System and method for improving image quality during inspection

Abstract

Systems, apparatuses, and methods for improving image quality. In some embodiments, a method may include: acquiring a plurality of images of a sample region; determining via a phase diversity analysis: a plurality of focus correlation values, wherein each focus correlation value of the plurality of focus correlation values is associated with each image of the plurality of images; performing maximum likelihood estimation on the plurality of images; and generating a focus-corrected image of the region based on the determined plurality of focus-related values and the determined maximum likelihood estimation.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to EP application 22170392.9 filed on April 27, 2022, and 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 improving image quality during inspections. Background Art

[0004] During the manufacturing process of integrated circuits (ICs), unfinished or completed circuit components need to be inspected to ensure that they are manufactured as designed and are free of defects. Inspection systems using optical microscopes typically have a resolution as low as 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, inspection systems with higher resolution than those using optical microscopes are needed.

[0005] Charged particle (e.g., electron) beam microscopes, such as scanning electron microscopes (SEMs) or transmission electron microscopes (TEMs), which can have resolutions down to sub-nanometers, can be used as practical tools for inspecting IC components with feature sizes less than 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 and may be backscattered or may cause the wafer to emit secondary electrons. The intensity of the electron beam, which includes backscattered electrons and secondary electrons, may vary based on the properties of the internal and external structures of the wafer, which may indicate whether the wafer is defective. Summary of the invention

[0006] Embodiments of the present disclosure provide devices, systems, and methods for improving image quality. In some embodiments, the systems, methods, and non-transitory computer-readable media may include the following steps: acquiring multiple images of a sample area; determining by phase diversity analysis: multiple focus correlation values, wherein each of the multiple focus correlation values ​​is associated with each of the multiple images; a maximum likelihood estimate of the multiple images; and generating a focus-corrected image of the area based on the determined multiple focus correlation values ​​and the determined maximum likelihood estimate.

[0007] In some embodiments, the systems, methods, and non-transitory computer-readable media may include the following steps: acquiring multiple images of a sample area; determining through phase diversity analysis: multiple focus correlation values, wherein each of the multiple focus correlation values ​​is associated with each of the multiple images; maximum likelihood estimation of the multiple images; and generating a focus adjusted image of the area based on the determined multiple focus correlation values ​​and the determined maximum likelihood estimation.

[0008] In some embodiments, the systems, methods, and non-transitory computer-readable media may include the following steps: acquiring multiple images of an area in a sample field of view, wherein a first image in the multiple images has a first focus-related value and a second image in the multiple images has a second focus-related value different from the first focus-related value; and generating a focus-adjusted image of the area in the field of view using the first and second images. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0010] Figure 2 is a schematic diagram showing an exemplary multi-beam system consistent with an embodiment of the present disclosure, the exemplary multi-beam system is Figure 1 A portion of an exemplary charged particle beam inspection system.

[0011] Figure 3 is a schematic diagram of an exemplary system for improving image quality consistent with embodiments of the present disclosure.

[0012] Figure 4 is a schematic diagram showing exemplary samples and image generation schematics consistent with embodiments of the present disclosure.

[0013] Figure 5 is a flow chart illustrating an exemplary process for improving image quality consistent with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] Reference will now be made in detail to exemplary embodiments, examples of which are shown in the accompanying drawings. The following description refers to the accompanying drawings, wherein, unless otherwise specified, the same numbers in different figures represent the same or similar elements. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with aspects related to the subject matter 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. Other types of charged particle beams may be similarly applied. In addition, other imaging systems may be used, such as optical imaging, photoelectric detection, X-ray detection, extreme ultraviolet inspection, deep ultraviolet inspection, etc., wherein they generate images of corresponding types.

[0015] Electronic devices are made up of circuits formed on a silicon wafer, called a substrate. Many circuits may be formed on the same silicon wafer, called an integrated circuit, or IC. The size of these circuits has been reduced dramatically so that more can fit on a substrate. For example, an IC chip in a smartphone can be as small as a thumbnail but may contain more than 2 billion transistors, each less than one thousandth the size of a human hair.

[0016] 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 unusable. 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, i.e., to increase the yield of the process.

[0017] One element of improving yield is monitoring the chip manufacturing process to ensure that sufficient numbers of functional integrated circuits are produced. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. This inspection can be done using a scanning electron microscope (SEM). The SEM can be used to image these extremely small structures, in effect taking a "photograph" of the wafer structure. This image can be used to determine if the structure was formed correctly and if it was formed in the correct location. If the structure is defective, the process can be adjusted so that the defect is less likely to occur again. Defects can be created at various stages of semiconductor processing. For the reasons listed above, it is important to detect defects early, accurately and efficiently.

[0018] 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 by a person or object. SEM takes a "picture" by receiving and recording the energy or number of electrons reflected or emitted by a structure. Before taking such a "picture", an electron beam can be provided to the structure, and when the electrons are reflected or emitted ("leave") 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 (called a "single-beam SEM"), while some SEMs use multiple electron beams (called 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 to obtain these multiple "pictures", resulting in more electrons leaving the structure. In this way, the detector can receive a greater number of emitted electrons at the same time, generating images of the wafer structure with higher efficiency and faster speed.

[0019] During inspection, it is advantageous to generate images (e.g., SEM images, optical images, x-ray images, photon images, etc.) with higher resolution or clarity so that the features on the sample in the image (e.g., contacts, metal lines, gates, etc.) accurately represent the actual sample. In order to generate images with higher resolution or clarity, the images of the features on the sample need to be focused. Aberrations (e.g., an out-of-focus electron beam) may result in blurry, low-quality images. To facilitate obtaining a high-quality focus, the settings of the inspection system (e.g., the voltage or intensity of the objective lens) can be adjusted to adjust the detection size of the electron beam.

[0020] In a typical inspection system, a focus measurement of the inspection system is performed to facilitate the generation of high-quality images. A typical focus measurement involves acquiring an image of a sample in an area outside the field of view (FOV). For example, the FOV may include a sample area to be inspected, and a typical focus measurement acquires an image of the sample outside the area to be inspected. A typical focus measurement involves acquiring multiple images of the sample outside the field of view at different defocus values. For example, before acquiring each image of the area outside the field of view, the objective lens may be adjusted (e.g., using different voltage values ​​or different current values) so that each image of the focus measurement has a different defocus value. An inspection setting (e.g., a voltage value of the objective lens) corresponding to the highest resolution image or the highest clarity image of the area outside the field of view (e.g., determined by a key performance indicator (KPI) of resolution or clarity) is used to acquire a sample image within the field of view (e.g., one or more areas within the field of view, the entire field of view, etc.) during inspection.

[0021] In a typical inspection system, multiple images of a sample within the field of view are acquired using an inspection setting corresponding to a highest resolution image or highest definition image of an area outside the field of view during focus measurement. An average of the multiple images (e.g., also referred to as multiple frames) of the sample within the field of view is determined to generate an inspection image of the sample within the field of view.

