Title :
Neural network modelling of GaAs pHEMTs suitable for millimeter-wave mixer design
Author :
Zlatica Marinković;Giovanni Crupi;Gustavo Avolio;Vera Marković;Alina Caddemi;Dominique M. M.-P. Schreurs
Author_Institution :
Faculty of Electronic Engineering, University of Niš
Abstract :
In this paper we present an approach for nonlinear modeling of GAs pHEMTs suitable for mixer design. We use artificial neural networks to model the DC drain current as well as the intrinsic capacitive core versus the intrinsic gate and drain voltages. The model is extracted from the measured DC current and S-parameters. The approach is validated by the comparison of the model simulations with the corresponding nonlinear measurements.
Keywords :
"Integrated circuit modeling","Artificial neural networks","Logic gates","Mixers","Current measurement","Voltage measurement","Gallium arsenide"
Conference_Titel :
Integrated Nonlinear Microwave and Millimetre-wave Circuits Workshop (INMMiC), 2015
DOI :
10.1109/INMMIC.2015.7330355
