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An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield Analysis

Published: 16 March 2018 Publication History
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  • Abstract

    Yield1 analysis of SRAM is a challenging issue, because the failure rates of SRAM cells are extremely small. In this article, an efficient non-Gaussian sampling method of cross entropy optimization is proposed for estimating the high sigma SRAM yield. Instead of sampling with the Gaussian distribution in existing methods, a non-Gaussian distribution, i.e., a joint one-dimensional generalized Pareto distribution and (n-1)-dimensional Gaussian distribution, is taken as the function family of practical distribution, which is proved to be more suitable to fit the ideal distribution in the view of extreme failure event. To minimize the cross entropy between practical and ideal distributions, a sequential quadratic programing solver with multiple starting points strategy is applied for calculating the optimal parameters of practical distributions. Experimental results show that the proposed non-Gaussian sampling is a 2.2--4.1× speedup over the Gaussian sampling, on the whole, it is about a 1.6--2.3× speedup over state-of-the-art methods with low- and high-dimensional cases without loss of accuracy

    References

    [1]
    Changdao Dong and Xin Li. 2011. Efficient SRAM failure rate prediction via Gibbs sampling. In Proceedings of the Design Automation Conference. 200--205.
    [2]
    Rouwaida Kanj, Rajiv Joshi, and Sani Nassif. 2006. Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events. In Proceedings of the Design Automation Conference, 2006. ACM/IEEE. 69--72.
    [3]
    Lara Dolecek, Masood Qazi, Devavrat Shah, and Anantha Chandrakasan. 2008. Breaking the simulation barrier: SRAM evaluation through norm minimization. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design. 322--329.
    [4]
    Qazi Masood, Tikekar Mehul, Dolecek Lara, Shah Devavrat, Chandrakasan and Anantha. 2010. Loop flattening 8 spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis. In Proceedings of the Conference on Design, Automation and Test in Europe. 801--806.
    [5]
    Mengshuo Wang, Changhao Yan, Xin Li, Dian Zhou, and Xuan Zeng. 2017. High-dimensional and multiple-failure-region importance sampling for SRAM yield analysis. IEEE Trans. Very Large Scale Integr. Syst. 25, 3 (2017), 806--819.
    [6]
    Mohammed Abdul Shahid. 2012. Cross entropy minimization for efficient estimation of SRAM failure rate. In Proceedings of the Design, Automation Test in Europe Conference Exhibition. 230--235.
    [7]
    Fang Gong, Sina Basir-Kazeruni, Lara Dolecek, and Lei He. 2012. A fast estimation of SRAM failure rate using probability collectives. In Proceedings of the ACM International Symposium on International Symposium on Physical Design. 41--48.
    [8]
    Sgibbs2hupeng Sun, Yamei Feng, Changdao Dong, and Xin Li. 2011. Efficient SRAM failure rate prediction via gibbs sampling. IEEE Trans. Comput.-Aid. Des. Integr. Circ. Syst. 31, 12 (2011), 1831--1844.
    [9]
    Jian Yao, Zuochang Ye, and Yan Wang. 2014. Importance boundary sampling for SRAM yield analysis with multiple failure regions. Trans. Comput.-Aid. Des. Integr. Circ. Syst. 31, 12 (2011), 1831--1844.
    [10]
    Shupeng Sun and Xin Li. 2014. Fast statistical analysis of rare circuit failure events via subset simulation in high-dimensional variation space. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design. 324--331.
    [11]
    Trent McConaghy and Patrick Drennan. 2011. Variation-aware custom IC design: Improving PVT and monte carlo analysis for design performance and parametric yield. In Solido White Paper.
    [12]
    Zhenyu Wu, Changhao Yan, Xuan Zeng, and Sheng Guo Wang. 2015. Rapid estimation of the probability of SRAM failure via adaptive multi-level sliding-window statistical method. Integr. VLSI J. 50 (2015), 1--15.
    [13]
    Fang Gong, Hao Yu, Yiyu Shi, Daesoo Kim, Junyan Ren, and Lei He. 2010. quickYield: An efficient global-search based parametric yield estimation with performance constraints. In Proceedings of the Design Automation Conference. 392--397.
    [14]
    Chenjie Gu and Jaijeet Roychowdhury. 2008. An efficient, fully nonlinear, variability-aware non-monte-carlo yield estimation procedure with applications to SRAM cells and ring oscillators. In Proceedings of the Asia and South Pacific Design Automation Conference (ASPDAC’08). 754--761.
    [15]
    Shweta Srivastava and Jaijeet Roychowdhury. 2007. Rapid estimation of the probability of SRAM failure due to MOS threshold variations. In Proceedings of the IEEE Custom Integrated Circuits Conference. 229--232.
    [16]
    R. A. Fonseca, L. Dilillo, A. Bosio, P. Girard, S. Pravossoudovitch, A. Virazel, and N. Badereddine. 2010. A statistical simulation method for reliability analysis of SRAM core-cells. In Proceedings of the Design Automation Conference. 853--856.
    [17]
    Fparametric_yieldang Gong, Yiyu Shi, Hao Yu, and Lei He. 2010. Parametric yield estimation for SRAM cells: Concepts, algorithms and challenges. In Proceedings of the Design Automation Conference.
    [18]
    Amith Singhee and Rob A. Rutenbar. 2007. Statistical blockade: A novel method for very fast monte carlo simulation of rare circuit events, and its application. In Proceedings of the Conference on Design, Automation and Test in Europe. 1379--1384.
    [19]
    Amith Singhee and Rob A. Rutenbar. 2009. Statistical blockade: Very fast statistical simulation and modeling of rare circuit events and its application to memory design. IEEE Trans. Comput.-Aid. Des. Integr. Circ. Syst. 28, 8 (2009), 1176--1189.
    [20]
    A. Papoulis and S. Pillai, 2001, Probability, Random Variables and Stochastic Processe. McGraw--Hill, New York, NY.
    [21]
    Tomas M. Cover and Joy A. Tomas. 2003. Elements of Information Theory. Wiley. 1600--1601.
    [22]
    A. A. Balkema and L. De Haan. 1974. Residual Life Time at Great Age. Ann. Probab. 2, 5 (1974), 792--804.
    [23]
    James Pickands. 1975. Statistical Inference Using Extreme Order Statistics. Ann. Stat. 3, 1 (1975), 119--131.
    [24]
    Paul T. Boggs and Jon W. Tolle. 1995. Sequential quadratic Programming. Acta Numer. 4, 4 (1995), 1--51
    [25]
    Alston S Householder. 1958. Unitary Triangularization of a Nonsymmetric Matrix. J. ACM 5, 4 (1958), 339--342.
    [26]
    Bo Peng, Fan Yang, Changhao Yan, and Xuan Zeng. 2016. Efficient multiple starting point optimization for automated analog circuit optimization via recycling simulation data. In Design, Automation Test in Europe Conference Exhibition. 1417--1422.
    [27]
    N. L. Johnson, S. Kotz, and N. Balakrishnan. 1994. Continuous Univariate Distributions, Volume 1. John Wiley 8 Sons. 173.
    [28]
    Zhang Jizhe, and S. Gupta. 2014. SRAM array yield estimation under spatially-correlated process variation. In Proceedings of the IEEE Test Symposium. 149--155.
    [29]
    Winterioraltz R. A., J. L. Morales, J. Nocedal, and D. Orban. 2006. An interior algorithm for nonlinear optimization that combines line search and trust region steps. Math. Program. 107, 3 (2006), 391--408.

