Substrate process endpoint detection using machine learning

A system comprising:

• a memory;
• and
• a processing device coupled to memory, the processing device to:
• obtain a machine learning model trained to provide a current value for a metrology measurement based on current spectral data collected during a current process for a current substrate at a manufacturing system, wherein the trained machine learning model is associated with a performance rating that satisfies a performance criterion associated with a type of the metrology measurement;
• provide the current spectral data as input to the trained machine learning model;
• extract, from one or more outputs of the trained machine learning model, an indication of a respective metrology measurement value corresponding to the current substrate, wherein the respective metrology measurement value is associated with the type of the metrology measurement;
• and
• responsive to determining that the respective metrology measurement value satisfies a metrology measurement criterion associated with the current process, generating an instruction comprising a command to terminate the current process at the manufacturing system.

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

• provide training data to train each of a plurality of machine learning models to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected during a substrate process performed for the substrate, wherein each of the plurality of machine learning models are associated with a different type of a set of machine learning model types;
• assign a performance rating to each of the plurality of machine learning models based on an accuracy of a value for the metrology measurement provided by a respective machine learning model in view of a measured value for the metrology measurement, the measured value generated based on historical metrology data collected by metrology equipment for a prior substrate of a set of prior substrates;
• and
• select, in view of the performance rating for each of the plurality of machine learning models, the respective machine learning model to be applied to future spectral data collected during a future substrate process performed for a future substrate.