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

Publication ,  Conference
Zhang, W; Li, X; Rutenbar, RA
Published in: Proceedings - Design Automation Conference
September 7, 2010

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.

Duke Scholars

Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

Publication Date

September 7, 2010

Start / End Page

262 / 267
 

Citation

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Zhang, W., Li, X., & Rutenbar, R. A. (2010). Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference. In Proceedings - Design Automation Conference (pp. 262–267). https://doi.org/10.1145/1837274.1837342
Zhang, W., X. Li, and R. A. Rutenbar. “Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference.” In Proceedings - Design Automation Conference, 262–67, 2010. https://doi.org/10.1145/1837274.1837342.
Zhang W, Li X, Rutenbar RA. Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference. In: Proceedings - Design Automation Conference. 2010. p. 262–7.
Zhang, W., et al. “Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference.” Proceedings - Design Automation Conference, 2010, pp. 262–67. Scopus, doi:10.1145/1837274.1837342.
Zhang W, Li X, Rutenbar RA. Bayesian Virtual Probe: Minimizing variation characterizationcost for nanoscale IC technologies via Bayesian inference. Proceedings - Design Automation Conference. 2010. p. 262–267.

Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

Publication Date

September 7, 2010

Start / End Page

262 / 267