• DocumentCode
    3692900
  • 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
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    3
  • 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"
  • Publisher
    ieee
  • Conference_Titel
    Integrated Nonlinear Microwave and Millimetre-wave Circuits Workshop (INMMiC), 2015
  • Type

    conf

  • DOI
    10.1109/INMMIC.2015.7330355
  • Filename
    7330355
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3692900