• DocumentCode
    1133709
  • Title

    Statistical Metrology of Metal Nanocrystal Memories With 3-D Finite-Element Analysis

  • Author

    Shaw, Jonathan ; Hou, Tuo-Hung ; Raza, Hassan ; Kan, Edwin Chihchuan

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
  • Volume
    56
  • Issue
    8
  • fYear
    2009
  • Firstpage
    1729
  • Lastpage
    1735
  • Abstract
    We study the parametrical yield of memory windows for the metal nanocrystal (NC) Flash memories with consideration of the 3-D electrostatics and channel percolation effects. Monte Carlo parametrical variation that accounts for the number and size fluctuations in NCs as well as channel length is used to determine the threshold voltage distribution and bit error rate for gate length scaling to 20 nm. Devices with nanowire-based channels are compared with planar devices having the same gate stack structure. Scalability prediction by 1-D analysis is found to be very different from 3-D modeling due to underestimation of effective NC coverage and failure to consider the 3-D nature of the channel percolation effect.
  • Keywords
    Monte Carlo methods; electrostatics; estimation theory; flash memories; nanostructured materials; nanowires; percolation; statistical analysis; 3-D electrostatics; 3-D finite-element analysis; Monte Carlo parametrical variation; bit error rate; channel length; channel percolation effects; gate length scaling; gate stack structure; memory windows; metal nanocrystal flash memories; nanowire-based channels; statistical metrology; threshold voltage distribution; Bit error rate; Electrostatics; Finite element methods; Flash memory; Fluctuations; Metrology; Monte Carlo methods; Nanocrystals; Nanoscale devices; Threshold voltage; 3-D electrostatics; Nanocrystal (NC); nonvolatile memories; programming window distribution;
  • fLanguage
    English
  • Journal_Title
    Electron Devices, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9383
  • Type

    jour

  • DOI
    10.1109/TED.2009.2024108
  • Filename
    5164934