Small-Signal Modeling of Microwave MESFETs Using RBF-ANNs

Abstract

This paper presents a comprehensive approach to accurate and efficient modeling of microwave active devices such as metal semiconductor field effect transistors (MESFETs) using artificial neural networks (ANNs). A radial basis function (RBF)-ANN model is developed for S-parameters and equivalent circuit parameters (ECPs) of MESFETs. The training and testing data for these models are obtained from the measured two-port scattering parameters and extracted ECPs of a 0.25 times 200 mum (4 times 50 mum) gallium arsenide MESFET. A four- input eight-output ANN is used to model the S-parameters of a microwave MESFET versus bias, temperature, and frequency, and a three-input eight-output ANN is used to model the ECPs of a microwave MESFET versus bias and temperature. Comparisons of measured and modeled data are presented, and the results show very good agreement. The average relative errors using the RBF-ANN models for the S-parameters and ECPs were 0.81% and 0.77%, respectively, which both represent about 60% reduction in error when compared to backpropagation ANN models of similar parameters of the same device.