Sensor metrology data integration

A non-transitory computer readable medium having instructions stored thereon, which, when executed by a processing device, cause the processing device perform operations comprising:

• identifying a plurality of sets of sensor data from sensors associated with processing chambers of wafer processing equipment, the sensor data associated with wafers processed via the processing chambers of the wafer processing equipment;
• identifying a plurality of sets of metrology data from metrology equipment, the metrology data associated with the wafers processed via the processing chambers of the wafer processing equipment;
• generating a plurality of sets of aggregated sensor-metrology data, each of the plurality of sets of aggregated sensor-metrology data comprising a respective set of sensor data and a respective set of metrology data;
• and
• training, based on the plurality of sets of aggregated sensor-metrology data, a machine learning model to provide a trained machine learning model, wherein the trained machine learning model is configured to generate one or more outputs for performing a corrective action associated with at least one of processing subsequent wafers via the processing chambers of the wafer processing equipment, determining predicted metrology data, or determining optimal design of one or more of manufacturing equipment or manufacturing processes.

A system comprising:

• a memory;
• and
• a processing device, coupled to the memory, to:
• identify a plurality of sets of sensor data from sensors associated with processing chambers of wafer processing equipment, the sensor data associated with wafers processed via the processing chambers of the wafer processing equipment;
• identify a plurality of sets of metrology data from metrology equipment, the metrology data associated with the wafers processed via the processing chambers of the wafer processing equipment;
• generate a plurality of sets of aggregated sensor-metrology data, each of the plurality of sets of aggregated sensor-metrology data comprising a respective set of sensor data and a respective set of metrology data;
• and
• train, based on the plurality of sets of aggregated sensor-metrology data, a machine learning model to provide a trained machine learning model, wherein the trained machine learning model is configured to generate one or more outputs for performing a corrective action associated with at least one of processing subsequent wafers via the processing chambers of the wafer processing equipment, determining predicted metrology data, or determining optimal design of one or more of manufacturing equipment or manufacturing processes.