[0022] However, typical focus measurement and inspection are subject to limitations. One example of a limitation of typical inspection is that inspection throughput is low because multiple focus measurement images are acquired outside the FOV and multiple inspection measurement images are acquired inside the FOV. Another example of a limitation of typical inspection is that focus deviations may occur on the sample (e.g., the curvature field may vary within the FOV), thereby reducing the accuracy and quality of the inspection images generated because focus measurements of areas outside the FOV may not be applicable to inspection images acquired from areas inside the FOV. Another example of a limitation of typical inspection is that noise in the acquired images may reduce the throughput of the inspection images generated.

[0023] Some disclosed embodiments provide systems and methods that address some or all of these shortcomings by improving image quality during inspection. The disclosed embodiments can use phase diversity analysis to determine focus-related values ​​(e.g., through-focus values) and maximum likelihood estimates of sample images within a FOV, thereby enabling the generation of a focus-adjusted (e.g., focus-corrected) image of the sample.

[0024] For the sake of clarity, the relative sizes of the components in the figures may be exaggerated. In the following description of the figures, the same or similar reference numerals denote the same or similar components or entities, and only the differences with respect to the various embodiments are described.

[0025] As used herein, unless expressly stated otherwise, the term "or" encompasses all possible combinations unless not feasible. For example, if a component is specified to 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 a component is specified to 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.

[0026] Figure 1 An exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure is shown. 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 additional loading ports. The first loading port 106a and the second loading port 106b receive a wafer front opening transport box (FOUP) containing a wafer (e.g., a semiconductor wafer or a wafer made of other materials) or a sample to be inspected (wafers and samples can be used interchangeably). "Batch" refers to a plurality of wafers that can be loaded and processed as a batch.

[0027] One or more robots (not shown) in the EFEM 106 can transfer the wafer to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown), which removes gas molecules in the load / lock chamber 102 to reach a first pressure below atmospheric pressure. After reaching the first pressure, one or more robots (not shown) can transfer the wafer from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pump system (not shown), which removes gas molecules in the main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer will be inspected by the electron beam tool 104. The electron beam tool 104 can be a single beam system or a multi-beam system.

[0028] The controller 109 is electronically connected to the electron beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. Figure 1 The controller 109 is shown in FIG. 1 as being external to the structure including the main chamber 101 , the load / lock chamber 102 , and the EFEM 106 , but it will be appreciated that the controller 109 may be part of the structure.

[0029] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or special 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, which includes one or more processors distributed on multiple machines or devices connected via a network.

[0030] In some embodiments, the controller 109 may also include one or more memories (not shown). The memory may be a general or special electronic device capable of storing processor-accessible code and data (e.g., via a bus). For example, the memory may include any number of any combination of the following: random access memory (RAM), read-only memory (ROM), optical disk, magnetic disk, hard disk, 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 application programs (or "apps") for specific tasks. The memory may also be a virtual memory, which includes one or more memories distributed on multiple machines or devices connected via a network.

[0031] Reference now Figure 2 , which is a schematic diagram showing an exemplary electron beam tool 104, which includes a multi-beam inspection tool, which is Figure 1 In some embodiments, the electron beam tool 104 may be operated as a single beam inspection tool. Figure 1 The multi-beam electron beam tool 104 (also referred to herein as the apparatus 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 for holding 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. A beam splitter 233 and a deflection scanning unit 232 may be located within the primary projection system 230.

[0032] 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.

[0033] The electron source 201 may include a cathode (not shown) and an extractor or an anode (not shown), wherein, during operation, the electron source 201 is configured to emit primary electrons from the cathode, and the primary electrons are extracted or accelerated by the extractor and / or the anode to form a primary electron beam 202, which forms a primary beam cross (virtual or real) 203. The primary electron beam 202 may be visualized as being emitted from the primary beam cross 203.

[0034] 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 the multiple primary sub-beams 211, 212, 213 of the primary electron beam 202 to normally enter the beam limiting aperture array, the image forming element array, and the aberration compensator array. In some embodiments, the device 104 can operate as a single beam system to generate a single primary beam. In some embodiments, the converging lens 210 is designed to focus the primary electron beam 202 into a parallel beam and vertically incident on the source conversion unit 220. The image forming element array may include a plurality of micro-deflectors or micro-lenses to affect the multiple primary sub-beams 211, 212, 213 of the primary electron beam 202 and form a plurality of parallel images (virtual or real) of the primary beam cross 203, each of the primary sub-beams 211, 212, and 213 having a parallel image. 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 beamlets 211, 212, and 213. The astigmatism compensator array may include a plurality of micro stigmatizers to compensate for the astigmatism aberration of the primary beamlets 211, 212, and 213. The beam limiting aperture array may be configured to limit the diameter of each of the primary beamlets 211, 212, and 213. Figure 2 Three primary sub-beams 211, 212, and 213 are shown as an example, and it should be understood that the source conversion unit 220 can be configured to form any number of primary sub-beams. The controller 109 can be connected to Figure 1 The controller 109 may be used to control various parts of the EBI system 100, such as the source conversion unit 220, the electronic detection device 240, the primary projection system 230, or the motorized stage 209. In some embodiments, as described in further detail below, the controller 109 may perform various image and signal processing functions. The controller 109 may also generate various control signals to control the operation of the charged particle beam inspection system.

[0035] The converging lens 210 is configured to focus the primary electron beam 202. The converging lens 210 can also be configured to adjust the current of the primary sub-beams 211, 212 and 213 downstream of the source conversion unit 220 by changing the optical power of the converging lens 210. Alternatively, the current can be changed by changing the radial size of the beam-limiting apertures within the beam-limiting aperture array corresponding to each primary sub-beam. The current can be changed by changing the radial size of the beam-limiting aperture and the optical power of the converging lens 210. The converging lens 210 can be an adjustable converging lens, which can be configured so that the position of its first principal plane is movable. The adjustable converging lens can be configured to be magnetic, which can cause the off-axis sub-beams 212 and 213 to illuminate the source conversion unit 220 at a rotation angle. The rotation angle varies with the optical power of the adjustable converging lens or the position of the first principal plane. The converging lens 210 can be an anti-rotation converging lens, which can be configured to keep the rotation angle unchanged when the optical power of the converging lens 210 changes. In some embodiments, the converging lens 210 may be an adjustable anti-rotation converging lens, whose rotation angle does not change when the optical power and the position of the first principal plane change.

[0036] The objective lens 231 can be configured to focus the sub-beams 211, 212, and 213 onto the sample 208 for inspection, and in the current embodiment, three detection points 221, 222, and 223 can be formed on the surface of the sample 208. The Coulomb aperture plate 271 is configured to block peripheral electrons of the primary electron beam 202 in operation to reduce the Coulomb effect. The Coulomb effect can enlarge the size of each detection point 221, 222, and 223 of the primary sub-beams 211, 212, and 213, thereby reducing the inspection resolution.

