• DocumentCode
    1401666
  • Title

    Stochastic Analysis and Design Guidelines for CNFETs in Gigascale Integrated Systems

  • Author

    Zarkesh-Ha, Payman ; Shahi, Ali Arabi M

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of New Mexico, Albuquerque, NM, USA
  • Volume
    58
  • Issue
    2
  • fYear
    2011
  • Firstpage
    530
  • Lastpage
    539
  • Abstract
    An integrated and compact model for probability of failure in carbon nanotube field-effect transistors (CNFETs) that includes 1) void CNFETs, 2) carbon nanotube (CNT) density variation, and 3) metallic CNTs is presented based on binomial probability distribution. Comparison with experimental data shows that the compact model successfully predicts the failure probability in CNFET devices. The model is used in a new design space to explore tradeoffs, key limitations, and opportunities for today´s gigascale CNFET integrated systems. To achieve 1-part-per-billion failure rate in a gigascale system, it is shown that an asymmetrically correlated stack of 25 CNFETs, each containing 18 CNTs in the channel can be used when the probability of metallic CNT occurrence is reduced to 3%. However, if the density of metallic CNTs approaches zero, then a similar failure rate can be achieved with a single CNFET that contains 15 CNTs in the channel.
  • Keywords
    carbon nanotubes; organic field effect transistors; probability; semiconductor device models; C; binomial probability distribution; carbon nanotube field effect transistors; compact model; failure probability; gigascale integrated systems; metallic CNT; stochastic analysis; void CNFET; Analytical models; CNTFETs; Equations; Logic gates; Mathematical model; Numerical models; Carbon nanotube field-effect transistor (CNFET); design optimization; gigascale integration; nanotechnology; probabilistic failure analysis;
  • fLanguage
    English
  • Journal_Title
    Electron Devices, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9383
  • Type

    jour

  • DOI
    10.1109/TED.2010.2092780
  • Filename
    5665766