• DocumentCode
    2524230
  • Title

    GaN power amplifier design based on artificial neural network modelling

  • Author

    Xiao, D. ; Schreurs, D. ; De Raedt, W. ; Derluyn, J. ; Balachander, K. ; Viaene, J. ; Germain, M. ; Nauwelaers, B. ; Borghs, G.

  • Author_Institution
    IMEC, Leuven
  • fYear
    2007
  • fDate
    8-10 Oct. 2007
  • Firstpage
    40
  • Lastpage
    43
  • Abstract
    GaN field effect transistors (FETs) have a strong potential for high-power applications. However the RF performance of these devices often experiences limitation due to trapping effects and self-heating. These complicate the development of accurate large-signal models for GaN FETs. To simplify this process, a state-space modelling technique using an artificial neural network (ANN) is used in this work to model the large signal behaviour of the GaN device. In this way, the model is constructed directly from large-signal measurement data collected while the device is in an operating mode close to its application, i.e., class AB power amplifier (PA). To demonstrate the approach, a hybrid power amplifier based on GaN FETs was designed and fabricated. The good agreement between measurements and simulation results verifies the proposed approach. It is the first time that this modelling approach is used in circuit design.
  • Keywords
    field effect transistor circuits; neural nets; power amplifiers; GaN; artificial neural network modelling; field effect transistors; hybrid power amplifier; power amplifier design; state-space modelling technique; Artificial neural networks; Circuit simulation; Circuit synthesis; FETs; Gallium nitride; Power amplifiers; Power measurement; Radio frequency; Radiofrequency amplifiers; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Microwave Integrated Circuit Conference, 2007. EuMIC 2007. European
  • Conference_Location
    Munich
  • Print_ISBN
    978-2-87487-002-6
  • Type

    conf

  • DOI
    10.1109/EMICC.2007.4412642
  • Filename
    4412642