[0037] The beam splitter 233 may be, for example, a Wien filter including an electrostatic deflector ( Figure 2 213). In operation, the beam splitter 233 can be configured to apply an electrostatic force to the individual electrons of the primary sub-beams 211, 212, and 213 through an electrostatic dipole field. The magnitude of the electrostatic force is equal to the magnetic force applied to the individual electrons by the magnetic dipole field of the beam splitter 233, but the direction is opposite. Therefore, the primary sub-beams 211, 212, and 213 can pass through the beam splitter 233 at least substantially straight, and the deflection angle is at least substantially zero.

[0038] The deflection scanning unit 232 is configured to deflect the primary beamlets 211, 212 and 213 in operation to scan the detection points 221, 222 and 223 on the respective scanning areas in the surface portion of the sample 208. In response to the primary beamlets 211, 212 and 213 or the detection points 221, 222 and 223 being incident 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 generally 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 beamlets 211, 212 and 213). The beam splitter 233 is configured to deflect the secondary electron beams 261, 262 and 263 to 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.

[0039] 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 the detection element may be the sum of the signals generated by all pixels within the detection element.

[0040] In some embodiments, the controller 109 may include an image processing system, which includes an image acquisition device (not shown) and a memory (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 type of mobile computing device, or a combination thereof. The image acquisition device may be 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, radio, 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, etc. The image acquisition device may be configured to perform adjustments to the brightness and contrast of the acquired image, etc. In some embodiments, the memory may be a storage medium such as a hard disk, a flash memory, a cloud storage, a random access memory (RAM), or other types of computer-readable memory. The memory may be coupled to the image acquisition device to save the scanned raw image data as a raw image and a post-processed image.

[0041] 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 memory. The single image can be an original image, which can be divided into multiple areas. Each area 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 sampled multiple times in a time series. Multiple images can be stored in a memory. 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.

[0042] In some embodiments, the controller 109 may include a measurement circuit system (e.g., an analog-to-digital converter) to obtain a distribution of the detected secondary electrons. The electron distribution data collected during the detection time window, combined with the corresponding scan path data of each primary beamlet 211, 212, and 213 incident on the wafer surface, can be used to reconstruct an image of the inspected wafer structure. The reconstructed image can be used to reveal various features of the internal or external structure of the sample 208, and thus can be used to reveal any defects that may be present in the wafer.

[0043] 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 cause the motorized stage 209 to continuously move the sample 208 in a certain direction at a constant speed. In other embodiments, the controller 109 may cause the motorized stage 209 to change the movement speed of the sample 208 over time according to the steps of the scanning process.

[0044] although Figure 2 The apparatus 104 is shown using three primary electron beams, but it is understood that the apparatus 104 may use two or more primary electron beams. The present disclosure does not limit the number of primary electron beams used in the apparatus 104. In some embodiments, the apparatus 104 may be a SEM for photolithography. In some embodiments, the electron beam tool 104 may be a single beam system or a multi-beam system.

[0045] Compared with a single charged particle beam imaging system ("single beam system"), a multiple charged particle beam imaging system ("multi-beam system") can be designed to optimize throughput for different scanning modes. Embodiments of the present disclosure provide a multi-beam system that can optimize throughput for different scanning modes by using beam arrays with different geometries to accommodate different throughput and resolution requirements.

[0046] Figure 3 3 is a schematic diagram of a system for improving image quality consistent with an embodiment of the present disclosure. System 300 may include inspection system 310 and image generation component 320. Inspection system 310 and image generation component 320 may be electrically coupled to each other (directly or indirectly) in a physical manner (e.g., via a cable) or remotely. Inspection system 310 may be about Figure 1 and Figure 2 The system described is used to acquire an image of a wafer (see e.g. Figure 2 In some embodiments, the components of system 300 may be implemented as one or more servers (e.g., where each server includes its own processor). In some embodiments, the components of system 300 may be implemented as software that can extract data from one or more databases of system 300. In some embodiments, system 300 may include one server or multiple servers. In some embodiments, system 300 may include a controller (e.g., Figure 1 Controller 109, Figure 2 One or more modules implemented by the controller 109).

[0047] Inspection system 310 may obtain a sample (e.g., Figure 2 Sample 208 or Figure 4 of the sample 400) (e.g., Figure 4411 or 412) of the region (e.g., Figure 4 Due to the adjustment of the inspection setting during the image acquisition, each of the multiple images may have a different focus-related value (e.g., a defocus value). For example, the adjusted inspection setting may be the intensity of the objective lens (e.g., using a different voltage value or a different current value). In some embodiments, the FOV (e.g., Figure 4 Before each image of the area of ​​FOV 410) adjust the objective lens (e.g., Figure 2 The inspection system 310 may be configured to include an objective lens 231 such that each image has a different defocus value. In some embodiments, the adjusted inspection setting may be an intensity range (e.g., a voltage value range) of the objective lens. Each of the multiple images may be obtained from the same area of ​​the sample in the FOV. The inspection system 310 may transmit data including multiple images of the sample area to the image generation component 320.

[0048] Image generation component 320 may include one or more processors (e.g., represented as processor 322, which may have one or more corresponding accelerators) and memory 324. Image generation component 320 may also include communication interface 326 to receive data from and send data to inspection system 310. In some embodiments, processor 322 may be configured to determine a defocus value associated with each of a plurality of images received from inspection system 310 and a maximum likelihood estimate of the plurality of images using phase diversity analysis.

[0049] Phase diversity analysis involves using multiple images of the same region, where phase diversity (e.g., aberrations, defocused electron beams, etc.) can be introduced to the region before each of the multiple images are acquired. For example, phase diversity can be introduced to the region before each image is acquired by adjusting the intensity of the objective lens before each image is acquired. By controlling the phase diversity introduced to the region, the processor 322 can maximize the objective function and solve for the unknown variable (e.g., the defocus value of the image).

[0050] Phase diversity analysis can be used to obtain maximum likelihood estimates of multiple images from the through-focus images and their associated through-focus values. In some embodiments, the maximum likelihood estimate can be a final, focus-adjusted (e.g., focus-corrected) image of the area within the FOV. Since the through-focus distance between images may be known, the maximum likelihood estimate can be obtained regardless of whether the through-focus value corresponds to a true unknown through-focus value. Phase diversity analysis allows the maximum likelihood estimate and the true unknown through-focus value to be determined simultaneously. The image generation component 320 can advantageously use phase diversity analysis and a non-iterative approach to maximize the objective function because it solves for a single unknown variable (e.g., the through-focus value of the image), thereby increasing the throughput of the inspection.

[0051] In some embodiments, other types of phase diversity may be used in the phase diversity analysis. For example, types of phase diversity that may be used in addition to or instead of the defocus value include aperture shift, detuning via an astigmatism filter, using a beam deflector to probe different parts of the generalized pupil function that characterizes the electro-optical array, etc.

