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Accurate and efficient gate-level parametric yield estimation considering correlated variations in leakage power and performance
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 42nd annual Design Automation Conference table of contents
Anaheim, California, USA
SESSION: Impact of process variations on power table of contents
Pages: 535 - 540  
Year of Publication: 2005
ISBN:1-59593-058-2
Authors
Ashish Srivastava  University of Michigan, Ann Arbor, MI
Saumil Shah  University of Michigan, Ann Arbor, MI
Kanak Agarwal  University of Michigan, Ann Arbor, MI
Dennis Sylvester  University of Michigan, Ann Arbor, MI
David Blaauw  University of Michigan, Ann Arbor, MI
Stephen Director  University of Michigan, Ann Arbor, MI
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 9,   Downloads (12 Months): 39,   Citation Count: 25
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ABSTRACT

Increasing levels of process variation in current technologies have a major impact on power and performance, and result in parametric yield loss. In this work we develop an efficient gate-level approach to accurately estimate the parametric yield defined by leakage power and delay constraints, by finding the joint probability distribution function (jpdf) for delay and leakage power. We consider inter-die variations as well as intra-die variations with correlated and random components. The correlation between power and performance arise due to their dependence on common process parameters and is shown to have a significant impact on yield in high-frequency bins. We also propose a method to estimate parametric yield given the power/delay jpdf that is much faster than numerical integration with good accuracy. The proposed approach is implemented and compared with Monte Carlo simulations and shows high accuracy, with the yield estimates achieving an average error of 2%.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  25

Collaborative Colleagues:
Ashish Srivastava: colleagues
Saumil Shah: colleagues
Kanak Agarwal: colleagues
Dennis Sylvester: colleagues
David Blaauw: colleagues
Stephen Director: colleagues