Browsing by Author "Kuo, Yu-Hsin"

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Accurate Statistical Soft Error Rate (SSER) Analysis Using A Quasi-Monte Carlo Framework With Quality Cell Models
(2010)
Kuo, Yu-Hsin
;
Peng, Huan-Kai
;
Wen, Charles H. -P.
;
電機工程學系
;
Department of Electrical and Computer Engineering
For CMOS designs in sub 90nm technologies, statistical methods are necessary to accurately estimate circuit SER considering process variations. However, due to the lack of quality statistical models, current statistical SER (SSER) frameworks have not yet achieved satisfactory accuracy. In this work, we present accurate table-based cell models, based on which a Monte Carlo SSER analysis framework is built. We further propose a heuristic to customize the use of quasirandom sequences, which successfully speeds up the convergence of simulation error and hence shortens the runtime. Experimental results show that this framework is capable of more precisely estimating circuit SSERs with reasonable speed.
Statistical Soft Error Rate (SSER) Analysis for Scaled CMOS Designs
(2012-01-01)
Peng, Huan-Kai
;
Huang, Hsuan-Ming
;
Kuo, Yu-Hsin
;
Wen, Charles H. -P.
;
電機工程學系
This article re-examines the soft error effect caused by radiation-induced particles beyond the deep submicron regime. Considering the impact of process variations, voltage pulse widths of transient faults are found no longer monotonically diminishing after propagation, as they were formerly. As a result, the soft error rates in scaled electronic designs escape traditional static analysis and are seriously underestimated. In this article we formulate the statistical soft error rate (SSER) problem and present two frameworks to cope with the aforementioned sophisticated issues. The table-lookup framework captures the change of transient-fault distributions implicitly by using a Monte-Carlo approach, whereas the SVR-learning framework does the task explicitly by using statistical learning theory. Experimental results show that both frameworks can more accurately estimate SERs than static approaches do. Meanwhile, the SVR-learning framework outperforms the table-lookup framework in both SER accuracy and runtime.