• DocumentCode
    927006
  • Title

    Neurogenetic design centering

  • Author

    Pratap, Rana J. ; Sen, Padmanava ; Davis, Cleon E. ; Mukhophdhyay, Rajarshi ; May, Gary S. ; Laskar, Joy

  • Author_Institution
    Intel Corp., Chandler, AZ, USA
  • Volume
    19
  • Issue
    2
  • fYear
    2006
  • fDate
    5/1/2006 12:00:00 AM
  • Firstpage
    173
  • Lastpage
    182
  • Abstract
    A new technique for design centering and yield enhancement of devices and circuits is presented. The proposed method uses neural networks for device and/or circuit modeling and genetic algorithms for parametric yield optimization. It uses a Monte Carlo-based method for yield estimation via the neural models (thus consuming less time) and genetic algorithms for efficient design centering. The neurogenetic methodology has been used for design centering of SiGe heterojunction transistors and millimeter-wave voltage controlled oscillators. It results in significant yield enhancement of the SiGe heterojunction bipolar transistors (from 25% to 75%) and voltage controlled oscillators (from 8 % to 85 %). To the best of our knowledge, this method has not been reported previously.
  • Keywords
    Ge-Si alloys; Monte Carlo methods; circuit optimisation; genetic algorithms; heterojunction bipolar transistors; millimetre wave oscillators; neural nets; voltage-controlled oscillators; Monte Carlo method; SiGe; circuit modeling; device modeling; genetic algorithms; heterojunction bipolar transistors; millimeterwave oscillators; neural models; neural networks; neurogenetic design centering; parametric yield optimization; voltage controlled oscillators; yield enhancement; yield estimation; Algorithm design and analysis; Circuits; Genetic algorithms; Germanium silicon alloys; Heterojunctions; Neural networks; Optimization methods; Silicon germanium; Voltage-controlled oscillators; Yield estimation; Genetic algorithms (GA); Monte Carlo method; neural networks; parametric yield estimation;
  • fLanguage
    English
  • Journal_Title
    Semiconductor Manufacturing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0894-6507
  • Type

    jour

  • DOI
    10.1109/TSM.2006.873517
  • Filename
    1628980