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Fast Statistical Analysis Using Machine Learning

  • Rouwaida KanjEmail author
  • Rajiv V. Joshi
  • Lama Shaer
  • Ali Chehab
  • Maria Malik
Chapter

Abstract

In this chapter, we describe a fast statistical yield analysis methodology for memory design. At the heart of its engine is a mixture importance sampling-based methodology which comprises a uniform sampling stage and an importance sampling stage. Logistic regression-based machine learning techniques are employed for modeling the circuit response and speeding up the importance sample points simulations. To avoid overfitting, we rely on a cross-validation-based regularization framework for ordered feature selection. The methodology is comprehensive and computationally efficient. We demonstrate the methodology on an industrial state-of-the-art 14 nm FinFET SRAM design with write-assist circuitry. The results corroborate well with hardware and with the fully circuit-simulation-based approach.

Notes

Acknowledgements

The authors would like to thank the Maroun Semaan Faculty of Engineering and Architecture at the American University of Beirut for supporting Ph.D. student Miss Lama Shaer.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rouwaida Kanj
    • 1
    Email author
  • Rajiv V. Joshi
    • 2
  • Lama Shaer
    • 1
  • Ali Chehab
    • 1
  • Maria Malik
    • 3
  1. 1.Maroun Semaan Faculty of Engineering and ArchitectureAmerican University of BeirutBeirutLebanon
  2. 2.IBM TJ Watson LabsYorktown HeightsUSA
  3. 3.George Mason UniversityFairfaxUSA

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