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Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage

Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage. Tao Li Zhiping Yu Institute of Microelectronics Tsinghua University. Outline. Motivation Junction Tunneling Leakage – Circuit Level Analysis Simple inverter Multi-input gate

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Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage

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  1. Statistical Full-Chip Leakage Analysis Considering Junction Tunneling Leakage Tao Li Zhiping Yu Institute of MicroelectronicsTsinghua University

  2. Outline • Motivation • Junction Tunneling Leakage – Circuit Level Analysis • Simple inverter • Multi-input gate • Statistical Full-Chip Leakage Analysis Technique • Modeling of process-induced parameter variations • PCA & ICA • Sum of leakage components • Experimental Results • Summary

  3. 0.18 μm 0.09 μm 65 nm 250 Leakage Power Active Power 200 150 Power (W) 100 50 0 0.1 0.25 0.18 0.13 Leakage and Process Variations • Leakage power becomes a major component ofthe total power. • Process variation has a significant impact on leakage. Feature Size Scale Down

  4. Major Leakage components • Subthreshold leakage • Gate oxide leakage • Junction tunneling leakage Gate Leakage Igate Subthreshold Leakage Isub Gate Source Drain n+ n+ Bulk Junction tunneling leakage

  5. Overview of Related Works • Previous works on statistical full-chip leakage computation • Computation of PDF of full-chip leakage • Approximate process variations asGaussian distributions • Finding full-chip leakage by summing upindependent lognormals • R. Rao ISLPED03,H. Chang ICCAD 03,H. Chang DAC05, X. Li DAC06, et al. • Most of the previous works ignored • Effect of Non-Gaussian parameters • Junction tunneling leakage

  6. Outline • Motivation • Junction tunneling leakage – Circuit level analysis • Simple inverter • Multi input gate • Statistical Full-chip leakage analysis technique • Modeling of process-induced parameter variations • PCA & ICA • Sum of leakage components • Experimental Results • Summary

  7. 0 Simple Inverter • When input = 0V • NMOS: maximum & • Can be independently calculated and added for total leakage • PMOS: gate oxide leakage – small and ignored • When input = • NMOS: gate oxide leakage • PMOS: subthreshold leakage and junction tunneling leakage 0

  8. Multi input gate: general approach • If all inputs have a high state • Analysis is similar to the that of the inverter • At least one input is low • Combination of , ,and • Approach: distinguish 6 different scenarios

  9. Computation of Total Chip Leakage • Total leakage current of a chip: : probability of input vector state i of the jth gate can be either the leakage for a fixed input vector or the average leakage current • Input pattern independent approach • Direct computation: 2k input vector states for a k-input gate • Applying dominant states of • Leakage of stack at state i is not always independent • Interactions ofIsub,Igateand Ijunc need to be considered • Analyzing leakage current of stack by input state

  10. Dominant States of Leakage Current • Interaction between Isub and Igate • Case (a) (c): dominate states of Igate NMOS-Transistor Stack D. Lee et. al. at DAC03 C. Oh et. al. at DAC99 D. Lee et. al. at DAC03 • Case (a) (b): dominate states of Isub • Dominant states of junction tunneling leakage Ijunc • States with the “on” transistors connected to the output node(stack effect ) • Only k dominant states for a k-input gate

  11. Results: Leakage estimation for 4-NAND • The error of the proposed analysis method over SPICE • Average ~1.5% over all input states • Maximum error = 4.5% @1110

  12. Outline • Motivation • Junction tunneling leakage – Circuit level analysis • Simple inverter • Multi input gate • Statistical Full-chip leakage analysis technique • Modeling of process-induced parameter variations • PCA & ICA • Sum of leakage components • Experimental Results • Summary

  13. Proposed Analysis Method Highlights Non-Gaussian and Gaussian variables transformed to independent basis with PCA/ICA Incorporates both Gaussian and non-Gaussian parameters Inputs are moments of varying process parameters Easier to obtain moments from process data files Three kinds of leakage components are considered Fast algorithm for the sum up of leakage components Moments matching-based PDF/CDF extraction Uses closed form PDF/CDF expressions

  14. Outline • Motivation • Junction tunneling leakage – Circuit level analysis • Simple inverter • Multi input gate • Statistical Full-chip leakage analysis technique • Modeling of process-induced parameter variations • PCA & ICA • Sum of leakage components • Experimental Results • Summary

  15. Experimental Results • Comparison of our results with Monte Carlo simulations • Comparison with Gaussian modeling of parameters

  16. Outline • Motivation • Junction tunneling leakage – Circuit level analysis • Simple inverter • Multi input gate • Statistical Full-chip leakage analysis technique • Modeling of process-induced parameter variations • PCA & ICA • Sum of leakage components • Experimental Results • Summary

  17. Summary • A fast approach to compute total leakage current • Considering , ,and • Average error 1.5% • Both Gaussian and Non-Gaussian parameters are considered • PCA and ICA are employed as preprocessing steps • Sum the leakage to get a final result • Algorithm has a complexity of

  18. Thanks!

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