Measurement-Based Non-Quasi-Static Large-Signal FET Model Using Artificial Neural Networks

Abstract

A new measurement-based FET model is presented which combines non-quasi-static dynamics formulated with constitutive relations derived using adjoint and conventional artificial neural networks (ANN). The new model features smoother constitutive relations than spline-based methods while maintaining the non-quasi-static dynamics for accurate distortion simulations. Additionally, this work demonstrates, for the first time, the construction of an adjoint-trained ANN-based "high-frequency drain current" constitutive relation (accounting for dispersion due to traps and thermal effects in III-V FETs), along with drain and gate terminal charges from measured bias-dependent data. The model is implemented in Agilent ADS and validated with nonlinear measurements on a 0.25mum GaAs pHEMT device