# US 2026/0093575 A1 · Substrate defect troubleshooting analysis using machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0093575 A1 - Document ID: US20260093575A1 - Status: Published application - Kind: A1 - Application number: 18/904,758 - Filed: 2024-10-02 - Published: 2026-04-02 - Earliest claimed: 2024-10-02 - Assignees: Applied Materials, Inc. - Applicants: Applied Materials, Inc. - Inventors: Jeffrey Ryan Collins; Hexuan Wang; Abhinav Kumar; Bhaskar Kumar; Qinyi Chen; Martin Jay Seamons; Ganesh Balasubramanian - Web page: https://patentgrep.com/patent/US20260093575A1 ## CPC - G05B 23/0275: {Fault isolation and identification, e.g. classify fault; estimate cause or root of failure} - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F 11/079: {Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36)} - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method includes obtaining, by a processing device, defect data for a substrate processed in a substrate processing system. The method further includes obtaining, by the processing device, context data associated with the substrate. The method further includes determining a troubleshooting guide associated with the defect data. The troubleshooting guide includes a sequence of troubleshooting operations, each associated with one or more probably root causes for the defect data. The method further includes determining a subset of context data based on the troubleshooting guide. The method further includes processing the defect data and the subset of context data using one or more trained machine learning models that output a predicted corrective action associated with a troubleshooting operation in the sequence of troubleshooting operations. The method further includes initiating the corrective action. ## Claims ### Claim 1 A method, comprising: obtaining, by a processing device, defect data for a substrate processed in a substrate processing system; obtaining, by the processing device, context data associated with the substrate; determining a troubleshooting guide associated with the defect data, the troubleshooting guide comprising a sequence of troubleshooting operations, each associated with one or more probable root causes for the defect data; determining a subset of context data based on the troubleshooting guide; processing the defect data and the subset of context data using one or more trained machine learning models that output a predicted corrective action associated with a troubleshooting operation in the sequence of troubleshooting operations; and initiating the corrective action. ### Claim 2 (depends on 1) The method of claim 1, further comprising: inputting the defect data and the subset of the context data into a first trained machine learning model of the one or more trained machine learning models, wherein the first trained machine learning model is trained to output a predicted root cause associated with the defect data and the context data; and inputting at least the predicted root cause into a second trained machine learning model of the one or more trained machine learning models, wherein the second trained machine learning model is trained to output the predicted corrective action. ### Claim 3 (depends on 2) The method of claim 2, wherein the second trained machine learning model is trained with training input data comprising historical defect data and historical context data, and wherein the second trained machine learning model is trained with training output data comprising historical predicted root cause data. ### Claim 4 (depends on 1) The method of claim 1, further comprising: receiving a selection of the troubleshooting guide from a plurality of troubleshooting guides. ### Claim 5 (depends on 1) The method of claim 1, wherein the troubleshooting guide comprises a plurality of corrective actions associated with a corresponding plurality of substrate defects. ### Claim 6 (depends on 1) The method of claim 1, further comprising: determining a project comprising historical defect data for one or more prior substrates that approximately matches the defect data, historical context data for the one or more prior substrates, root causes of the historical defect data, and actions performed to identify the root causes of the historical defect data. ### Claim 7 (depends on 1) The method of claim 1, further comprising: determining whether the corrective action resolved a root cause of the defect data; and updating the troubleshooting guide based whether the corrective action resolved a root cause of the defect data. ### Claim 8 (depends on 1) The method of claim 1, further comprising: generating a report indicative of at least one of the defect data, the context data, the troubleshooting guide, or the corrective action. ### Claim 9 (depends on 1) The method of claim 1, further comprising: prompting a user to provide feedback based on output of the one or more trained machine learning models; determining, based on the feedback, whether to retrain at least one of the one or more trained machine learning models; and retraining at least one of the trained machine learning models. ### Claim 10 (depends on 1) The method of claim 1, wherein the defect data comprises one or more of: image features of the substrate; defect composition data; defect spatial signature data; or defect classification data generated by a third trained machine learning model. ### Claim 11 (depends on 1) The method of claim 1, wherein the context data comprises one or more of: process chamber data in association with the substrate; hardware component data in association with the process chamber; process recipe data; or chamber chemistry data. ### Claim 12 (depends on 1) The method of claim 1, wherein the corrective action comprises at least one of: one or more seasoning operations of a process chamber; one or more cleaning operations of the process chamber; replacement of a component of the process chamber; or one or more maintenance operations for the process chamber. ### Claim 13 A non-transitory machine-readable storage medium storing instructions which, when executed by a processing device, cause the processing device to perform operations comprising: obtaining defect data for a substrate processed in a substrate processing system; obtaining context data associated with the substrate; determining a troubleshooting guide associated with the defect data, the troubleshooting guide comprising a sequence of troubleshooting operations, each associated with one or more probable root causes for the defect data; determining a subset of context data based on the troubleshooting guide; processing the defect data and the subset of context data using one or more trained machine learning models that output a predicted corrective action associated with a troubleshooting operation in the sequence of troubleshooting operations; and initiating the corrective action. ### Claim 14 (depends on 13) The non-transitory machine-readable storage medium of claim 13, wherein the processing device is to perform operations further comprising: inputting the defect data and the subset of the context data into a first trained machine learning model of the one or more trained machine learning models, wherein the first trained machine learning model is trained to output a predicted root cause associated with the defect data and the context data; and inputting at least the predicted root cause into a second trained machine learning model of the one or more trained machine learning models, wherein the second trained machine learning model is trained to output the predicted corrective action. ### Claim 15 (depends on 13) The non-transitory machine-readable storage medium of claim 13, wherein the processing device is to perform operations further comprising: receiving a selection of the troubleshooting guide from a plurality of troubleshooting guides. ### Claim 16 (depends on 13) The non-transitory machine-readable storage medium of claim 13, wherein the processing device is to perform operations further comprising: determining a project comprising historical defect data for one or more prior substrates that approximately matches the defect data, historical context data for the one or more prior substrates, root causes of the historical defect data, and actions performed to identify the root causes of the historical defect data. ### Claim 17 A system, comprising memory and a processing device operatively coupled with the memory, wherein the processing device is configured to: obtain defect data for a substrate processed in a substrate processing system; obtain context data associated with the substrate; determine a troubleshooting guide associated with the defect data, the troubleshooting guide comprising a sequence of troubleshooting operations, each associated with one or more probable root causes for the defect data; determine a subset of context data based on the troubleshooting guide; process the defect data and the subset of context data using one or more trained machine learning models that output a predicted corrective action associated with a troubleshooting operation in the sequence of troubleshooting operations; and initiate the corrective action. ### Claim 18 (depends on 17) The system of claim 17, wherein the processing device is further configured to: input the defect data and the subset of the context data into a first trained machine learning model of the one or more trained machine learning models, wherein the first trained machine learning model is trained to output a predicted root cause associated with the defect data and the context data; and input at least the predicted root cause into a second trained machine learning model of the one or more trained machine learning models, wherein the second trained machine learning model is trained to output the predicted corrective action. ### Claim 19 (depends on 17) The system of claim 17, wherein the processing device is further configured to: receive a selection of the troubleshooting guide from a plurality of troubleshooting guides. ### Claim 20 (depends on 17) The system of claim 17, wherein the processing device is further configured to: determine a project comprising historical defect data for one or more prior substrates that approximately matches the defect data, historical context data for the one or more prior substrates, root causes of the historical defect data, and actions performed to identify the root causes of the historical defect data.