Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference

Wangyang Zhang, Xin Li, Rob A. Rutenbar

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

Abstract

The expensive cost of testing and characterizing parametric variations is one of the most critical issues for today's nanoscale manufacturing process. In this paper, we propose a new technique, referred to as Bayesian Virtual Probe (BVP), to efficiently measure, characterize and monitor spatial variations posed by manufacturing uncertainties. In particular, the proposed BVP method borrows the idea of Bayesian inference and information theory from statistics to determine an optimal set of sampling locations where test structures should be deployed and measured to monitor spatial variations with maximum accuracy. Our industrial examples with silicon measurement data demonstrate that the proposed BVP method offers superior accuracy (1.5×error reduction) over the VP approach that was recently developed in [12].

Original languageEnglish (US)
Title of host publicationProceedings of the 47th Design Automation Conference, DAC '10
Pages262-267
Number of pages6
DOIs
StatePublished - 2010
Event47th Design Automation Conference, DAC '10 - Anaheim, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

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

Other

Other47th Design Automation Conference, DAC '10
CountryUnited States
CityAnaheim, CA
Period6/13/106/18/10

Keywords

  • Integrated circuit
  • Process variation
  • Variation characterization

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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  • Cite this

    Zhang, W., Li, X., & Rutenbar, R. A. (2010). Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference. In Proceedings of the 47th Design Automation Conference, DAC '10 (pp. 262-267). (Proceedings - Design Automation Conference). https://doi.org/10.1145/1837274.1837342