Analysis and validation of neural network approach for extraction of small-signal model parameters of microwave transistors

Extraction of parameters of a small-signal model is the first step in modeling transistors for advanced microwave applications. There are different extraction techniques, mostly based on optimizations or on direct analytical procedures. An alternative to the standard extraction methods are procedures based on the application of artificial neural networks. Namely, an artificial neural network is trained to determine equivalent circuit elements directly from the measured scattering parameters without the need for any additional tuning of the elements. In this study the results of a comprehensive analysis of the neural network based extraction procedures are presented. Stability of the extracted values with the choice of the input set of scattering parameters as well as accuracy of the final small-signal model were examined. Moreover, the influence of the number of measured data necessary for development of reliable neural models was investigated. The extraction procedure was examined for a HEMT transistor working under varying temperature conditions. (C) 2012 Elsevier Ltd. All rights reserved.