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In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.3 pp.900-911

- Publication Date
- 2009/03/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E92.A.900

- Type of Manuscript
- PAPER

- Category
- VLSI Design Technology and CAD

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Shingo TAKAHASHI, Shuji TSUKIYAMA, "A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 3, pp. 900-911, March 2009, doi: 10.1587/transfun.E92.A.900.

Abstract: In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.900/_p

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@ARTICLE{e92-a_3_900,

author={Shingo TAKAHASHI, Shuji TSUKIYAMA, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together},

year={2009},

volume={E92-A},

number={3},

pages={900-911},

abstract={In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.},

keywords={},

doi={10.1587/transfun.E92.A.900},

ISSN={1745-1337},

month={March},}

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TY - JOUR

TI - A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 900

EP - 911

AU - Shingo TAKAHASHI

AU - Shuji TSUKIYAMA

PY - 2009

DO - 10.1587/transfun.E92.A.900

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E92-A

IS - 3

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - March 2009

AB - In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.

ER -