• DocumentCode
    856052
  • Title

    A statistical model for extracting geometric sources of transistor performance variation

  • Author

    Ma, Sean T. ; Keshavarzi, Ali ; De, Vivek ; Brews, John R.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Arizona, Tucson, AZ, USA
  • Volume
    51
  • Issue
    1
  • fYear
    2004
  • Firstpage
    36
  • Lastpage
    41
  • Abstract
    This paper provides an approach to extracting geometrical variations in nominally identical devices fabricated in close proximity to each other. The method assumes correlations between factors can be neglected. With this assumption, it is shown that geometrical variations are significant, and any study of microscopic variations, such as dopant fluctuations, must first strip away these macroscopic geometrical variations. We assess the gate length L and width W dependence of threshold voltage (VT) variations. Related geometrical variations, namely corner influences where the source and drain encounter the isolation edges are examined, and incorporated in the model. Results from the model are compared to measurements at small dimensions. The differences provide lower bounds for excess variations other than these geometrical contributions. Our study shows that these other variations account for almost half the total VT variance at the smallest device size fabricated, demonstrating the seriousness of these other variations when scaling down devices.
  • Keywords
    semiconductor device models; semiconductor doping; transistors; corner influences; dopant fluctuations; gate length; geometric source extraction; isolation edges; nominally identical devices; statistical model; threshold voltage; transistor performance variation; Fluctuations; Implants; MOSFETs; Microscopy; Parametric statistics; Process control; Semiconductor process modeling; Solid modeling; Statistical distributions; Threshold voltage;
  • fLanguage
    English
  • Journal_Title
    Electron Devices, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9383
  • Type

    jour

  • DOI
    10.1109/TED.2003.820648
  • Filename
    1258143