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9th International Symposium on Quality Electronic Design (isqed 2008)
Process-Variation Statistical Modeling for VLSI Timing Analysis
March 17-March 19
ISBN: 978-0-7695-3117-5
| ASCII Text | x | ||
| Jui-Hsiang Liu, Jun-Kuei Zeng, Ai-Syuan Hong, Lumdo Chen, Charlie Chung Ping Chen, "Process-Variation Statistical Modeling for VLSI Timing Analysis," Quality Electronic Design, International Symposium on, pp. 730-733, 9th International Symposium on Quality Electronic Design (isqed 2008), 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/ISQED.2008.66, author = {Jui-Hsiang Liu and Jun-Kuei Zeng and Ai-Syuan Hong and Lumdo Chen and Charlie Chung Ping Chen}, title = {Process-Variation Statistical Modeling for VLSI Timing Analysis}, journal ={Quality Electronic Design, International Symposium on}, volume = {0}, year = {2008}, isbn = {978-0-7695-3117-5}, pages = {730-733}, doi = {http://doi.ieeecomputersociety.org/10.1109/ISQED.2008.66}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Quality Electronic Design, International Symposium on TI - Process-Variation Statistical Modeling for VLSI Timing Analysis SN - 978-0-7695-3117-5 SP730 EP733 A1 - Jui-Hsiang Liu, A1 - Jun-Kuei Zeng, A1 - Ai-Syuan Hong, A1 - Lumdo Chen, A1 - Charlie Chung Ping Chen, PY - 2008 KW - Process Variation KW - VLSI KW - SSTA KW - non-Gaussian model VL - 0 JA - Quality Electronic Design, International Symposium on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISQED.2008.66
SSTA requires accurate statistical distribution models of non-Gaussian random variables of process parameters andtiming variables. Traditional quadratic Gaussian model has been shown to have some serious limitations. In particular, it limits the range of skewness that can be modeled and it can not model the kurtosis. In this paper, we presented complex-coefficient quadratic Gaussian polynomial model and higher order Gaussian polynomial model to resolve these difficulties. Experimental results show how our methods and new algorithms expose some enhancements in both accuracy and versatility.
Index Terms:
Process Variation, VLSI, SSTA, non-Gaussian model
Citation:
Jui-Hsiang Liu, Jun-Kuei Zeng, Ai-Syuan Hong, Lumdo Chen, Charlie Chung Ping Chen, "Process-Variation Statistical Modeling for VLSI Timing Analysis," isqed, pp.730-733, 9th International Symposium on Quality Electronic Design (isqed 2008), 2008
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