# US 2022/0035256 A1 · Determining hot spot ranking based on wafer measurement > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0035256 A1 - Document ID: US20220035256A1 - Status: Published application - Kind: A1 - Application number: 17/276,533 - Filed: 2019-09-20 - Published: 2022-02-03 - Earliest claimed: 2018-09-28 - Assignees: ASML Netherlands B.V. - Applicants: Asml Netherlands B.V. - Inventors: Youping ZHANG; Weixuan HU; Fei YAN; Wei PENG; Vivek Kumar JAIN - Web page: https://patentgrep.com/patent/US20220035256A1 ## CPC - G03F 7/705: {Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions} - G03F 7/70525: {Controlling normal operating mode, e.g. matching different apparatus, remote control or prediction of failure} - G03F 7/70641: {Focus} - G03F 7/7065: {Defects, e.g. optical inspection of patterned layer for defects} - G03F 7/7085: {Detection arrangement, e.g. detectors of apparatus alignment possibly mounted on wafers, exposure dose, photo-cleaning flux, stray light, thermal load} - 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 A method of hot spot ranking for a patterning process. The method includes obtaining (i) a set of hot spots of a patterning process, (ii) measured values of one or more parameters of the patterning process corresponding to the set of hot spots, and (ii) simulated values of the one or more parameters of the patterning process corresponding to the set of hot spots; determining a measurement feedback based on the measured values and the simulated values of the one or more parameters of the patterning process; and determining, via simulation of a process model of the patterning process, a ranking of a hot spot within the set of hot spots based on the measurement feedback. ## Claims ### Claim 1 A comprising: obtaining (i) a set of hot spots of a patterning process, (ii) measured values of one or more parameters of the patterning process corresponding to the set of hot spots, and (iii) simulated values of the one or more parameters of the patterning process corresponding to the set of hot spots; determining, via a hardware computer system, a measurement feedback based on the measured values and the simulated values of the one or more parameters of the patterning process; and determining, via computer simulation using a process model of the patterning process, a ranking of a hot spot within the set of hot spots based on the measurement feedback. ### Claim 2 (depends on 1) The method of claim 1, wherein the measurement feedback is a bias function determined as a difference between a first function representing the measured values and a second function representing the simulated values. ### Claim 3 (depends on 2) The method of claim 2, wherein the determining the measurement feedback comprises: categorizing hot spots within the set of hot spots in one or more categories; and determining the bias function for a particular category of the hot spot based on the measured values of the one or more parameters of the patterning process and the simulated values of the one or more parameters of the patterning process, and/or further comprising determining the bias function, the determining the bias function comprising: determining the first function as one fitting the measured values; determining the second function as one corresponding to the process model of the patterning process used to generate the simulated values; and determining the difference between the first function and the second function. ### Claim 4 (depends on 2) The method of claim 2, wherein the first function and/or the second function is a mathematical model and/or a look up table. ### Claim 5 (depends on 1) The method of claim 1, wherein the measurement feedback is a maximum value of a difference between the measured values and the simulated values. ### Claim 6 (depends on 1) The method of claim 1, wherein the measurement feedback is an average value of a difference between the measured values and the simulated values corresponding to a category of the hot spot within the set of the hot spots. ### Claim 7 (depends on 1) The method of claim 1, wherein the determining the ranking of the hot spot comprises: determining a category of one or more hot spots of the set of hot spots; applying a bias function corresponding to the category to generate a biased version of the one or more hot spots; and determining, via simulation of a ranking algorithm of the process model of the patterning process, the ranking of the biased hot spot. ### Claim 8 (depends on 7) The method of claim 7, wherein the ranking algorithm includes determining a defect metric corresponding to each of hot spot within the set of hot spots, wherein: the defect metric is evaluated within a focus uncertainty range, and/or the defect metric is a weighted sum of a difference between a defect size and a threshold defect size at different focus values within a focus uncertainty range. ### Claim 9 (depends on 1) The method of claim 1, further comprising: guiding an inspection tool to collect measurement data on a printed substrate according to the ranking of the hot spots; and determining defects on the printed substrate based on the measurement data. ### Claim 10 (depends on 1) The method of claim 1, wherein the determining the measurement feedback is an iterative process, an iteration comprising: obtaining defect data corresponding to the set of hot spots; determining a correlation between the defect data and hot spot ranking; evaluating a level of the correlation; and responsive to the level of correlation breaching a threshold, determining the measurement feedback. ### Claim 11 (depends on 3) The method of claim 3, wherein the category is based on a pattern type and/or on values of one or more parameters of the patterning process. ### Claim 12 (depends on 11) The method of claim 11, wherein the category is based on a pattern type and the pattern type is determined based on geometric shapes of the features within the hot spot. ### Claim 13 (depends on 11) The method of claim 11, wherein the category is based on values of one or more parameters of the patterning process and the one or more parameters of the patterning process include a critical dimension, a dose, and/or a focus. ### Claim 14 A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: obtain (i) a set of hot spots of a patterning process, (ii) measured values of one or more parameters of the patterning process corresponding to the set of hot spots, and (iii) simulated values of the one or more parameters of the patterning process corresponding to the set of hot spots; determine a measurement feedback based on the measured values and the simulated values of the one or more parameters of the patterning process; and determine, via computer simulation using a process model of the patterning process, a ranking of a hot spot within the set of hot spots based on the measurement feedback. ### Claim 15 (depends on 14) The computer program product of claim 14, wherein the measurement feedback is a bias function determined as a difference between a first function representing the measured values and a second function representing the simulated values. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the instructions configured to cause the computer system to determine the measurement feedback are further configured to cause the computer system to: categorize hot spots within the set of hot spots in one or more categories; and determine the bias function for a particular category of the hot spot based on the measured values of the one or more parameters of the patterning process and the simulated values of the one or more parameters of the patterning process. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the instructions are further configured to cause the computer system to determine the bias function, the determination of the bias function comprising: determination of the first function as one fitting the measured values; determination of the second function as one corresponding to the process model of the patterning process used to generate the simulated values; and determination of the difference between the first function and the second function. ### Claim 18 (depends on 14) The computer program product of claim 14, wherein the measurement feedback is a maximum value of a difference between the measured values and the simulated values or an average value of a difference between the measured values and the simulated values corresponding to a category of the hot spot within the set of the hot spots. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the instructions configured to cause the computer system to determine the ranking of the hot spot are further configured to cause the computer system to: determine a category of one or more hot spots of the set of hot spots; apply a bias function corresponding to the category to generate a biased version of the one or more hot spots; and determine, via simulation of a ranking algorithm of the process model of the patterning process, the ranking of the biased hot spot. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the instructions configured to cause the computer system to determine the measurement feedback are further configured to cause the computer system to do so in an iterative process, an iteration thereof comprising: obtaining of defect data corresponding to the set of hot spots; determination of a correlation between the defect data and hot spot ranking; evaluation of a level of the correlation; and responsive to the level of correlation breaching a threshold, determination of the measurement feedback.