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

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3 Author(s)
Wangyang Zhang ; Electrical & Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 ; Xin Li ; Rob A. Rutenbar

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].

Published in:

Design Automation Conference (DAC), 2010 47th ACM/IEEE

Date of Conference:

13-18 June 2010