Variability Aware FET Model With Physics Knowledge Based Machine Learning

We present variability-aware, computationally efficient, models for Fin Field Effect Transistors (FinFETs) using various machine learning (ML) architectures. This paper provides a detailed comparison of the various architectures. Our physics knowledge-based artificial neural networks (ANNs) demonstrate unprecedented modeling efficiency. This is the first work presenting Prior Knowledge with Input Difference (PKID) ANN architecture for device modeling.