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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
제조 변동성에 대한 통계적 타이밍 분석에는 공간적으로 연관된 변동의 모델링이 필요합니다. 공간 상관 변동성을 위한 일반적인 그리드 기반 모델링에는 특히 PCA(주성분 분석)의 경우 정확도와 계산 비용 간의 균형이 필요합니다. 본 논문에서는 공간 격자를 정밀화하는 대신 정확도 향상을 위해 변동 계수를 공간적으로 보간하는 방법을 제안합니다. 실험 결과, 공간 보간법은 공간 상관관계의 연속적인 표현을 구현하고 희소 공간 격자에서 발생하는 타이밍 추정의 최대 오류를 감소시키는 것으로 나타났습니다. 동일한 정확도를 얻기 위해 제안한 보간법은 테스트 케이스에서 PCA의 CPU 시간을 97.7% 줄였습니다. .
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -