# US 2010/0166289 A1 · Feature-quantity extracting method, designed-circuit-pattern verifying method, and computer program product > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2010/0166289 A1 - Document ID: US20100166289A1 - Status: Published application - Kind: A1 - Application number: 12/646,677 - Filed: 2009-12-23 - Published: 2010-07-01 - Earliest claimed: 2008-12-27 - Inventors: Masaki SATAKE; Masafumi Asano; Satoshi Tanaka; Taiga Uno - Web page: https://patentgrep.com/patent/US20100166289A1 ## CPC - G03F 1/44: Testing or measuring features, e.g. grid patterns, focus monitors, sawtooth scales or notched scales - G03F 7/705: {Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions} - G03F: PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR; (phototypographic composing devices B41B; photosensitive materials or processes for photographic purposes G03C; electrophotography, sensitive layers or processes therefor G03G) ## Abstract Feature-quantity extraction parameters used by feature-quantity extraction functions for calculating feature quantities used as explanatory variables of a resist model for predicting a resist image are set. The feature-quantity extraction functions, for which the feature-quantity extraction parameters are set, are caused to act on optical images of a pattern of a photomask to calculate feature quantities from the optical images. ## Claims ### Claim 1 A feature-quantity extracting method comprising: setting feature-quantity extraction parameters used by feature-quantity extraction functions for calculating feature quantities used as explanatory variables of a resist model for predicting a resist image; and causing the feature-quantity extraction functions, for which the feature-quantity extraction parameters are set, to act on optical images of a pattern of a photomask and calculating feature quantities from the optical images. ### Claim 2 (depends on 1) The feature-quantity extracting method according to claim 1, wherein the setting further includes: acquiring one or more feature-quantity extraction parameters of a resist model used in a past; calculating a range in which the acquired feature-quantity extraction parameters are fit; and setting feature-quantity extraction parameter used by the feature-quantity extraction functions from the calculated range. ### Claim 3 (depends on 1) The feature-quantity extracting method according to claim 1, wherein the resist model is a constant threshold model (CTM). ### Claim 4 (depends on 1) The feature-quantity extracting method according to claim 1, wherein the pattern of the photomask is test patterns prepared in advance including test patterns used for measuring a residual between a predicted dimension of the resist image by the resist model and an actual dimension of a resist pattern formed on a wafer, and the feature-quantity extracting method further comprises selecting, based on feature quantities calculated from respective optical images of the test patterns prepared in advance and priority set in advance for each of the test patterns according to measurement accuracy of the actual dimension, a test pattern used for measuring the residual out of the test patterns prepared in advance. ### Claim 5 (depends on 4) The feature-quantity extracting method according to claim 4, wherein the priority is set in advance according to measurement accuracy of a resist dimension. ### Claim 6 (depends on 1) The feature-quantity extracting method according to claim 1, wherein the pattern of the photomask is a circuit pattern, and the feature-quantity extracting method further comprises: generating, according to a distribution of the feature quantities calculated from the optical images of the circuit pattern, test patterns which have lines and spaces adjusted in a range in which dimensions of lines and spaces are not resolved on a wafer; and creating a resist model based on a residual between a predicted dimension of a resist image of the generated test patterns and an actual dimension of a resist pattern formed on a wafer. ### Claim 7 (depends on 6) The feature-quantity extracting method according to claim 6, wherein line width of the respective lines included in the test patterns decreases toward at least one direction among lining-up directions of the lines of the line and space. ### Claim 8 (depends on 6) The feature-quantity extracting method according to claim 6, wherein the range in which the dimensions of the lines and the spaces are not resolved on the wafer is a range in which a relation among a pitch P of the test patterns, wavelength 2 of an exposure apparatus, a number of lens apertures Na, and a coherence factor σ of illumination satisfies a condition P<λ/{NA(1+σ)}. ### Claim 9 (depends on 8) The feature-quantity extracting method according to claim 8, wherein the actual dimension of the resist pattern formed on the wafer is measured by an optical measuring device. ### Claim 10 (depends on 4) The feature-quantity extracting method according to claim 4, further comprising causing a nonlinear function to act on the feature quantities calculated from the optical images of the selected respective test patterns to map the feature quantities to a higher-dimensional space and causing, based on a residual between a predicted dimension of a resist image of the test patterns and an actual dimension of a resist pattern formed on a wafer, a relation between the resist image of the test patterns and the feature quantities to linearly regress in the higher-dimensional space to create a resist model. ### Claim 11 (depends on 10) The feature-quantity extracting method according to claim 10, wherein the nonlinear function is a kernel function including a Gaussian kernel, a sigmoid kernel or a logistic kernel. ### Claim 12 (depends on 11) The feature-quantity extracting method according to claim 11, wherein the linear regression is liner regression performed by using a support vector machine or a neural network. ### Claim 13 A designed-circuit pattern verifying method comprising: setting feature-quantity extraction parameters used by feature-quantity extraction functions for calculating feature quantities used as explanatory variables of a resist model for predicting a resist image; causing the feature-quantity extraction functions, for which the feature-quantity extraction parameters are set, to act on optical images of a circuit pattern created by a designer and calculating feature quantities at a plurality of points of the circuit pattern from the optical images; comparing a distribution of the calculated feature quantities and a distribution of feature quantities of test patterns and extracting a point of the circuit pattern deviating from a range of the feature quantity distribution of the test patterns; and displaying the extracted point of the circuit pattern on a display device. ### Claim 14 (depends on 13) The designed-circuit-pattern verifying method according to claim 13, wherein the extracting includes determining, based on a Euclidian distance between the calculated feature quantities of the circuit pattern and the feature quantities forming the feature quantity distribution of the test patterns, whether points where the feature quantities of the circuit pattern are calculated deviate from a range of the feature quantity distribution of the test pattern. ### Claim 15 (depends on 14) The designed-circuit-pattern verifying method according to claim 14, wherein the determining further includes: calculating, according to the Euclidian distance, a score for each of the points where the feature quantities of the circuit pattern are calculated; and determining, based on the calculated score, whether the points where the feature quantities of the circuit pattern are calculated deviate from the range of the feature quantity distribution of the test patterns. ### Claim 16 A computer program product executable by a computer, the computer program product causing the computer to execute: setting feature-quantity extraction parameters used by feature-quantity extraction functions for calculating feature quantities used as explanatory variables of a resist model for predicting a resist image; and causing the feature-quantity extraction functions, for which the feature-quantity extraction parameters are set, to act on optical images of a pattern of a photomask and calculating feature quantities from the optical images. ### Claim 17 (depends on 16) The computer program product according to claim 16, wherein the setting further includes; acquiring one or more feature-quantity extraction parameters of a resist model used in a past; calculating a range in which the acquired feature-quantity extraction parameters are fit; and setting feature-quantity extraction parameter used by the feature-quantity extraction functions from the calculated range. ### Claim 18 (depends on 16) The computer program product according to claim 16, wherein the resist model is a constant threshold model (CTM).