A Machine Learning-Based Epistemic Modeling Framework for EMC and SI Assessment

Abstract

A novel machine learning-based framework is presented to evaluate the effect of design parameters, affected by epistemic uncertainty, on the Signal Integrity (SI) and Electromagnetic Compatibility (EMC) performance of electronic products. In particular, possibility theory is leveraged to characterize the epistemic variations, and is combined with Bayesian optimization to accurately and efficiently perform uncertainty quantification (UQ). A suitable application example validates the proposed method.