# US 11,897,078 B2 · Polishing apparatus, information processing system, polishing method, and computer-readable storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,078 B2 - Document ID: US11897078B2 - Status: Granted - Kind: B2 - Application number: 17/205,344 - Filed: 2021-03-18 - Published: 2021-09-23 - Granted: 2024-02-13 - Earliest claimed: 2020-03-19 - Assignees: Ebara Corporation - Applicants: Ebara Corporation - Inventors: Akira Nakamura; Tsuneo Torikoshi; Yuta Suzuki; Hisanori Matsuo; Takahito Kagoshima - Web page: https://patentgrep.com/patent/US11897078B2 ## CPC - B24B 37/005: Control means for lapping machines or devices - B24B 37/10: for single side lapping - B24B 49/02: according to the instantaneous size and required size of the workpiece acted upon, the measuring or gauging being continuous or intermittent (B24B49/12 takes precedence) - B24B 49/14: taking regard of the temperature during grinding - B24B: MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING (grinding of gear teeth B23F, of screw-threads B23G1/36; by electro-erosion B23H; abrasive or related blasting B24C; tools for grinding, buffing or sharpening B24D; polishing compositions C09G1/00; abrasives C09K3/14; electrolytic etching or polishing C25F3/00; grinding arrangements for use on assembled railway tracks E01B31/17); DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS ## Abstract A polishing apparatus that is capable of referring to a storage in which a machine learning model is stored, the machine learning model being learned using learning data in which a feature amount of a signal regarding a frictional force between a polishing member and a substrate in polishing or a feature amount of a temperature of the polishing member or the substrate in polishing is input, and data regarding a film thickness of the polished substrate or a parameter related to yield of a product included in the polished substrate is output, the device including: a polishing table provided with the polishing member and configured to be rotatable; a polishing head facing the polishing table and configured to be rotatable, wherein the substrate is attachable to a surface facing the polishing table; a control unit configured to perform control to polish the substrate by pressing the substrate against the polishing member while rotating the polishing table and the polishing head having the substrate attached thereto; and a processor configured to generate the feature amount from the signal regarding the frictional force between the polishing member and the substrate in polishing or the feature amount from the temperature of the polishing member or a target substrate in polishing, and output the data regarding the film thickness of the polished substrate or any of the parameters related to the yield of the product included in the polished substrate, as an estimated value, by inputting the generated feature amount to the learned machine learning model. ## Claims ### Claim 1 A polishing apparatus that is capable of referring to a storage in which a machine learning model is stored, the machine learning model being learned using learning data in which a feature amount of a signal regarding a frictional force between a polishing member and a substrate in polishing or a feature amount of a temperature of the polishing member or the substrate in polishing is input, and data regarding a film thickness of the polished substrate or a parameter related to yield of a product included in the polished substrate is output, the apparatus comprising: a polishing table provided with the polishing member and configured to be rotatable; a polishing head facing the polishing table and configured to be rotatable, wherein the substrate is attachable to a surface facing the polishing table; a control unit configured to perform control to polish the substrate by pressing the substrate against the polishing member while rotating the polishing table and the polishing head having the substrate attached thereto; a processor configured to generate the feature amount from the signal regarding the frictional force between the polishing member and the substrate in polishing or the feature amount from the temperature of the polishing member or a target substrate in polishing, and output the data regarding the film thickness of the polished substrate or any of the parameters related to the yield of the product included in the polished substrate, as an estimated value, by inputting the generated feature amount to the learned machine learning model; and a film thickness measuring device configured to measure the film thickness of the substrate, wherein the processor controls the film thickness measuring device to measure a film thickness of the polished target substrate when the output estimated value satisfies a predetermined polishing deterioration condition, and controls the film thickness measuring device not to measure the film thickness of the polished target substrate when the output estimated value does not satisfy the predetermined polishing deterioration condition. ### Claim 2 (depends on 1) The polishing apparatus according to claim 1, wherein the processor stops processing for a subsequent substrate when the output estimated value satisfies a predetermined polishing deterioration condition. ### Claim 3 (depends on 1) The polishing apparatus according to claim 1, wherein the processor outputs a maintenance timing by using a tendency of the estimated value output for the polished substrate at a plurality of different times. ### Claim 4 (depends on 1) The polishing apparatus according to claim 1, wherein the processor performs control to issue a warning for urging a maintenance, when the output estimated value satisfies a predetermined polishing deterioration condition. ### Claim 5 (depends on 1) The polishing apparatus according to claim 1, wherein the processor adjusts a polishing condition for a subsequent substrate in accordance with the output estimated value so that data regarding a desired film thickness of a polished substrate or a parameter related to desired yield of a product included in the polished substrate is obtained. ### Claim 6 (depends on 1) The polishing apparatus according to claim 1, wherein the processor learns the machine learning model again using the feature amount during an operation of the polishing apparatus. ### Claim 7 A polishing method for polishing a substrate by a polishing apparatus that is capable of referring to a storage in which a machine learning model is stored, the machine learning model being learned using learning data in which a feature amount of a signal regarding a frictional force between a polishing member and a substrate in polishing or a feature amount of a temperature of the polishing member or the substrate in polishing is input, and data regarding a film thickness of the polished substrate or a parameter related to yield of a product included in the polished substrate is output, the method comprising: polishing the substrate by pressing the substrate against the polishing member while rotating the polishing table and the polishing head having the substrate attached thereto; generating the feature amount by measuring the signal regarding the frictional force between the polishing member and the substrate in polishing or the temperature of the polishing member or a target substrate in polishing; inputting the generated feature amount to the learned machine learning model; outputting the data regarding the film thickness of the polished substrate or any of the parameters related to the yield of the product included in the polished substrate, as an estimated value; and determining whether or not the output estimated value satisfies a predetermined polishing deterioration condition; and causing a film thickness measuring device to measure a film thickness of the polished target substrate when the output estimated value satisfies the predetermined polishing deterioration condition, and not to measure the film thickness of the polished target substrate when the output estimated value does not satisfy the predetermined polishing deterioration condition. ### Claim 8 (depends on 7) The polishing method according to claim 7, further comprising: determining whether or not the output estimated value satisfies a predetermined polishing deterioration condition; and stopping processing for a subsequent substrate when the output estimated value satisfies the predetermined polishing deterioration condition. ### Claim 9 (depends on 7) The polishing method according to claim 7, further comprising outputting a maintenance timing by using a tendency of the estimated value output for the polished substrate at a plurality of different times. ### Claim 10 (depends on 7) The polishing method according to claim 7, further comprising: determining whether or not the output estimated value satisfies a predetermined polishing deterioration condition; and issuing a warning for urging a maintenance when the output estimated value satisfies the predetermined polishing deterioration condition. ### Claim 11 (depends on 7) The polishing method according to claim 7, further comprising adjusting a polishing condition for a subsequent substrate in accordance with the output estimated value so that data regarding a desired film thickness of a polished substrate or a parameter related to desired yield of a product included in the polished substrate is obtained. ### Claim 12 (depends on 7) The polishing method according to claim 7, further comprising learning the machine learning model again using the feature amount during an operation of the polishing apparatus.