A Wiener-Type Dynamic Neural Network Approach to the Modeling of Nonlinear Microwave Devices and Its Applications
Wenyuan Liu, Weicong Na, Feng Feng, Lin Zhu, Qian Lin
Abstract
The Wiener-type dynamic neural network (DNN) approach can be used for nonlinear device modeling. The analytical formulation of Wiener-type DNN structure consists of a cascade of a simplified linear dynamic part and a nonlinear static part based on a Wiener system formulation. The Wiener-type DNN model can be trained to be accurate relative to device data. Furthermore, the Wiener-type DNN provides enhanced convergence properties over existing neural network approaches such as time delay neural network (TDNN) and TDNN with extrapolation. Modeling of GaAs metal-semiconductor-field-effect transistor (MESFET) is presented. In this paper, we address the use of Wiener-type DNN model in harmonic balance simulations which demonstrate that the Wiener-type DNN is a robust approach for modeling microwave devices. It is useful for systematic and automated update of nonlinear device model library for existing circuit simulators.