Subthreshold leakage modeling and estimation of general CMOS complex gates

Paulo F. Butzen, André I. Reis, Chris H. Kim, Renato P. Ribas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A new subthreshold leakage model is proposed in order to improve the static power estimation in general CMOS complex gates. Series-parallel transistor arrangements with more than two logic depth, as well as non-seriesparallel off-switch networks are covered by such analytical modeling. The occurrence of on-switches in off-networks, also ignored by previous works, is considered in the proposed analysis. The model has been validated through electrical simulations, taking into account transistor sizing, operating temperature, supply voltage and threshold voltage variations.

Original languageEnglish (US)
Title of host publicationIntegrated Circuit and System Design
Subtitle of host publicationPower and Timing Modeling, Optimization and Simulation - 17th International Workshop, PATMOS 2007, Proceedings
Pages474-484
Number of pages11
StatePublished - Dec 1 2007
Event17th International Workshop on Power and Timing Modeling, Optimization and Simulation, PATMOS 2007 - Gothenburg, Sweden
Duration: Sep 3 2007Sep 5 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4644 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Workshop on Power and Timing Modeling, Optimization and Simulation, PATMOS 2007
CountrySweden
CityGothenburg
Period9/3/079/5/07

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    Butzen, P. F., Reis, A. I., Kim, C. H., & Ribas, R. P. (2007). Subthreshold leakage modeling and estimation of general CMOS complex gates. In Integrated Circuit and System Design: Power and Timing Modeling, Optimization and Simulation - 17th International Workshop, PATMOS 2007, Proceedings (pp. 474-484). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4644 LNCS).