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A coupled FDTD‐artificial neural network technique for large‐signal analysis of microwave circuits
Author(s) -
Goasguen S.,
ElGhazaly S. M.
Publication year - 2002
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.10018
Subject(s) - artificial neural network , mesfet , monolithic microwave integrated circuit , microwave , signal (programming language) , finite difference time domain method , electronic engineering , electronic circuit , computer science , integrated circuit , time domain , voltage , transistor , engineering , electrical engineering , artificial intelligence , physics , telecommunications , field effect transistor , amplifier , cmos , quantum mechanics , computer vision , programming language
Abstract We propose a first‐order global modeling approach of Monolithic Microwave Integrated Circuits (MMIC) by modeling the active device with a neural network based on a full hydrodynamic model. This neural network describes the nonlinearities of the equivalent circuit parameters of an MESFET implemented in an extended Finite Difference Time Domain mesh to predict large‐signal behaviors of the circuits. We successfully represented the transistor characteristics with a one‐hidden‐layer neural network, whose inputs are the gate voltage V gs and the drain voltage V ds . The trained neural network shows excellent accuracy and dramatically reduces the computational time in comparison with the hydrodynamic model. Small‐signal simulation is performed and validated by comparison with HP‐Libra. Then large‐signal behaviors are obtained, which demonstrates the successful use of the artificial neural network. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 25–36, 2002.