[0052] In some embodiments, higher order aberrations may be used for phase diversity analysis (e.g., astigmatism). In some cases, higher order aberrations or other types of phase diversity may not be preferred because they may involve solving for more unknown variables, thereby increasing the computational load, increasing throughput, and reducing the accuracy of generating corrected images. In some embodiments, these limitations can be reduced by increasing the number of images obtained from the area in the FOV.

[0053] The embodiments advantageously acquire images within the FOV without acquiring images outside the FOV (e.g., Figure 4 region 420) to generate an image of the sample (e.g., for inspection), thereby improving throughput.

[0054] In some embodiments, the above method can be applied to multiple areas within the field of view (e.g., Figure 4 For example, the first area of ​​the sample in the field of view (e.g., Figure 4 A first phase diversity analysis may be performed on a first set of images associated with a second region of the sample in the field of view (e.g., Figure 4 A second phase diversity analysis is performed on a second set of images associated with a region 412 of the image acquisition system, wherein the first region may be different from the second region (e.g., a separate region). In some embodiments, the adjustment of the inspection settings for the first region (e.g., using different voltage values ​​or different current values, ranges of voltage values, etc.) may be the same as the adjustment of the inspection settings for the second region (e.g., the voltage value used to obtain images from the first region is the same as the voltage value used to obtain images from the second region). It should be understood that using the same inspection setting adjustments for different regions does not necessarily mean that the images obtained from the two regions will have the same associated focus-related values. In some embodiments, the adjustment of the inspection settings for the first region may be different from the adjustment of the inspection settings for the second region (e.g., one or more voltage values ​​used to obtain images from the first region are different from one or more voltage values ​​used to obtain images from the second region).

[0055] In some embodiments, performing phase diversity analysis to generate images of different regions of a sample can improve the accuracy of the calculations, thereby improving the quality of the generated images, by performing local calculations rather than applying the same inspection settings (e.g., objective lens intensity) across the entire FOV. For example, in order to generate higher quality images of a sample in the FOV, the voltage settings used on the objective lens may vary depending on the region being inspected in the FOV. That is, the above method can be performed on one or more regions within the FOV so that a focus-corrected image local to (e.g., specific to) that region can be generated. This method may be advantageous over methods that use the same inspection settings (e.g., objective lens voltage) across the entire FOV to generate images. In addition, determining the focus value for each region can provide valuable diagnostic data to improve the control or design of the imaging system.

[0056] This method can advantageously improve ( Figure 4 The image quality of the image 454) can be improved because a focus-corrected image of the area can be generated by acquiring an image of the same area, rather than relying on focus measurements performed outside the FOV.

[0057] By using images obtained from within the field of view to generate a focus-corrected image of a sample area within the field of view, the method can advantageously improve the image quality of the generated image by counteracting focus deviations that may occur on the sample (e.g., the field of view curvature may vary across the field of view). For example, the focus of the electron beam may change as the electron beam moves away from the optical axis (e.g., the center of the field of view).

[0058] For example, a first region of a sample (e.g., Figure 4 The region 411) may be located near the center of the FOV, while a second region of the sample (e.g., Figure 4 The region 412) may be located outside the center of the FOV. A first phase diversity analysis may be performed to generate an inspection image of the first region using images obtained from the first region. A second phase diversity analysis may be performed to generate an inspection image of the second region using images obtained from the second region. The disclosed embodiments include generating images that overcome focus deviation limitations on the sample FOV.

[0059] In some embodiments, one or more of the images obtained may include noise that reduces the accuracy of the calculated defocus value or the accuracy of the maximum likelihood estimate, thereby reducing the quality of the one or more images generated. In some embodiments, since the electron beam dose used to obtain the regional image is low (e.g., the nominal number of incident primary electrons per pixel is low), the noise in the obtained image can be modeled by a Poisson distribution. The obtained image can be denoised, for example, using methods such as Zhang et al. in "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE 10838, 2019 / 1 / 16. The residual noise in the image can be characterized by a Gaussian distribution.

[0060] In some embodiments, processor 322 may use phase diversity analysis to determine a defocus value associated with each of the plurality of de-noised images and a maximum likelihood estimate of the plurality of de-noised images. Phase diversity analysis may be performed in accordance with the disclosed embodiments.

[0061] By denoising the image obtained by modeling the Poisson distribution and characterizing the residual noise in the image according to the Gaussian distribution, the method can advantageously reduce the amount of computation required during phase diversity analysis, thereby producing higher quality images and improving throughput. Phase diversity analysis of denoised images can be performed using methods such as those used in Zhang et al., "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE10838, 2019 / 1 / 16. The method can be applied without denoising in low-dose situations.

[0062] Reference now Figure 4 , which shows an exemplary sample 400 and an image generation schematic 450 consistent with an embodiment of the present disclosure.

[0063] As mentioned above, the inspection system (e.g. Figure 3 The inspection system 310) can obtain a sample (e.g., Figure 2410). Each of the plurality of images 452 may be obtained from a region 411 of the sample 208 of the FOV 410. Due to adjustment of an inspection setting during image acquisition, each of the plurality of images 452 may have a different focus-related value (e.g., a defocus value). For example, the adjusted inspection setting may be an intensity of an objective lens (e.g., using a different voltage value). In some embodiments, the objective lens may be adjusted before each image of the region 411 of the FOV 410 is obtained so that each image has a different defocus value. In some embodiments, the adjusted inspection setting may be an intensity range (e.g., a voltage value range) of the objective lens. Each of the plurality of images 452 may be obtained from the same region 411 of the sample in the FOV 420.

[0064] As described above, the image generation component (e.g., Figure 3 The image generation component 320 may include a processor (e.g., Figure 3 A processor 322) is configured to use phase diversity analysis to determine a defocus value associated with each image of the plurality of images 452 and a maximum likelihood estimate of the plurality of images 452.

[0065] Phase diversity analysis may be used to obtain a maximum likelihood estimate of the plurality of images 452 from the through-focus images and their associated through-focus values. In some embodiments, the maximum likelihood estimate may be a final, in-focus (e.g., focused) image 454 of the area 411 within the FOV 410. Since the through-focus distances between the images may be known, the maximum likelihood estimate may be obtained regardless of whether the through-focus values ​​correspond to true unknown through-focus values. Phase diversity analysis allows for simultaneous determination of the maximum likelihood estimate as well as the true unknown through-focus values. The image generation component may advantageously use phase diversity analysis and a non-iterative approach to maximize an objective function because it solves for a single unknown variable (e.g., the through-focus values ​​of the images), thereby increasing the throughput of the inspection.

[0066] The described embodiments advantageously increase throughput by acquiring images 454 of sample 400 (eg, for inspection) by acquiring images 452 within FOV 410 without acquiring images from areas 420 outside of FOV 410 .

[0067] In some embodiments, the above method may be performed on multiple regions 411 and 412 within FOV 410. For example, a first phase diversity analysis may be performed on a first set of images associated with a first region 411 of sample 400 in FOV 420. A second phase diversity analysis may be performed on a second set of images associated with a second region 412 of sample 400 in FOV 410, where first region 411 may be different from (e.g., a separate region) than second region 412.

