Substrate defect troubleshooting analysis using machine learning

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.

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.