• DocumentCode
    1762135
  • Title

    Adaptive-Learning-Based Importance Sampling for Analog Circuit DPPM Estimation

  • Author

    Yilmaz, Ender ; Ozev, Sule

  • Volume
    32
  • Issue
    1
  • fYear
    2015
  • fDate
    Feb. 2015
  • Firstpage
    36
  • Lastpage
    43
  • Abstract
    This paper addresses the important problem of defect level estimation. For more than 30 years, there have been published models which are commonly used to estimate the time zero test escape rate of digital logic designs. However, estimating escape rate for analog circuits is much more challenging. This paper applies importance sampling techniques to this problem to arrive at a much more practical method of analog defect level computation.
  • Keywords
    analogue circuits; importance sampling; learning (artificial intelligence); adaptive-learning-based importance sampling; analog circuit DPPM estimation; analog defect level computation; defect level estimation; digital logic designs; time zero test escape rate; Adaptation models; Analog circuits; Computational modeling; Fault detection; Integrated circuit modeling; Mathematical model; Mixed analog digital integrated circuits; Monte Carlo methods; Sampling methods; System-on-chip;
  • fLanguage
    English
  • Journal_Title
    Design & Test, IEEE
  • Publisher
    ieee
  • ISSN
    2168-2356
  • Type

    jour

  • DOI
    10.1109/MDAT.2014.2361719
  • Filename
    6917044
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1762135