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Statistical leakage analysis using the deterministic modeling of cell leakage current

Authors: Jae Hoon Kim; Young Hwan Kim;

Statistical leakage analysis using the deterministic modeling of cell leakage current

Abstract

This paper presents a new approach to estimate the n-sigma chip leakage current in the chip leakage probability density function of statistical leakage analysis (SLA) through gate-level deterministic leakage analysis (DLA). Although SLA provides accurate result than corner-based analysis, it is an impractical solution in recent technology comprising millions logic cells in a system since its computational complexity is O(N2). The proposed method uses DLA, and this makes it not only efficient but also suitable for use in the existing design environments. In addition, by providing the upper and lower bounds of SLA results, n-sigma chip leakage, the proposed method avoids the pessimism of existing DLA methods.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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