Machine learning-powered compact modeling of stochastic electronic devices using mixture density networks
Abstract The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to accurately account for the inherent stochastic nature of certain devices.While conventional deterministic models have served as indispensable tools for circuit d