An Enhanced Machine Learning Model for Adaptive Monte Carlo Yield Analysis
Richard Kimmel, Tong Li, David Winston · 2020
This paper presents a novel methodology for generating machine learning models used by an adaptive Monte Carlo analysis. The advantages of this methodology are that model generation occurs at the beginning of the analysis with no retraining required, it applies to both classification and regression models, and accuracy of the Monte Carlo analysis is not impacted by the accuracy of the model. This paper discusses the details of constructing and enhancing the machine learning model with emphasis on model training. It will then show how the model enables a Monte Carlo analysis that monitors and adapts to model mispredictions.