[0068] In some embodiments, performing phase diversity analysis to generate images of different regions of a sample can improve the accuracy of the calculations, thereby improving the quality of the generated images, by performing local calculations rather than applying the same inspection settings (e.g., objective lens intensity) across the entire FOV 410. For example, to generate a higher quality image of the sample 400 in the FOV 410, the voltage settings used on the objective lens can vary depending on the region being inspected in the FOV 410. That is, the above method can be performed for one or more regions within the FOV 410 so that a focus-corrected image local to (e.g., specific to) that region can be generated. This method may be more advantageous than a method that uses the same inspection settings (e.g., objective lens voltage) across the entire FOV 410 to generate images. For example, the method can use a first inspection setting to acquire an image and generate a focus-corrected image for region 411, and use a second inspection setting to acquire an image and generate a focus-corrected image for region 412, rather than using the same inspection settings for regions 411 and 412 without performing separate analysis on the two regions. In addition, determining the focus value for each region can provide valuable diagnostic data to improve the control or design of the imaging system.

[0069] This method can advantageously improve the generated images (e.g. Figure 4 The image quality of the image 454) can be improved because a focus-corrected image of the area can be generated by acquiring an image of the same area, rather than relying on focus measurements performed in an area 420 outside of the FOV 410.

[0070] By using images obtained from within FOV 410 to generate a focus-corrected image of a sample region within FOV 410, the method can advantageously improve the image quality of the generated image by counteracting focus deviations that may occur on the sample (e.g., field curvature may vary across the FOV). For example, the focus of the electron beam on region 411 may be different from the focus of the electron beam on region 412.

[0071] For example, region 411 of sample 400 may be located near the center of FOV 410, while region 412 of sample 400 may be located outside the center of FOV 410. A first phase diversity analysis may be performed to generate an inspection image of region 411 using images obtained from region 411. A second phase diversity analysis may be performed to generate an inspection image of region 412 using images obtained from region 412. The disclosed embodiments include generating images that overcome focus deviation limitations on the sample FOV. In some embodiments, a single beam may be used to obtain images from one or more regions of a sample within the FOV. In some embodiments, two or more regions of a sample within the FOV may be scanned with different beams.

[0072] In some embodiments, one or more of the acquired images 452 may contain noise that reduces the accuracy of the calculated defocus value or the accuracy of the maximum likelihood estimate, thereby reducing the quality of the generated one or more images 454. In some embodiments, since the electron beam dose used to obtain the regional image is low (e.g., the nominal number of incident primary electrons per pixel is low), the noise in the acquired image can be modeled by a Poisson distribution. The acquired image 452 can be denoised, for example, using a method such as that used by Zhang et al. in "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE 10838, 2019 / 1 / 16. The residual noise in the image can be characterized by a Gaussian distribution.

[0073] In some embodiments, the processor may use phase diversity analysis to determine a defocus value associated with each of the plurality of de-noised images 452 and a maximum likelihood estimate of the plurality of de-noised images 452. The phase diversity analysis may be performed consistent with the disclosed embodiments.

[0074] By denoising the acquired image 452 modeled according to a Poisson distribution and characterizing the residual noise in the image 452 according to a Gaussian distribution, the method can advantageously reduce the amount of computation required during phase diversity analysis, thereby producing higher quality images 454 and improving throughput. The denoised image 452 can be subjected to phase diversity analysis using methods such as those used in Zhang et al., "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE 10838, 2019 / 1 / 16. In low-dose situations, the method can be applied without denoising.

[0075] Reference now Figure 5 , which shows a flowchart of an exemplary method 500 for improving image quality consistent with an embodiment of the present disclosure. The steps of the method 500 can be performed by a system (e.g., Figure 3 For purposes of illustration, the system 300 may be executed on a computing device or otherwise use features of a computing device, such as Figure 1 Controller 109. It will be appreciated that the illustrated method 500 may be altered to modify the order of steps and include additional steps.

[0076] In step 501, the system (e.g., using Figure 3 The inspection system 310) can obtain a sample (e.g., Figure 2 Sample 208 or Figure 4 of the sample 400) (e.g., Figure 4 411 or 412) of the region (e.g., Figure 4 Due to the adjustment of the inspection setting during the image acquisition, each of the multiple images may have a different focus-related value (e.g., through-focus value). For example, the adjusted inspection setting may be the intensity of the objective lens (e.g., using a different voltage value). In some embodiments, the FOV (e.g., Figure 4 Before each image of the area of ​​FOV 410) adjust the objective lens (e.g., Figure 2 The objective lens 231 is configured such that each image has a different defocus value. In some embodiments, the adjusted inspection setting can be an intensity range (e.g., a voltage value range) of the objective lens. Each of the multiple images can be obtained from the same area of ​​the sample in the FOV.

[0077] In step 503, a processor (eg, Figure 3 The processor 322) can be configured to use phase diversity analysis to determine a defocus value associated with each of a plurality of images received from the inspection system and a maximum likelihood estimate of the plurality of images.

[0078] Phase diversity analysis involves using multiple images of the same area, where phase diversity (e.g., aberrations, defocused electron beams, etc.) can be introduced to the area before each of the multiple images is obtained. For example, phase diversity can be introduced to the area before each image is obtained by adjusting the intensity of the objective lens before each image is obtained. By controlling the phase diversity introduced to the area, the processor 322 can maximize the objective function and solve for the unknown variable (e.g., the defocus value of the image). Phase diversity analysis can be used to obtain a maximum likelihood estimate of the multiple images from the defocus values ​​associated with each image.

[0079] In some embodiments, other types of phase diversity may be used in the phase diversity analysis. For example, types of phase diversity that may be used in addition to or instead of the defocus value include aperture shift, detuning via an astigmatism filter, using a beam deflector to probe different parts of the generalized pupil function that characterizes the electro-optical array, etc.

[0080] In some embodiments, higher order aberrations may be used for phase diversity analysis (e.g., astigmatism). In some cases, higher order aberrations or other types of phase diversity may not be preferred because they may involve solving for more unknown variables, thereby increasing the computational load, increasing throughput, and reducing the accuracy of generating corrected images. In some embodiments, these limitations can be reduced by increasing the number of images obtained from the area in the FOV.

[0081] At step 505, the processor may generate a focus adjustment (eg, focus correction) image of the region based on the determined plurality of defocus values ​​and the determined maximum likelihood estimate (eg, Figure 4 In some embodiments, the maximum likelihood estimate may be a final focus-corrected image of the region within the field of view (e.g., Figure 4 454). Since the defocus distance between the images may be known, a maximum likelihood estimate can be obtained regardless of whether the defocus value corresponds to a true unknown defocus value. Phase diversity analysis allows the maximum likelihood estimate as well as the true unknown defocus value to be determined simultaneously. The processor advantageously uses phase diversity analysis and a non-iterative approach to maximize the objective function because it solves for a single unknown variable (e.g., the defocus value of the image), thereby increasing the throughput of the inspection.

