IEICE TRANSACTIONS on Fundamentals
Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling
Summary :
An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.7 pp.1390-1399
- Publication Date
- 2016/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.1390
- Type of Manuscript
- Special Section PAPER (Special Section on Design Methodologies for System on a Chip)
- Category
Authors
Hiromitsu AWANO
Kyoto University
Masayuki HIROMOTO
Kyoto University
Takashi SATO
Kyoto University
Keyword
Latest Issue
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Hiromitsu AWANO, Masayuki HIROMOTO, Takashi SATO, "Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 7, pp. 1390-1399, July 2016, doi: 10.1587/transfun.E99.A.1390.
Abstract: An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.1390/_p
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@ARTICLE{e99-a_7_1390,
author={Hiromitsu AWANO, Masayuki HIROMOTO, Takashi SATO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling},
year={2016},
volume={E99-A},
number={7},
pages={1390-1399},
abstract={An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.},
keywords={},
doi={10.1587/transfun.E99.A.1390},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1390
EP - 1399
AU - Hiromitsu AWANO
AU - Masayuki HIROMOTO
AU - Takashi SATO
PY - 2016
DO - 10.1587/transfun.E99.A.1390
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2016
AB - An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.
ER -
English
