Neural networks for large- and small-signal modeling of MESFET/HEMT transistors

Abstract

In this paper, we present a comparative study of three neural networks-based solutions for large- and small-signal modeling of MESFET and HEMT transistors. The first two neural architectures are specific for this modeling problem: the generalized radial basis function (GRBF) network, and the smoothed piecewise linear (SPWL) model. These models are compared with the well-known multilayer perceptron (MLP) network. Results are presented for both the large- and small-signal regimes separately. Finally, a global model is proposed that is able to accurately characterize the whole behavior of the transistors. This model is based on a simple combination of the best models obtained for the two kinds of regimes.