[0082] This method can advantageously improve the generated images (e.g. Figure 4 The image of the sample (e.g., for inspection) may be generated in an area 420 outside of the FOV, thereby improving throughput.

[0083] In some embodiments, the above method can be applied to multiple areas within the field of view (e.g., Figure 4 For example, the first area of ​​the sample in the field of view (e.g., Figure 4 A first phase diversity analysis may be performed on a first set of images associated with a second region of the sample in the field of view (e.g., Figure 4 A second phase diversity analysis is performed on a second set of images associated with a region 412 of the image processing unit, wherein the first region may be different from (eg, a separate region) the second region.

[0084] In some embodiments, performing phase diversity analysis to generate images of different regions of a sample can improve the accuracy of the calculations, thereby improving the quality of the generated images, by performing local calculations rather than applying the same inspection settings (e.g., objective lens intensity) across the entire FOV. For example, in order to generate higher quality images of a sample in the FOV, the voltage settings used on the objective lens may vary depending on the region being inspected in the FOV. That is, the above method can be performed on one or more regions within the FOV so that a focus-corrected image local to (e.g., specific to) that region can be generated. This method may be advantageous over methods that use the same inspection settings (e.g., objective lens voltage) across the entire FOV to generate images. In addition, determining the focus value for each region can provide valuable diagnostic data to improve the control or design of the imaging system.

[0085] The method may advantageously improve the image quality of the generated images because a focus-corrected image of an area may be generated by acquiring images of the same area, rather than relying on focus measurements performed outside the FOV.

[0086] By using images obtained from within the field of view to generate a focus-corrected image of a sample area within the field of view, the method can advantageously improve the image quality of the generated image by counteracting focus deviations that may occur on the sample (e.g., the field of view curvature may vary within the field of view). For example, the focus of the electron beam may change as the electron beam moves away from the optical axis (e.g., the center of the field of view).

[0087] For example, a first region of a sample (e.g., Figure 4 The region 411) may be located near the center of the FOV, while a second region of the sample (e.g., Figure 4 The region 412) may be located outside the center of the FOV. A first phase diversity analysis may be performed to generate an inspection image of the first region using images obtained from the first region. A second phase diversity analysis may be performed to generate an inspection image of the second region using images obtained from the second region. The disclosed embodiments include generating images that overcome focus deviation limitations on the sample FOV.

[0088] In some embodiments, one or more of the images obtained may include noise that reduces the accuracy of the calculated defocus value or the accuracy of the maximum likelihood estimate, thereby reducing the quality of the one or more images generated. In some embodiments, since the electron beam dose used to obtain the regional image is low (e.g., the nominal number of incident primary electrons per pixel is low), the noise in the obtained image can be modeled by a Poisson distribution. The obtained image can be denoised, for example, using methods such as Zhang et al. in "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE 10838, 2019 / 1 / 16. The residual noise in the image can be characterized by a Gaussian distribution.

[0089] In some embodiments, the processor may determine a defocus value associated with each of the plurality of de-noised images and a maximum likelihood estimate of the plurality of de-noised images using phase diversity analysis.The phase diversity analysis may be performed consistent with the disclosed embodiments.

[0090] By denoising the acquired images modeled according to the Poisson distribution and characterizing the residual noise in the images according to a Gaussian distribution, the method can advantageously reduce the amount of computation required during phase diversity analysis, thereby producing higher quality images and increasing throughput. Phase diversity analysis of denoised images can be performed using methods such as those used by Zhang et al. in "Modified phase diversity technique to eliminate Poisson noise for reconstructing high-resolution images", Proc. SPIE 10838, 2019 / 1 / 16. The method can be applied without denoising in low-dose situations.

[0091] A non-transitory computer readable medium may be provided that stores a controller (eg, Figure 1 109) for controlling the electron beam tool, controlling the electron beam applied to the objective lens (e.g., Figure 2 231), or a processor that controls other systems and servers (e.g., Figure 3322 of the processor), consistent with the embodiments of the present disclosure. These instructions may allow one or more processors to perform image processing, data processing, beamlet 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 may be provided, which stores instructions for the processor to perform the steps of process 500. 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 hole patterns, 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 cartridges, and networked versions thereof.

[0092] The embodiments may be further described using the following terms:

[0093] 1. A method of improving image quality, the method comprising: acquiring a plurality of images of an area of ​​a sample; determining via phase diversity analysis: a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and

[0094] A focus-corrected image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE.

[0095] 2. A method according to clause 1, wherein each focus-related value of the plurality of focus-related values ​​comprises a defocus value.

[0096] 3. A method according to any one of claims 1 to 2, wherein each image of the plurality of images has a different associated focus-related value.

[0097] 4. A method according to any of clauses 1 to 3, wherein the plurality of focus-related values ​​comprises a range of focus-related values.

[0098] 5. A method according to any of clauses 1 to 4, wherein the range of focus-related values ​​corresponds to a range of voltages associated with an objective lens.

[0099] 6. The method according to any one of claims 1 to 5, wherein the region is located within the field of view of the sample.

[0100] 7. A method according to any one of clauses 1 to 6, wherein the MLE is determined based on the determined plurality of focus-related values.

[0101] 8. A method according to any one of clauses 1 to 7, wherein:

[0102] The regions include a first region of the sample and a second region of the sample;

[0103] The plurality of images include a first set of images of the first area of ​​the sample and a second set of images of the second area of ​​the sample; the phase diversity analysis includes a first phase diversity analysis corresponding to the first set of images and a second phase diversity analysis corresponding to the second set of images; the plurality of focus-related values ​​include a first set of focus-related values ​​corresponding to the first phase diversity analysis and a second set of focus-related values ​​corresponding to the second phase diversity analysis;

[0104] The MLEs include a first MLE for the first set of images and a second MLE for the second set of images; and the focus-corrected image includes a first focus-corrected image for the first region and a second focus-corrected image for the second region.

[0105] 9. The method of clause 8, further comprising: determining via the first phase diversity analysis:

[0106] The first set of focus-related values, wherein each focus-related value in the first set of focus-related values ​​is associated with each image in the first set of images; the first MLE of the first set of images; and generating the first focus-corrected image of the first area based on the determined plurality of the first focus-related values ​​and the determined first MLE.

[0107] 10. The method according to any one of clauses 8-9 further includes: determining via the second phase diversity analysis: the second set of focus-related values, wherein each focus-related value in the second set of focus-related values ​​is associated with each image in the second set of images; the second MLE of the second set of images; and generating the second focus correction image of the second area based on the determined multiple second focus-related values ​​and the determined second MLE.

[0108] 11. The method according to any one of claims 1 to 10, further comprising: denoising the noise in the acquired multiple images, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the acquired multiple images based on a Gaussian distribution.

