Equipment parameter management at a manufacturing system using machine learning

A system comprising:

• a memory;
• and
• a processing device coupled to the memory, the processing device to:
• receive first data indicative of an input value of a setting for calibration of an equipment parameter of a process tool of a plurality of process tools of a first manufacturing system;
• provide at least the first data as input to a trained machine learning model, wherein the trained machine learning model is trained using historical data pertaining to equipment parameters of the plurality of process tools of the first manufacturing system;
• obtain, as output of the trained machine learning model, a predicted value of a metric corresponding to the equipment parameter;
• compare the predicted value of the metric with the first data;
• and
• update the setting for calibration of the equipment parameter based on the comparing.

A non-transitory computer readable medium comprising instructions that, when executed by a processing device, cause the processing device to:

• receive first data indicative of an input value of a setting for calibration of an equipment parameter of a process tool of a plurality of process tools of a first manufacturing system;
• provide at least the first data as input to a trained machine learning model, wherein the trained machine learning model is trained using historical data pertaining to equipment parameters of the plurality of process tools of the first manufacturing system;
• obtain, as output of the trained machine learning model, a predicted value of a metric corresponding to the equipment parameter;
• compare the predicted value of the metric with the first data;
• and
• update the setting for calibration of the equipment parameter based on the comparing.