A large-signal characterization of an HEMT using a multilayered neural network

We propose an approach to describe the large-signal behavior of a high electron-mobility transistor (HEMT) by using a multilayered neural network, To conveniently implement this in standard circuit simulators, we extracted tile HEMT's bias-dependent behavior in terms of conventional small-signal equivalent-circuit elements. We successfully represented seven intrinsic elements with a five-layered neural network; (composed of 28 neurons) whose inputs are the gate-to-source bias (V-gs) and drain-to-source bias (V-ds). A ''well-trained'' neural network shows excellent accuracy and generates good extrapolations.