[0109] 12. The method of clause 11, wherein each of the plurality of focus-related values ​​is associated with each denoised image of the plurality of images modeled based on the Gaussian distribution.

[0110] 13. A method of improving image quality, the method comprising: acquiring a plurality of images of a region of a sample; determining via phase diversity analysis: a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and

[0111] A focus adjusted image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE.

[0112] 14. A method for improving image quality, the method comprising: acquiring multiple images of an area in a field of view of a sample, wherein a first image in the multiple images has a first focus-related value, and a second image in the multiple images has a second focus-related value different from the first focus-related value; and generating a focus-adjusted image of the area in the field of view using the first image and the second image.

[0113] 15. The method of claim 14, wherein generating the focus adjustment image of the region of the field of view comprises: performing a phase diversity analysis on the first image and the second image; and

[0114] A maximum likelihood estimate of the first image and the second image is determined.

[0115] 16. Method according to any of clauses 14 to 15, wherein the first focus-related value corresponds to a first voltage associated with an objective lens and the second focus-related value corresponds to a second voltage associated with the objective lens.

[0116] 17. The method according to any one of clauses 14 to 16, further comprising:

[0117] Noise in the first image and the second image is denoised, wherein the noise is modelled as a Poisson distribution; and the denoised noise in the first image and the second image is modelled according to a Gaussian distribution.

[0118] 18. The method of claim 17, wherein the first focus-related value is associated with the first denoised image and the second focus-related value is associated with the second denoised image.

[0119] 19. A system for improving image quality, the system comprising: a controller including a circuit system configured to cause the system to perform the following:

[0120] Multiple images of an area of ​​the sample are acquired; wafer phase diversity analysis determines:

[0121] a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and generating a focus-corrected image of the region based on the determined plurality of focus-related values ​​and the determined MLE.

[0122] 20. The system of claim 19, wherein each focus-related value of the plurality of focus-related values ​​comprises a defocus value.

[0123] 21. A system according to any one of claims 19 to 20, wherein each image of the plurality of images has a different associated focus-related value.

[0124] 22. A system according to any of clauses 19 to 21, wherein the plurality of focus-related values ​​comprises a range of focus-related values.

[0125] 23. A system according to any of clauses 19 to 22, wherein the range of focus-related values ​​corresponds to a range of voltages associated with an objective lens.

[0126] 24. The system of any one of claims 19 to 23, wherein the region is located within a field of view of the sample.

[0127] 25. A system according to any of clauses 19 to 24, wherein the MLE is determined based on the determined plurality of focus-related values.

[0128] 26. A system according to any one of clauses 19-25, wherein: the region includes a first region of the sample and a second region of the sample; the multiple images include a first group of images of the first region of the sample and a second group of images of the second region of the sample; the phase diversity analysis includes a first phase diversity analysis corresponding to the first group of images and a second phase diversity analysis corresponding to the second group of images; the multiple focus-related values ​​include a first group of focus-related values ​​corresponding to the first phase diversity analysis and a second group of focus-related values ​​corresponding to the second phase diversity analysis; the MLE includes a first MLE for the first group of images and a second MLE for the second group of images; and

[0129] The focus correction image includes a first focus correction image of the first area and a second focus correction image of the second area.

[0130] 27. A system according to claim 26, wherein the circuit system is configured such that the system also performs: determining via the first phase diversity analysis: the first set of focus-related values, wherein each focus-related value in the first set of focus-related values ​​is associated with each image in the first set of images; the first MLE of the first set of images; and generating the first focus correction image of the first area based on the determined multiple first focus-related values ​​and the determined first MLE.

[0131] 28. A system according to any of clauses 26-27, wherein the circuit system is configured to cause the system to further perform: determining, via the second phase diversity analysis: the second set of focus-related values, wherein each focus-related value in the second set of focus-related values ​​is associated with each image in the second set of images; the second MLE of the second set of images; and

[0132] The second focus corrected image of the second area is generated based on the determined plurality of the second focus-related values ​​and the determined second MLE.

[0133] 29. A system according to any one of clauses 19-28, wherein the circuit system is configured so that the system also performs: denoising the noise in the multiple images obtained, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the multiple images obtained according to a Gaussian distribution.

[0134] 30. The system of clause 29, wherein each focus-related value of the plurality of focus-related values ​​is associated with each denoised image of the plurality of images modeled based on the Gaussian distribution.

[0135] 31. A system for improving image quality, the system comprising: a controller including circuitry configured to cause the system to: acquire a plurality of images of a region of a sample;

[0136] Determining via phase diversity analysis: a plurality of focus-related values, wherein each of the plurality of focus-related values ​​is associated with each of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and generating a focus adjustment image of the region based on the determined plurality of focus-related values ​​and the determined MLE.

[0137] 32. A system for improving image quality, the system comprising: a controller including a circuit system configured to cause the system to perform the following: acquiring multiple images of an area in a field of view of a sample, wherein a first image in the multiple images has a first focus-related value and a second image in the multiple images has a second focus-related value different from the first focus-related value; and generating a focus-adjusted image of the area in the field of view using the first image and the second image.

[0138] 33. The system of claim 32, wherein generating the focus adjusted image of the area in the field of view comprises: performing a phase diversity analysis on the first image and the second image; and determining a maximum likelihood estimate of the first image and the second image.

[0139] 34. A system according to any of clauses 32-33, wherein the first focus-related value corresponds to a first voltage associated with an objective lens, and the second focus-related value corresponds to a second voltage associated with the objective lens.

[0140] 35. A system according to any one of clauses 32 to 34, wherein the circuit system is configured such that the system also performs: denoising noise in the first image and the second image, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the first image and the second image according to a Gaussian distribution.

[0141] 36. The system of clause 35, wherein the first focus-related value is associated with the first image that is denoised, and the second focus-related value is associated with the second image that is denoised.

[0142] 37. 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 improving image quality, the method comprising: acquiring a plurality of images of a region of a sample; determining by phase diversity analysis: a plurality of focus correlation values, wherein each focus correlation value of the plurality of focus correlation values ​​is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and

[0143] A focus-corrected image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE.

[0144] 38. The non-transitory computer-readable medium of clause 37, wherein each focus-related value of the plurality of focus-related values ​​comprises a defocus value.

[0145] 39. The non-transitory computer-readable medium of any of clauses 37-38, wherein each image of the plurality of images has a different associated focus-related value.

[0146] 40. The non-transitory computer-readable medium of any of clauses 37-39, wherein the plurality of focus-related values ​​comprises a range of focus-related values.

[0147] 41. The non-transitory computer-readable medium of any of clauses 37-40, wherein the range of focus-related values ​​corresponds to a range of voltages associated with an objective lens.

[0148] 42. The non-transitory computer-readable medium of any of clauses 37-41, wherein the region is within a field of view of the sample.

[0149] 43. The non-transitory computer-readable medium of any of clauses 37-42, wherein the MLE is determined based on the determined plurality of focus-related values.

