The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'analyse temporelle statistique de la variabilité de la fabrication nécessite la modélisation de la variation spatialement corrélée. La modélisation commune basée sur une grille pour la variabilité spatialement corrélée implique un compromis entre précision et coût de calcul, en particulier pour l'ACP (analyse en composantes principales). Cet article propose d'interpoler spatialement les coefficients de variation pour améliorer la précision au lieu d'affiner les grilles spatiales. Les résultats expérimentaux montrent que l'interpolation spatiale réalise une expression continue de la corrélation spatiale et réduit l'erreur maximale des estimations temporelles provenant de grilles spatiales clairsemées. Pour atteindre la même précision, l'interpolation proposée a réduit le temps CPU pour PCA de 97.7 % dans un scénario de test. .
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Shinyu NINOMIYA, Masanori HASHIMOTO, "Accuracy Enhancement of Grid-Based SSTA by Coefficient Interpolation" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 12, pp. 2441-2446, December 2010, doi: 10.1587/transfun.E93.A.2441.
Abstract: Statistical timing analysis for manufacturing variability requires modeling of spatially-correlated variation. Common grid-based modeling for spatially-correlated variability involves a trade-off between accuracy and computational cost, especially for PCA (principal component analysis). This paper proposes to spatially interpolate variation coefficients for improving accuracy instead of fining spatial grids. Experimental results show that the spatial interpolation realizes a continuous expression of spatial correlation, and reduces the maximum error of timing estimates that originates from sparse spatial grids For attaining the same accuracy, the proposed interpolation reduced CPU time for PCA by 97.7% in a test case.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.2441/_p
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@ARTICLE{e93-a_12_2441,
author={Shinyu NINOMIYA, Masanori HASHIMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Accuracy Enhancement of Grid-Based SSTA by Coefficient Interpolation},
year={2010},
volume={E93-A},
number={12},
pages={2441-2446},
abstract={Statistical timing analysis for manufacturing variability requires modeling of spatially-correlated variation. Common grid-based modeling for spatially-correlated variability involves a trade-off between accuracy and computational cost, especially for PCA (principal component analysis). This paper proposes to spatially interpolate variation coefficients for improving accuracy instead of fining spatial grids. Experimental results show that the spatial interpolation realizes a continuous expression of spatial correlation, and reduces the maximum error of timing estimates that originates from sparse spatial grids For attaining the same accuracy, the proposed interpolation reduced CPU time for PCA by 97.7% in a test case.},
keywords={},
doi={10.1587/transfun.E93.A.2441},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Accuracy Enhancement of Grid-Based SSTA by Coefficient Interpolation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2441
EP - 2446
AU - Shinyu NINOMIYA
AU - Masanori HASHIMOTO
PY - 2010
DO - 10.1587/transfun.E93.A.2441
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2010
AB - Statistical timing analysis for manufacturing variability requires modeling of spatially-correlated variation. Common grid-based modeling for spatially-correlated variability involves a trade-off between accuracy and computational cost, especially for PCA (principal component analysis). This paper proposes to spatially interpolate variation coefficients for improving accuracy instead of fining spatial grids. Experimental results show that the spatial interpolation realizes a continuous expression of spatial correlation, and reduces the maximum error of timing estimates that originates from sparse spatial grids For attaining the same accuracy, the proposed interpolation reduced CPU time for PCA by 97.7% in a test case.
ER -