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A review on the artificial neural network applications for small‐signal modeling of microwave FETs
Author(s) -
Marinković Zlatica,
Crupi Giovanni,
Caddemi Alina,
Marković Vera,
Schreurs Dominique M.M.P.
Publication year - 2019
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2668
Subject(s) - artificial neural network , computer science , signal (programming language) , field (mathematics) , microwave , construct (python library) , electronic engineering , point (geometry) , small signal model , artificial intelligence , machine learning , engineering , electrical engineering , telecommunications , mathematics , geometry , voltage , pure mathematics , programming language
The purpose of this paper is to provide a comprehensive overview of the field‐effect transistor (FET) small‐signal modeling using artificial neural networks (ANNs). To gain an in‐depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering ( S‐ ) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN‐based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.

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