Using physical process window trend to constrain nominal condition machine learning model behavior

Abstract

A method for training a machine learning model associated with a lithography process is disclosed. More particularly, a method for training a machine learning model associated with a lithography process to generate process window trend predictions for an imaging characteristic with improved accuracy is disclosed. The disclosed method provides a universal machine learning model that can group training data based on an imaging characteristic variation across a varied lithography process condition and use the grouped training data to train the model and generate a predicted variation trend. A loss function may be used to evaluate and minimize an overlapping area between the predicted variation trend and the reference variation trend, a distance between the predicted variation trend and the reference variation trend, or a slope between a first point and a second point on both the predicted variation trend and the reference variation trend.

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