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CAD tools for variation tolerance
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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: Special session: DFM and variability: theory and practice table of contents
Pages: 766 - 766  
Year of Publication: 2005
ISBN:1-59593-058-2
Authors
David Blaauw  University of Michigan, Ann Arbor, MI
Kaviraj Chopra  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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ABSTRACT

Process variability greatly affects power and timing of nanometer scale CMOS circuits, leading to parametric yield loss due to both timing and power constraint violations. This parametric yield loss will continue to worsen in future technologies as a result of increasing process variations [1] and the increased importance of leakage power. Hence, statistical techniques are required to maximize parametric yield under given power and frequency constraints. Recently, much progress has been reported in the area of statistical modeling of leakage power [6] and circuit timing [2-5]. These techniques are useful in analyzing the impact of process variations on performance and power in nanometer CMOS designs. In this extended abstract, we outline the need for statistical optimization methods.


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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Collaborative Colleagues:
David Blaauw: colleagues
Kaviraj Chopra: colleagues