# US 2021/0241436 A1 · Image Generation System > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241436 A1 - Document ID: US20210241436A1 - Status: Published application - Kind: A1 - Application number: 17/155,680 - Filed: 2021-01-22 - Published: 2021-08-05 - Earliest claimed: 2020-02-05 - Assignees: Hitachi, Ltd. - Applicants: Hitachi, Ltd. - Inventors: Takeshi OHMORI; Yutaka OKUYAMA - Web page: https://patentgrep.com/patent/US20210241436A1 ## CPC - G06T 7/001: {using an image reference approach} - G01B 11/02: for measuring length, width or thickness (G01B11/08 takes precedence) - G01B 15/04: for measuring contours or curvatures - G06F 16/51: Indexing; Data structures therefor; Storage structures - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06T 7/60: Analysis of geometric attributes - G06T 11/00: Two-dimensional [2D] image generation - G01B 2210/56: Measuring geometric parameters of semiconductor structures, e.g. profile, critical dimensions or trench depth - G01N 23/2251: using incident electron beams, e.g. scanning electron microscopy [SEM] - G06N 3/045: Combinations of networks - G06N 3/047: Probabilistic or stochastic networks - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06T 2207/20081: Training; Learning - G06T 2207/30148: Semiconductor; IC; Wafer - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) ## Abstract Provided is a system that constructs a learning database for a learning model in a short period of time. The system generates an image in which a structure image is similar to an actual image by image processing of the structure image. One or more processors acquire a first structure image and a second structure image different from the first structure image. The one or more processors create a plurality of intermediate structure images indicating an intermediate structure between the first structure image and the second structure image. The one or more processors generate an image by making each of the plurality of intermediate structure images to be similar to an actual image by image processing of each of the plurality of intermediate structure images. ## Claims ### Claim 1 A system that generates an image in which a structure image is similar to an actual image by image processing of the structure image, the system comprising: one or more storage apparatuses; and one or more processors that operate in accordance with a program stored in the one or more storage apparatuses, wherein the one or more processors acquire a first structure image and a second structure image different from the first structure image, create a plurality of intermediate structure images indicating an intermediate structure between the first structure image and the second structure image, and generate an image by making each of the plurality of intermediate structure images to be similar to an actual image by the image processing of each of the plurality of intermediate structure images. ### Claim 2 (depends on 1) The system according to claim 1, wherein the one or more processors generate an image in which each of the plurality of intermediate structure images is similar to the actual image based on a reference image. ### Claim 3 (depends on 1) The system according to claim 1, wherein one of the first structure image and the second structure image is a structure image corresponding to a goal shape. ### Claim 4 (depends on 1) The system according to claim 1, wherein one of the first structure image and the second structure image is a structure image corresponding to a shape closest to a goal shape in an acquired actual image. ### Claim 5 (depends on 1) The system according to claim 1, wherein the first structure image is a structure image corresponding to a shape closest to a goal shape in an acquired actual image, and the second structure image is a structure image corresponding to the goal shape. ### Claim 6 (depends on 1) The system according to claim 1, wherein the one or more processors generate the plurality of intermediate structure images by changing a structure image from the first structure image to the second structure image according to a specified correspondence. ### Claim 7 (depends on 1) The system according to claim 1, wherein the one or more processors construct a database including the plurality of intermediate structure images and an image in which each of the plurality of intermediate structure images is similar to the actual image, construct a structure acquisition model that outputs a structure image from an input actual image by using the database, and generate a new structure image from a new actual image using the structure acquisition model. ### Claim 8 (depends on 7) The system according to claim 7, wherein the one or more processors measure a predetermined feature dimension in the new structure image using a dimension extraction model that extracts the feature dimension. ### Claim 9 (depends on 8) The system according to claim 8, wherein the one or more processors construct a learning model that outputs a structure image corresponding to an input apparatus condition using a database including the measured feature dimension, the new structure image, the new actual image, and an apparatus condition for processing a sample from which the new actual image is acquired, and estimate an apparatus condition for implementing a goal shape using the learning model. ### Claim 10 (depends on 2) The system according to claim 2, wherein one of the first structure image and the second structure image is a structure image corresponding to a goal shape. ### Claim 11 (depends on 2) The system according to claim 2, wherein one of the first structure image and the second structure image is a structure image corresponding to a shape closest to a goal shape in an acquired actual image. ### Claim 12 (depends on 2) The system according to claim 2, wherein the first structure image is a structure image corresponding to a shape closest to a goal shape in an acquired actual image, and the second structure image is a structure image corresponding to the goal shape. ### Claim 13 (depends on 2) The system according to claim 2, wherein the one or more processors generate the plurality of intermediate structure images by changing a structure image from the first structure image to the second structure image according to a specified correspondence. ### Claim 14 (depends on 2) The system according to claim 2, wherein the one or more processors construct a database including the plurality of intermediate structure images and an image in which each of the plurality of intermediate structure images is similar to the actual image, construct a structure acquisition model that outputs a structure image from an input actual image by using the database, and generate a new structure image from a new actual image using the structure acquisition model. ### Claim 15 (depends on 14) The system according to claim 14, wherein the one or more processors measure a predetermined feature dimension in the new structure image using a dimension extraction model that extracts the feature dimension. ### Claim 16 (depends on 15) The system according to claim 15, wherein the one or more processors construct a learning model that outputs a structure image corresponding to an input apparatus condition using a database including the measured feature dimension, the new structure image, the new actual image, and an apparatus condition for processing a sample from which the new actual image is acquired, and estimate an apparatus condition for implementing a goal shape using the learning model. ### Claim 17 A method for a system to generate an image in which a structure image is similar to an actual image by image processing of the structure image, the method comprising: acquiring, by the system, a first structure image and a second structure image different from the first structure image, creating, by the system, a plurality of intermediate structure images indicating an intermediate structure between the first structure image and the second structure image, and generating, by the system, an image by making each of the plurality of intermediate structure images to be similar to an actual image by image processing of each of the plurality of intermediate structure images.