Accurate and efficient gate-level parametric yield estimation considering correlated variations in leakage power and performance

Authors:
Ashish Srivastava;Saumil Shah;Kanak Agarwal;Dennis Sylvester;David Blaauw;Stephen Director
Affiliations:
University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI
Venue:
Proceedings of the 42nd annual Design Automation Conference
Year:
2005

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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%.