Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy

Abstract

In this article, we have presented the use of prior physics knowledge-based artificial neural networks (KBANNs) to improve the simulation speed and accuracy of compact models for circuit simulations. Multiple architectures such as source-difference artificial neural network (SD-ANN), prior knowledge input artificial neural network (PKI-ANN), and prior knowledge input with difference artificial neural network (PKID-ANN) have been developed to evaluate the best KBANN framework for runtime improvement, accuracy improvement as well as development (training) time improvement. In particular, the proposed PKID-ANN is the best KBANN since it matches the accuracy of rigorous physics-based technology computer-aided design (TCAD) solvers while exhibiting the most compact architecture, the fastest learning rate, and the fastest runtime. To the best of our knowledge, we have reported the first PKID-based architecture for device modeling in this article, as well as the fastest (simulation time) artificial neural network (ANN) compact model for FinFETs, which even outperforms conventional ANNs (C-ANNs) and industry standard compact models (BSIM-CMG) while retaining the physics and predictive accuracy. Moreover, the reported model also has the lowest training/development cost among all alternatives.