Modeling power and intermodulation behavior of microwave transistors with unified small-signal/large-signal neural network models

Abstract

This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large-signal model of a power MESFET device, modeling the nonlinear relationship of drain-source current Ids as well as gate and drain charge Qg and Qd with respect to intrinsic voltages Vgs and Vds over the whole operational bias region. The neural models have been implemented into a user-defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load-pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first-order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276–284, 2003.