Machine learning platform for substrate processing

A method of operating a substrate processing facility, the method comprising:

• identifying current data associated with current substrate lots of the substrate processing facility;
• identifying current features associated with the current data;
• providing the current features to a trained machine learning model that was trained based on historical features, wherein the current features are associated with a first substrate yield, the historical features being generated based on historical data for historical substrate lots associated with historical sets of substrates processed by substrate processing tools in the substrate processing facility, wherein each historical substrate lot is a corresponding set of historical substrates;
• obtaining, from the trained machine learning model, an output;
• and
• causing, based on the output of the trained machine learning model, performance of one or more corrective actions associated with processing of corresponding sets of current substrates of the current substrate lots in the substrate processing facility to produce a second substrate yield that is higher than the first substrate yield.

A non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising:

• identifying historical data associated with historical substrate lots associated with historical sets of substrates processed by substrate processing tools in a substrate processing facility, wherein each historical substrate lot is a corresponding set of historical substrates;
• training a machine learning model with data input associated with the historical data for the historical substrate lots to generate a trained machine learning model;
• providing, as input to the machine learning model, current features of the substrate processing facility, wherein the current features are associated with a first substrate yield;
• and
• causing, based on output obtained from the trained machine learning model, performance of one or more current corrective actions associated with processing of corresponding sets of current substrates of current substrate lots in the substrate processing facility to produce a second substrate yield that is higher than the first substrate yield.