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Parametric yield estimation considering leakage variability
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 41st annual Design Automation Conference table of contents
San Diego, CA, USA
SESSION: Yield estimation and optimization table of contents
Pages: 442 - 447  
Year of Publication: 2004
ISBN:1-58113-828-8
Authors
Rajeev R. Rao  University of Michigan, Ann Arbor, MI
Anirudh Devgan  IBM Corporation, Austin, TX
David Blaauw  University of Michigan, Ann Arbor, MI
Dennis Sylvester  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
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Downloads (6 Weeks): 8,   Downloads (12 Months): 46,   Citation Count: 29
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ABSTRACT

Leakage current has become a stringent constraint in modern processor designs in addition to traditional constraints on frequency. Since leakage current exhibits a strong inverse correlation with circuit delay, effective parametric yield prediction must consider the dependence of leakage current on frequency. In this paper, we present a new chip-level statistical method to estimate the total leakage current in the presence of within-die and die-to-die variability. We develop a closed-form expression for total chip leakage that models the dependence of the leakage current distribution on a number of process parameters. The model is based on the concept of scaling factors to capture the effects of within-die variability. Using this model, we then present an integrated approach to accurately estimate the yield loss when both frequency and power limits are imposed on a design. Our method demonstrates the importance of considering both these limiters in calculating the yield of a lot.


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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S. Narendra, D. Blaauw, A. Devgan and F. Najm, "Leakage issues in IC design: Trends, estimation and avoidance", Tutorial, ICCAD 2003.
 
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A. Papoulis, Probability, Random Variables and Stochastic Processes, McGraw-Hill Inc., New York 1991.
 
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CITED BY  29

Collaborative Colleagues:
Rajeev R. Rao: colleagues
Anirudh Devgan: colleagues
David Blaauw: colleagues
Dennis Sylvester: colleagues