Advanced neural network techniques for GaN-HEMT dynamic behavior characterization

Abstract

This paper presents a new approach to build RF dynamic behavioral models, based on Time-Delay Neural Networks (TDNNs), suitable for FET devices, and capable to identify the working class and to characterize both short- and long-term device memory, through a time-domain training procedure, for a wide range of input power levels. The presented model has been effectively applied to GaN-based devices, working in class A, AB and B. © 2006 EuMA.