Remembrance of transistors past: Compact model parameter extraction using bayesian inference and incomplete new measurements

Li Yu, Sharad Saxena, Christopher Hess, Abe Elfadel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

In this paper, we propose a novel MOSFET parameter extraction method to enable early technology evaluation. The distinguishing feature of the proposed method is that it enables the extraction of an entire set of MOSFET model parameters using limited and incomplete IV measurements from on-chip monitor circuits. An important step in this method is the use of maximum-A-posteriori estimation where past measurements of transistors from various technologies are used to learn a prior distribution and its uncertainty ma- trix for the parameters of the target technology. The frame- work then utilizes Bayesian inference to facilitate extraction using a very small set of additional measurements. The pro- posed method is validated using various past technologies and post-silicon measurements for a commercial 28-nm pro- cess. The proposed extraction could also be used to charac- terize the statistical variations of MOSFETs with the signi-cant benet that some constraints required by the backward propagation of variance (BPV) method are relaxed.

Original languageBritish English
Title of host publicationDAC 2014 - 51st Design Automation Conference, Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479930173
DOIs
StatePublished - 2014
Event51st Annual Design Automation Conference, DAC 2014 - San Francisco, CA, United States
Duration: 2 Jun 20145 Jun 2014

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference51st Annual Design Automation Conference, DAC 2014
Country/TerritoryUnited States
CitySan Francisco, CA
Period2/06/145/06/14

Keywords

  • Bayesian inference
  • Maximum-A-posteriori (MAP) estimation
  • MIT Virtual Source (MVS) MOSFET model
  • Param-eter extraction

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