Predictive technology model for nano-CMOS design exploration

Yu Cao, Wei Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Predictive MOSFET model is critical for early circuit design research. In this work, a new generation of Predictive Technology Model (PTM) is developed, covering emerging physical effects and alternative structures. Based on physical models and early stage silicon data, PTM of bulk and double-gate devices are successfully generated from 130nm to 32nm technology nodes, with effective channel length down to 13nm. By tuning only ten primary parameters, PTM can be easily customized to cover a wide range of process uncertainties. The accuracy of PTM predictions is comprehensively verified with published silicon data: the error of the current is below 10% for both NMOS and PMOS. Furthermore, the new PTM correctly captures process sensitivities in the nanometer regime. PTM is available on line at http://www.eas.asu.edu/~ptm.

Original languageEnglish (US)
Title of host publication2006 1st International Conference on Nano-Networks and Workshops, Nano-Net
DOIs
StatePublished - Dec 1 2006
Event2006 1st International Conference on Nano-Networks and Workshops, Nano-Net - Lausanne, Switzerland
Duration: Sep 14 2006Sep 16 2006

Publication series

Name2006 1st International Conference on Nano-Networks and Workshops, Nano-Net

Other

Other2006 1st International Conference on Nano-Networks and Workshops, Nano-Net
CountrySwitzerland
CityLausanne
Period9/14/069/16/06

Keywords

  • Early design exploration
  • FinFET
  • Predictive modeling
  • Process variations
  • Technology scaling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications

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  • Cite this

    Cao, Y., & Zhao, W. (2006). Predictive technology model for nano-CMOS design exploration. In 2006 1st International Conference on Nano-Networks and Workshops, Nano-Net [4152810] (2006 1st International Conference on Nano-Networks and Workshops, Nano-Net). https://doi.org/10.1109/NANONET.2006.346227