A method for reducing uncertainty in machine learning model predictions.

摘要

Methods for quantifying uncertainty in parameterized (eg, machine learning) model predictions are described herein. The method involves having a parameterized model predict multiple posterior distributions from the parameterized model for a given input. A multiple posterior distribution includes a distribution among distributions. The method comprises determining the variability of a predicted multiple posterior distribution for a given input by sampling from one of the distributions; and using the determined variability within the predicted multiple posterior distributions to quantify the uncertainty in the parameterized model prediction. The parameterized model includes an encoder-decoder architecture. The method is a predicted method to adjust a parameterized model to reduce uncertainty in the parameterized model to predict wafer geometry, overlay and/or other information as part of a semiconductor manufacturing process.