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 and simulated data for simulated substrate lots associated with simulated sets of substrates processed by simulated substrate processing tools, wherein each historical substrate lot is a corresponding set of historical substrates, wherein each simulated substrate lot is a simulation of a corresponding set of substrates, the simulated data being generated based on perturbations of facility data, and wherein the perturbations comprise moving forward or backward lots in route, wherein the trained machine learning model is trained to generate corresponding output indicative of one or more corresponding corrective actions to be performed in the substrate processing facility;
• 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;
• generating, based on perturbations of facility data, simulated data for simulated substrate lots associated with simulated sets of substrates processed by simulated substrate processing tools, wherein each simulated substrate lot is a simulation of a corresponding set of substrates, wherein the perturbations comprise moving forward or backward lots in route;
• generating features from the historical data for the historical substrate lots and the simulated data for the simulated substrate lots;
• training a machine learning model with data input comprising the features to generate a trained machine learning model, wherein the trained machine learning model is trained to generate corresponding output indicative of one or more corresponding corrective actions to be performed in the substrate processing facility;
• 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.