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Title: Hypersphere Sampling for Accelerating High-Dimension and Low-Failure Probability Circuit-Yield Analysis
Authors: HAGIWARA, Shiho
DATE, Takanori
MASU, Kazuya
SATO, Takashi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 佐藤, 高史
Keywords: design for manufacturing
Monte Carlo method
importance sampling
process variation
norm minimization
Gaussian mixture models
hypersphere sampling
Issue Date: 1-Apr-2014
Publisher: IEICE
Journal title: IEICE Transactions on Electronics
Volume: E97.C
Issue: 4
Start page: 280
End page: 288
Abstract: This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.
Rights: © 2014 The Institute of Electronics, Information and Communication Engineers
DOI(Published Version): 10.1587/transele.E97.C.280
Appears in Collections:Journal Articles

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