Machine learning model training

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

• receiving spectral data of a substrate and metrology data corresponding to the spectral data of the substrate;
• determining a plurality of feature model configurations for each of a plurality of feature models, each of the plurality of feature model configurations comprising one or more feature model conditions;
• determining a plurality of feature model combinations, wherein each feature model combination of the plurality of feature model combinations comprises a subset of the plurality of feature model configurations;
• generating a plurality of input datasets, wherein each input dataset of the plurality of input datasets is generated based on application of the spectral data to a respective feature model combination of the plurality of feature model combinations;
• training a plurality of machine learning models, wherein each machine learning model is trained to generate an output using an input dataset of the plurality of input datasets and the metrology data;
• and
• selecting a trained machine learning model from the plurality of trained machine learning models satisfying one or more selection criteria.

A system comprising:

• a memory;
• and
• a processing device coupled to the memory, the processing device to:
• receive spectral data of a substrate and metrology data corresponding to the spectral data of the substrate;
• determine a plurality of feature model configurations for each of a plurality of feature models, each of the plurality of feature model configurations comprising one or more feature model conditions;
• determine a feature model combination, wherein the feature model combination comprises a subset of the plurality of feature model configurations;
• generate an input dataset, wherein the input dataset is generated based on application of the spectral data to the feature model combination;
• and
• train a plurality of machine learning models, wherein each machine learning model is trained to generate an output using the input dataset and the metrology data;
• and
• select a trained machine learning model from the plurality of trained machine learning models satisfying one or more selection criteria.