Physics-Inspired Neural Networks for Efficient Device Compact Modeling

Abstract

We present a novel physics-inspired neural network (Pi-NN) approach for compact modeling. Development of high-quality compact models for devices is a key to connect device science with applications. One recent approach is to treat compact modeling as a regression problem in machine learning. The most common learning algorithm to develop compact models is the multilayer perceptron (MLP) neural network. However, device compact models derived using the MLP neural networks often exhibit unphysical behavior, which is eliminated in the Pi-NN approach proposed in this paper, since the Pi-NN incorporates fundamental device physics. As a result, smooth, accurate, and computationally efficient device models can be learned from discrete data points by using Pi-NN. This paper sheds new light on the future of the neural network compact modeling.