[0150] 44. The non-transitory computer-readable medium of any of clauses 37-43, wherein:

[0151] The regions include a first region of the sample and a second region of the sample;

[0152] The multiple images include a first group of images of the first area of ​​the sample and a second group of images of the second area of ​​the sample; the phase diversity analysis includes a first phase diversity analysis corresponding to the first group of images and a second phase diversity analysis corresponding to the second group of images; the multiple focus-related values ​​include a first group of focus-related values ​​corresponding to the first phase diversity analysis and a second group of focus-related values ​​corresponding to the second phase diversity analysis; the MLE includes a first MLE of the first group of images and a second MLE of the second group of images; and the focus correction image includes a first focus correction image of the first area and a second focus correction image of the second area.

[0153] 45. A non-transitory computer-readable medium according to clause 44, wherein the set of instructions is executable by at least one processor of a computing device to cause the computing device to further perform: determining, via the first phase diversity analysis: the first set of focus-related values, wherein each focus-related value in the first set of focus-related values ​​is associated with each image in the first set of images; a first MLE of the first set of images; and

[0154] The first focus corrected image of the first area is generated based on the determined plurality of first focus-related values ​​and the determined first MLE.

[0155] 46. ​​A non-transitory computer-readable medium according to any one of clauses 44-45, wherein the instruction set is capable of being executed by at least one processor of a computing device so that the computing device also performs: determining via the second phase diversity analysis: the second set of focus-related values, wherein each focus-related value in the second set of focus-related values ​​is associated with each image in the second set of images; a second MLE of the second set of images; and generating the second focus-corrected image of the second area based on the determined multiple second focus-related values ​​and the determined second MLE.

[0156] 47. A non-transitory computer-readable medium according to any one of clauses 37-46, wherein the instruction set is executable by at least one processor of a computing device so that the computing device further performs: denoising noise in the multiple images obtained, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the multiple images obtained according to a Gaussian distribution.

[0157] 48. The non-transitory computer-readable medium of clause 47, wherein each focus-related value of the plurality of focus-related values ​​is associated with each denoised image of the plurality of images modeled based on a Gaussian distribution.

[0158] 49. 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 improving image quality, the method comprising: acquiring a plurality of images of a sample area; determining by phase diversity analysis: a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; a maximum likelihood estimate (MLE) of the plurality of images; and

[0159] A focus adjusted image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE.

[0160] 50. A non-transitory computer-readable medium storing an instruction set capable of being executed by at least one processor of a computing device to cause the computing device to perform a method for improving image quality, the method comprising: acquiring multiple images of an area in a field of view of a sample, wherein a first image of the multiple images has a first focus-related value and a second image of the multiple images has a second focus-related value different from the first focus-related value; and generating a focus-adjusted image of the area in the field of view using the first image and the second image.

[0161] 51. A non-transitory computer-readable medium according to clause 50, wherein generating the focus adjustment image of the area in the field of view includes: performing a phase diversity analysis on the first image and the second image; and determining a maximum likelihood estimate of the first image and the second image.

[0162] 52. The non-transitory computer-readable medium of any of clauses 50-51, wherein the first focus-related value corresponds to a first voltage associated with an objective lens and the second focus-related value corresponds to a second voltage associated with the objective lens.

[0163] 53. A non-transitory computer-readable medium according to any one of clauses 50-52, wherein the instruction set is capable of being executed by at least one processor of a computing device so that the computing device also performs: denoising the noise in the first image and the second image, wherein the noise is modeled as a Poisson distribution; and modeling the denoised noise in the first image and the second image according to a Gaussian distribution.

[0164] 54. The non-transitory computer-readable medium of clause 53, wherein the first focus-related value is associated with a denoised first image and the second focus-related value is associated with a denoised second image.

[0165] It should be understood that the embodiments of the present disclosure are not limited to the exact constructions described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A system for improving image quality, the system include: A controller comprising a circuit system configured to cause the system to perform the following: acquiring multiple images of an area of ​​a sample; Determined by phase diversity analysis: a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; Maximum likelihood estimation MLE of the multiple images; as well as A focus-corrected image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE. 2 . The system of claim 1 , wherein each of the plurality of focus-related values ​​comprises a defocus value. 3 . The system of claim 1 , wherein each image of the plurality of images has a different associated focus-related value. The system of claim 1 , wherein the plurality of focus-related values ​​comprises a range of focus-related values.

5. The system of claim 1, wherein the range of focus-related values ​​corresponds to a range of voltages associated with the objective lens.

6. The system of claim 1, wherein the region is within a field of view of the sample.

7. The system of claim 1, wherein the MLE is determined based on the determined plurality of focus-related values.

8. The system according to claim 1, in: The regions include a first region of the sample and a second region of the sample; The plurality of images includes a first set of images of the first area of ​​the sample and a second set of images of the second area of ​​the sample; The phase diversity analysis includes a first phase diversity analysis corresponding to the first set of images and a second phase diversity analysis corresponding to the second set of images; The plurality of focus-related values ​​include: a first set of focus-related values ​​corresponding to the first phase diversity analysis; and a second set of focus correlation values ​​corresponding to said second phase diversity analysis; The MLEs include a first MLE for the first set of images and a second MLE for the second set of images; and The focus correction image includes a first focus correction image of the first area and a second focus correction image of the second area.

9. The system of claim 8, wherein the circuit system is configured such that the system further performs: The first phase diversity analysis determined that: the first set of focus-related values, wherein each focus-related value of the first set of focus-related values ​​is associated with each image of the first set of images; the first MLE of the first set of images; and The first focus-corrected image of the first area is generated based on the determined plurality of the first focus-related values ​​and the determined first MLE.

10. The system of claim 8, wherein the circuit system is configured such that the system further performs: The second phase diversity analysis determined that: the second set of focus-related values, wherein each focus-related value of the second set of focus-related values ​​is associated with each image of the second set of images; the second MLE of the second set of images; and The second focus corrected image of the second area is generated based on the determined plurality of the second focus-related values ​​and the determined second MLE.

11. The system of claim 1 , wherein the circuit system is configured such that the system further performs: denoising the noise in the obtained plurality of images, wherein the noise is modeled as a Poisson distribution; and The denoised noise in the obtained plurality of images is modeled based on a Gaussian distribution. 12 . The system of claim 11 , wherein each focus-related value of the plurality of focus-related values ​​is associated with each denoised image of the plurality of images modeled based on the Gaussian distribution.

13. A method for improving image quality, the method include: acquiring multiple images of an area of ​​a sample; Determined by phase diversity analysis: a plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values ​​is associated with each image of the plurality of images; Maximum likelihood estimation MLE of the multiple images; as well as A focus-corrected image of the area is generated based on the determined plurality of focus-related values ​​and the determined MLE. The method of claim 13 , wherein each focus-related value of the plurality of focus-related values ​​comprises a defocus value.

15. The method of claim 13, wherein each image of the plurality of images has a different associated focus-related value.

Images