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      Published In

      cover image ACM Transactions on Design Automation of Electronic Systems
      ACM Transactions on Design Automation of Electronic Systems  Volume 23, Issue 3
      May 2018
      341 pages
      ISSN:1084-4309
      EISSN:1557-7309
      DOI:10.1145/3184476
      • Editor:
      • Naehyuck Chang
      Issue’s Table of Contents
      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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      Publication History

      Published: 16 March 2018
      Accepted: 01 December 2017
      Received: 01 July 2017
      Published in TODAES Volume 23, Issue 3

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      Author Tags

      1. Failure rate
      2. SRAM
      3. cross entropy minimization
      4. generalized pareto distribution

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      • Research-article
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      • Refereed

      Funding Sources

      • National Major Science and Technology Special Project of China
      • National Science Foundation (NSF)
      • National Natural Science Foundation of China (NSFC) research projects
      • Recruitment Program of Global Experts (the Thousand Talents Plan)
      • NSF
      • National Key Research and Development Program of China

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      Cited By

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      • (2023)CNNFlow: Memory-driven Data Flow Optimization for Convolutional Neural NetworksACM Transactions on Design Automation of Electronic Systems10.1145/357701728:3(1-36)Online publication date: 19-Mar-2023
      • (2023)Inferencing on Edge Devices: A Time- and Space-aware Co-scheduling ApproachACM Transactions on Design Automation of Electronic Systems10.1145/357619728:3(1-33)Online publication date: 19-Mar-2023
      • (2023)DDAM: Data Distribution-Aware Mapping of CNNs on Processing-In-Memory SystemsACM Transactions on Design Automation of Electronic Systems10.1145/357619628:3(1-30)Online publication date: 19-Mar-2023
      • (2023)Accelerating Graph Computations on 3D NoC-Enabled PIM ArchitecturesACM Transactions on Design Automation of Electronic Systems10.1145/356429028:3(1-16)Online publication date: 19-Mar-2023
      • (2022)A Switching NMOS Based Single Ended Sense Amplifier for High Density SRAM ApplicationsACM Transactions on Design Automation of Electronic Systems10.1145/357619828:3(1-14)Online publication date: 12-Dec-2022
      • (2022)GANDSE: Generative Adversarial Network-based Design Space Exploration for Neural Network Accelerator DesignACM Transactions on Design Automation of Electronic Systems10.1145/357092628:3(1-20)Online publication date: 9-Nov-2022
      • (2022)Efficient bayesian yield analysis and optimization with active learningProceedings of the 59th ACM/IEEE Design Automation Conference10.1145/3489517.3530607(1195-1200)Online publication date: 10-Jul-2022

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