the modeling based on the plurality of measurement errors.

[0273] 65. The system of any of clauses 56 to 64, wherein the circuit system is further configured to cause the system to perform: extracting a plurality of measurements from the adjusted at least one image.

[0274] 66. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for distortion adjustment, the method comprising:

[0275] Acquire multiple images;

[0276] determining a plurality of position coordinates, wherein each position coordinate of the plurality of position coordinates corresponds to one of a plurality of features on the plurality of images;

[0277] determining a plurality of differences, wherein each difference in the plurality of differences is between each position coordinate in the plurality of position coordinates and a predetermined position coordinate in a plurality of predetermined position coordinates corresponding to the plurality of features;

[0278] modeling the plurality of differences; and

[0279] Using the modeling to adjust at least one of the following:

[0280] machine settings corresponding to obtaining the plurality of images; or

[0281] At least one position coordinate corresponding to a feature of the plurality of features.

[0282] 67. The non-transitory computer-readable medium of clause 66, wherein obtaining the plurality of images further comprises extracting corresponding machine settings.

[0283] 68. A non-transitory computer-readable medium according to any one of clauses 66 to 67, wherein determining the plurality of differences comprises: using corresponding the machine settings and the plurality of predetermined position coordinates to align the plurality of position coordinates with the corresponding features in the plurality of predetermined position coordinates.

[0284] 69. The non-transitory computer-readable medium of any of clauses 66 to 67, wherein modeling the plurality of differences comprises using a model based on corresponding settings of the machine.

[0285] 70. The non-transitory computer-readable medium of clause 69, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

[0286] 71. The non-transitory computer-readable medium of clause 69, wherein the model characterizes higher-order distortion.

[0287] 72. A non-transitory computer-readable medium as described in clause 69, wherein the model includes a plurality of models, the plurality of models including at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

[0288] 73. The non-transitory computer-readable medium of any one of clauses 66 to 72, wherein the adjustment is a distortion correction.

[0289] 74. A non-transitory computer-readable medium according to any one of clauses 66 to 73, wherein the instruction set is executable by at least one processor of a computing device so that the computing device also performs: determining multiple measurement errors associated with the multiple differences and tuning the modeling based on the multiple measurement errors.

[0290] 75. The non-transitory computer-readable medium of any of clauses 66 to 74, wherein the set of instructions is executable by at least one processor of a computing device such that the computing device further performs: extracting a plurality of measurements from the adjusted at least one image.

[0291] It should be appreciated that the embodiments of the present disclosure are not limited to the exact constructions that have been described above and illustrated in the drawings, and that various modifications and changes may be made without departing from the scope of the present disclosure.

Claims

1. A system for distortion adjustment, the system comprising: A controller comprising circuitry configured to cause the system to perform: Acquire multiple images; determining alignment differences between a plurality of features on the plurality of images and corresponding features in layout data corresponding to the plurality of images; modeling the alignment differences; as well as Using the modeling to adjust at least one of the following: machine settings corresponding to obtaining the plurality of images; or At least one feature of the plurality of features on at least one image of the plurality of images.

2. The system of claim 1, wherein obtaining the plurality of images further comprises: The corresponding machine settings are extracted.

3. The system of claim 1 , wherein determining the alignment difference comprises: The plurality of features on the plurality of images are aligned with the corresponding features in the layout data using corresponding the machine settings and the layout data.

4. The system of claim 1 , wherein modeling the alignment differences comprises: A model based on the corresponding machine settings is used.

5. The system of claim 4, wherein the corresponding machine settings include a plurality of deflector signal frequencies. The system of claim 4 , wherein the model characterizes higher order distortion.

7. The system of claim 4, wherein the model comprises a plurality of models, the plurality of models comprising at least one model corresponding to a first dimension of the alignment difference and at least one model corresponding to a second dimension of the alignment difference.

8. The system of claim 1, wherein the adjustment is distortion correction.

9. The system of claim 1, wherein the circuit system is further configured to cause the system to perform: determining a plurality of measurement errors associated with the alignment difference and tuning the modeling based on the plurality of measurement errors.

10. The system of claim 1, wherein the circuitry is further configured to cause the system to perform: extracting a plurality of measurements from the adjusted at least one image.

11. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for distortion adjustment, the method comprising: Acquire multiple images; determining alignment differences between a plurality of features on the plurality of images and corresponding features in layout data corresponding to the plurality of images; modeling the alignment differences; as well as Using the modeling to adjust at least one of the following: machine settings corresponding to obtaining the plurality of images; or At least one feature of the plurality of features on at least one image of the plurality of images.

12. The non-transitory computer readable medium of claim 11, wherein obtaining the plurality of images further comprises: The corresponding machine settings are extracted.

13. The non-transitory computer readable medium of claim 11 , wherein determining the alignment difference comprises: The plurality of features on the plurality of images are aligned with the corresponding features in the layout data using corresponding the machine settings and the layout data.

14. The non-transitory computer readable medium of claim 11 , wherein modeling the alignment difference comprises: A model based on the corresponding machine settings is used.

15. The non-transitory computer readable medium of claim 14, wherein the corresponding machine settings include a plurality of deflector signal frequencies.

Images