Efficient high-sigma yield analysis for high dimensional problems
Authors: 
Moning Zhang, Zuochang Ye, Yan WangAuthors Info & Claims
Moning Zhang, Zuochang Ye, Yan WangAuthors Info & ClaimsArticle No.: 107, Pages 1 - 6
Abstract
High-sigma analysis is important for estimating the probability of rare events. Traditional high-sigma analysis can only work for small-size (low-dimension) problems limiting to 10 ~ 20 random variables, mostly due to the difficulty of finding optimal boundary points. In this paper we propose an efficient method to deal with high-dimension problems. The proposed method is based on performing optimization in a series of low dimension parameter spaces. The final solution can be regarded as a greedy version of the global optimization. Experiments show that the proposed method can efficiently work with problems with > 100 independent variables.
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- Efficient high-sigma yield analysis for high dimensional problems
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- EDAA: European Design Automation Association
- ECSI
- EDAC: Electronic Design Automation Consortium
- IEEE Council on Electronic Design Automation (CEDA)
- The Russian Academy of Sciences: The Russian Academy of Sciences
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European Design and Automation Association
Leuven, Belgium
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Published: 24 March 2014
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- The Russian Academy of Sciences
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