Small-signal and noise modeling of class of HEMTs using knowledge-based artificial neural networks

Abstract

In this article, the neural network approach is exploited for development of bias-dependent small-signal and noise models of a class of microwave field effect transistor (FETs) made in the same technology but differing in the gate width. The prior knowledge neural approach is applied. Introducing gate width at the input of proposed neural networks, as well as the S/noise parameters of a device that belongs to the same class as the modeled device representing the prior knowledge, leads to very accurate scattering and noise parameters' modeling, as exemplified by modeling of class of pseudomorphic high electron mobility transistor (pHEMT) devices. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.