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Efficient smart sampling based full-chip leakage analysis for intra-die variation considering state dependence

Published: 26 July 2009 Publication History

Abstract

Leakage power minimization is critical to semiconductor design in nanoscale CMOS. On the other hand increasing variability with scaling adds complexity to the leakage analysis problem. In this work we seek to achieve tractability in Monte Carlo-based statistical leakage analysis. A novel approach for fast and accurate statistical leakage analysis considering inter-die and intra-die components is proposed. We show that the optimal way to select samples, to capture intra-die variation accurately, is according to the probability distribution function of total process variation. Intelligent selection of samples is performed using a Quasi Monte Carlo technique. Results are presented for benchmarks with sizes varying from approximately 5,000 to 200,000 gates. The largest benchmark with 198461 gates is evaluated in 3 minutes with the proposed approach compared to 23 hours for random sampling with comparable accuracy. Compared to a conventional analytical approach using Wilkinson's approximation, the proposed technique offers superior accuracy while maintaining efficiency. State dependence and multiple sources of variation are considered and the approach is scalable with number of process parameter variables for standard cell characterization cost. We also show reduction in sample size to meet target accuracy for computing leakage distribution due to the inter-die component only when compared to random selection of samples.

References

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Cited By

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  • (2024)A Leakage Analysis Methodology Considering Intra-Cell and Inter-Cell Layout Dependent Effect2024 International VLSI Symposium on Technology, Systems and Applications (VLSI TSA)10.1109/VLSITSA60681.2024.10546410(1-4)Online publication date: 22-Apr-2024
  • (2020)Additive Statistical Leakage Analysis Using Exponential Mixture ModelIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.2975154(1-1)Online publication date: 2020
  • (2018)Estimation of Leakage Distribution Utilizing Gaussian Mixture Model2018 International SoC Design Conference (ISOCC)10.1109/ISOCC.2018.8649978(149-150)Online publication date: Nov-2018
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  1. Efficient smart sampling based full-chip leakage analysis for intra-die variation considering state dependence

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      cover image ACM Conferences
      DAC '09: Proceedings of the 46th Annual Design Automation Conference
      July 2009
      994 pages
      ISBN:9781605584973
      DOI:10.1145/1629911
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 26 July 2009

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      Author Tags

      1. Monte Carlo
      2. statistical leakage
      3. variance reduction

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      DAC '09: The 46th Annual Design Automation Conference 2009
      July 26 - 31, 2009
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      • (2024)A Leakage Analysis Methodology Considering Intra-Cell and Inter-Cell Layout Dependent Effect2024 International VLSI Symposium on Technology, Systems and Applications (VLSI TSA)10.1109/VLSITSA60681.2024.10546410(1-4)Online publication date: 22-Apr-2024
      • (2020)Additive Statistical Leakage Analysis Using Exponential Mixture ModelIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.2975154(1-1)Online publication date: 2020
      • (2018)Estimation of Leakage Distribution Utilizing Gaussian Mixture Model2018 International SoC Design Conference (ISOCC)10.1109/ISOCC.2018.8649978(149-150)Online publication date: Nov-2018
      • (2018)Statistical Leakage Analysis Using Gaussian Mixture ModelIEEE Access10.1109/ACCESS.2018.28705286(51939-51950)Online publication date: 2018
      • (2017)Variability aware transistor stack based regression surrogate models for accurate and efficient statistical leakage estimationMicroelectronics Journal10.1016/j.mejo.2017.05.01569(1-19)Online publication date: Nov-2017
      • (2014)Robust Optimization for Gate Sizing Considering Non-Gaussian Local VariationsApplied Mathematics10.4236/am.2014.51624505:16(2558-2569)Online publication date: 2014
      • (2014)SMV methodology enhancements for high speed I/O links of SoCs2014 IEEE 32nd VLSI Test Symposium (VTS)10.1109/VTS.2014.6818767(1-5)Online publication date: Apr-2014
      • (2014)Efficient post-silicon validation via segmentation of process variation envelope — Global vs local variationsFifteenth International Symposium on Quality Electronic Design10.1109/ISQED.2014.6783314(115-122)Online publication date: Mar-2014
      • (2014)Hybrid Gate-Level Leakage Model for Monte Carlo Analysis on Multiple GPUsIEEE Access10.1109/ACCESS.2014.23089222(183-194)Online publication date: 2014
      • (2013)A Literature Review on Sampling Techniques in Semiconductor ManufacturingIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2013.225694326:2(188-195)Online publication date: May-2013
      • Show More Cited By

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