Artificial neural networks for compound semiconductor device modeling and characterization

Abstract

This paper reviews applications of artificial neural networks (ANNs) to several distinct problem areas that arise in compound semiconductor device modeling and characterization. Properties and corresponding benefits of ANNs for these applications are presented culminating in an accurate large signal-model of GaN HEMT transistors (with thermal and trapping effects). Smooth functional approximations of device properties and parameters are also illustrated based on unique properties of ANNs. Finally, it is suggested that ANN technology can be quite helpful as a device characterization tool, over and above the obvious utility for multi-dimensional data fitting.