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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wangyang Zhang Xin Li Rutenbar, R.A. |
| Copyright Year | 2010 |
| Description | Author affiliation: Electrical & Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (Wangyang Zhang; Xin Li) || Computer Science Department, University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave, Urbana IL 61801 (Rutenbar, R.A.) |
| 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]. |
| Starting Page | 262 |
| Ending Page | 267 |
| File Size | 984946 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424466771 |
| ISSN | 0738100X |
| e-ISBN | 9781450300025 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Association for Computing Machinery, Inc. (ACM) |
| Subject Keyword | Bayesian methods Probes Costs Testing Manufacturing processes Pulp manufacturing Information theory Statistical analysis Sampling methods Particle measurements Variation Characterization Process Variation Integrated Circuit |
| Content Type | Text |
| Resource Type | Article |
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