# US 2025/0166166 A1 · Systems and methods for defect location binning in charged-particle systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0166166 A1 - Document ID: US20250166166A1 - Status: Published application - Kind: A1 - Application number: 18/838,459 - Filed: 2023-01-19 - Published: 2025-05-22 - Earliest claimed: 2022-02-17 - Assignees: ASML Netherlands B.V. - Applicants: ASML Netherlands B.V. - Inventors: Shengcheng JIN; Yunbo GUO; Chen ZHANG - Web page: https://patentgrep.com/patent/US20250166166A1 ## CPC - G06T 7/001: {using an image reference approach} - G06T 7/0006: {using a design-rule based approach} - G06T 2207/10061: from scanning electron microscope - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30141: Printed circuit board [PCB] - G06T 2207/30148: Semiconductor; IC; Wafer - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Apparatuses, systems, and methods for providing beams for defect detection and defect location binning associated with a sample of charged particle beam systems. A method of image analysis may include obtaining an image of a sample, identifying a feature captured in the image of the sample, generating a template image from a design layout of the identified feature, comparing 5 the image of the sample with the template image, and processing the image based on the comparison. In some embodiments, a method of image analysis may include obtaining an image of a sample, identifying a feature captured in the obtained image of the sample, mapping the obtained image to a template image generated from a design layout of the identified feature, and analyzing the image based on the mapping. ## Claims ### Claim 1 A non-transitory computer readable medium that stores a set of instructions that is executable by one or more processors of a computing device to cause the computing device to perform a method for image analysis, the method comprising: obtaining an image of a sample; identifying a feature captured in the image of the sample; generating a template image from a design layout of the identified feature; comparing the image of the sample with the template image; and processing the image based on the comparison. ### Claim 2 (depends on 1) The non-transitory computer readable medium of claim 1, wherein generating the template image comprises: generating a location template in the design layout, the location template representing a portion of the obtained image; and generating the template image based on the location template. ### Claim 3 (depends on 1) The non-transitory computer readable medium of claim 1, wherein generating the template image further comprises: training a machine learning model by mapping information associated with the design layout to information associated with a reference image; and generating a simulated template image using the trained machine learning model. ### Claim 4 (depends on 2) The non-transitory computer readable medium of claim 2, wherein generating the location template comprises: identifying a region of the design layout corresponding to the portion of the obtained image; and grouping a plurality of features in the identified region into one or more repeating patterns at least based on a distance between adjacent features of the plurality of features of the plurality of features. ### Claim 5 (depends on 4) The non-transitory computer readable medium of claim 4, wherein generating the location template further comprises determining boundary coordinates of the one or more repeating patterns. ### Claim 6 (depends on 5) The non-transitory computer readable medium of claim 5, wherein the boundary coordinates of the one or more repeating patterns are configurable. ### Claim 7 (depends on 4) The non-transitory computer readable medium of claim 4, wherein the set of instructions that is executable by the one or more processors of the computing device causes the computing device to further perform indexing a location of the plurality of features in the one or more repeating patterns. ### Claim 8 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the set of instructions that is executable by the one or more processors of the computing device causes the computing device to further perform storing information associated with boundary coordinates of the one or more repeating patterns and information associated with the indexed location of the plurality of features in the one or more repeating patterns. ### Claim 9 (depends on 1) The non-transitory computer readable medium of claim 1, wherein comparing the image of the sample with the template image comprises: aligning the image of the sample with the template image; and identifying a set of locations of any one or more defects in the obtained image of the sample. ### Claim 10 (depends on 9) The non-transitory computer readable medium of claim 9, wherein the any one or more defects indicate any one of necking, bridging, edge placement error, hole, or a broken line. ### Claim 11 (depends on 9) The non-transitory computer readable medium of claim 9, wherein processing the image based on the comparison further comprises binning the set of locations of the any one or more defects based on a location of one or more corresponding features on the template image. ### Claim 12 (depends on 2) The non-transitory computer readable medium of claim 2, wherein the template image comprises a simulated scanning electron microscopy (SEM) image of a region of the location template corresponding to the obtained image. ### Claim 13 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the simulated SEM image is substantially distortion-free. ### Claim 14 (depends on 3) The non-transitory computer readable medium of claim 3, wherein the reference image comprises an inspected scanning electron microscopy (SEM) image. ### Claim 15 A system for image analysis, comprising: a controller including circuitry configured to cause the system to perform: obtaining an image of a sample; identifying a feature captured in the image of the sample; generating a template image from a design layout of the identified feature; comparing the image of the sample with the template image; and processing the image based on the comparison. ### Claim 16 (depends on 15) The system of claim 15, wherein generating the template image comprises: generating a location template in the design layout, the location template representing a portion of the obtained image; and generating the template image based on the location template. ### Claim 17 (depends on 15) The system of claim 15, wherein generating the template image further comprises: training a machine learning model by mapping information associated with the design layout to information associated with a reference image; and generating a simulated template image using the trained machine learning model. ### Claim 18 (depends on 16) The system of claim 16, wherein generating the location template comprises: identifying a region of the design layout corresponding to the portion of the obtained image; and grouping a plurality of features in the identified region into one or more repeating patterns at least based on a distance between adjacent features of the plurality of features. ### Claim 19 (depends on 18) The system of claim 18, wherein generating the location template further comprises determining boundary coordinates of the one or more repeating patterns. ### Claim 20 (depends on 19) The system of claim 19, wherein the boundary coordinates of the one or more repeating patterns are configurable.