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

Published

Conference Paper

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]. © Copyright 2010 ACM.

Full Text

Duke Authors

Cited Authors

  • Zhang, W; Li, X; Rutenbar, RA

Published Date

  • September 7, 2010

Published In

Start / End Page

  • 262 - 267

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781450300025

Digital Object Identifier (DOI)

  • 10.1145/1837274.1837342

Citation Source

  • Scopus