The University of Southampton
University of Southampton Institutional Repository

Statistical power analysis for nanoscale CMOS

Statistical power analysis for nanoscale CMOS
Statistical power analysis for nanoscale CMOS
With the scaling down of CMOS technology, process variations are becoming significant. Power consumption is a major constraint on IC yield. However, there has been little research on statistical power analysis compared with that on timing analysis. Here, both the static and dynamic power are considered. We characterize a cell library containing mean power. A standard deviation power library is extracted from Monte Carlo simulations. Then, the mean and variance of the power are derived. The proposed technique is validated on benchmark circuits at 35 nm. We compare the results with SPICE simulations and show that the difference is acceptable.
Practical, Theoretical or Mathematical/ CMOS integrated circuits, Monte Carlo methods, nanoelectronics, power integrated circuits, statistical analysis/ statistical power analysis, nanoscale CMOS, power consumption, static power, dynamic power, cell library, mean power, Monte Carlo simulation, size 35 nm/ B2570D CMOS integrated circuits, B2570P Power integrated circuits, B0240G Monte Carlo methods/ size 3.5E-08 m
201-204
Wang, Yangang
84511804-36d1-44c7-90d4-a18201735a08
Merrett, M.
5054c490-7dad-45ba-a937-88a80c11abcf
Zwolinski, M.
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Wang, Yangang
84511804-36d1-44c7-90d4-a18201735a08
Merrett, M.
5054c490-7dad-45ba-a937-88a80c11abcf
Zwolinski, M.
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0

Wang, Yangang, Merrett, M. and Zwolinski, M. (2010) Statistical power analysis for nanoscale CMOS. 2010 International Conference on Signals and Electronic Systems (ICSES). pp. 201-204 .

Record type: Conference or Workshop Item (Other)

Abstract

With the scaling down of CMOS technology, process variations are becoming significant. Power consumption is a major constraint on IC yield. However, there has been little research on statistical power analysis compared with that on timing analysis. Here, both the static and dynamic power are considered. We characterize a cell library containing mean power. A standard deviation power library is extracted from Monte Carlo simulations. Then, the mean and variance of the power are derived. The proposed technique is validated on benchmark circuits at 35 nm. We compare the results with SPICE simulations and show that the difference is acceptable.

Text
SPA.pdf - Author's Original
Download (760kB)

More information

Published date: September 2010
Additional Information: 2010 International Conference on Signals and Electronic Systems (ICSES), 7-10 September 2010, Gliwice, Poland
Venue - Dates: 2010 International Conference on Signals and Electronic Systems (ICSES), 2010-09-01
Keywords: Practical, Theoretical or Mathematical/ CMOS integrated circuits, Monte Carlo methods, nanoelectronics, power integrated circuits, statistical analysis/ statistical power analysis, nanoscale CMOS, power consumption, static power, dynamic power, cell library, mean power, Monte Carlo simulation, size 35 nm/ B2570D CMOS integrated circuits, B2570P Power integrated circuits, B0240G Monte Carlo methods/ size 3.5E-08 m
Organisations: EEE

Identifiers

Local EPrints ID: 272299
URI: http://eprints.soton.ac.uk/id/eprint/272299
PURE UUID: eb512c39-135d-4bc9-aa86-bc0c5f22074e
ORCID for M. Zwolinski: ORCID iD orcid.org/0000-0002-2230-625X

Catalogue record

Date deposited: 17 May 2011 17:13
Last modified: 15 Mar 2024 02:39

Export record

Contributors

Author: Yangang Wang
Author: M. Merrett
Author: M. Zwolinski ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×