PROCÉDÉ DE RÉDUCTION DE L'INCERTITUDE DANS DES PRÉDICTIONS DE MODÈLE D'APPRENTISSAGE PAR MACHINE

摘要

Described herein is a method for quantifying uncertainty in parameterized (e.g., machine learning) model predictions. The method comprising causing a machine learning model to predict multiple output realizations from the machine learning model for a given input; determining a variability of the predicted multiple output realizations for the given input, and using the determined variability in the predicted multiple output realizations to adjust the machine learning model to decrease an uncertainty of the machine learning model. The machine learning model comprises encoder-decoder architecture. The method comprises using the determined variability in the predicted multiple output realizations to adjust the machine learning model to decrease the uncertainty of the machine learning model for predicting wafer geometry, overlay, and/or other information as part of a semiconductor manufacturing process.