A Compact High-Dimensional Yield Analysis Method using Low-Rank Tensor Approximation

Xiao Shi; Hao Yan; Qiancun Huang; Chengzhen Xuan; Longxing Shi

doi:10.1145/3483941

journal article Nov 02, 2021

A Compact High-Dimensional Yield Analysis Method using Low-Rank Tensor Approximation

Xiao Shi

Hao Yan

Qiancun Huang Chengzhen Xuan Longxing Shi

ACM Transactions on Design Automation of Electronic Systems Vol. 27 No. 2 pp. 1-23 · Association for Computing Machinery (ACM)

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Abstract

“Curse of dimensionality” has become the major challenge for existing high-sigma yield analysis methods. In this article, we develop a meta-model using
Low-Rank Tensor Approximation (LRTA)
to substitute expensive SPICE simulation. The polynomial degree of our LRTA model grows linearly with the circuit dimension. This makes it especially promising for high-dimensional circuit problems. Our LRTA meta-model is solved efficiently with a robust greedy algorithm and calibrated iteratively with a bootstrap-assisted adaptive sampling method. We also develop a novel global sensitivity analysis approach to generate a reduced LRTA meta-model which is more compact. It further accelerates the procedure of model calibration and yield estimation. Experiments on memory and analog circuits validate that the proposed LRTA method outperforms other state-of-the-art approaches in terms of accuracy and efficiency.

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References

30

[1]

10.1137/13091899x

[2]

10.1109/tcad.2019.2925340

[3]

10.5555/1509456.1509533

[4]

Least angle regression

Bradley Efron, Trevor Hastie, Iain Johnstone et al.

The Annals of Statistics 10.1214/009053604000000067

[5]

10.1145/3316781.3317818

[6]

10.1145/1146909.1146930

[7]

10.1016/j.jcp.2016.06.005

[8]

10.5555/1870926.1871123

[9]

Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index

Andrea Saltelli, Paola Annoni, Ivano Azzini et al.

Computer Physics Communications 10.1016/j.cpc.2009.09.018

[10]

Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, and Stefano Tarantola. 2008. Global Sensitivity Analysis: the Primer. John Wiley & Sons.

[11]

10.1145/3195970.3195972

[12]

10.1145/3316781.3317863

[13]

10.1145/3299902.3309748

[14]

10.1109/tcad.2020.2966481

[15]

Xiao Shi, Jinlong Yan, Hao Yan, Jiajia Zhang, Jinxin Wang, Longxing Shi, and Lei He. 2019. Adaptive low-rank tensor approximation for SRAM yield analysis using bootstrap resampling. In Proceedings of the 2019 IEEE 13th International Conference on ASIC (ASICON). IEEE, 1–4.

[16]

10.5555/1266366.1266667

[17]

10.1109/vlsi.2008.54

[18]

Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

I.M Sobol′

Mathematics and Computers in Simulation 10.1016/s0378-4754(00)00270-6

[19]

10.1137/s1064827503424505

[20]

10.1016/j.ress.2007.04.002

[21]

Regression Shrinkage and Selection Via the Lasso

Robert Tibshirani

Journal of the Royal Statistical Society Series B:... 10.1111/j.2517-6161.1996.tb02080.x

[22]

10.1109/tcad.2017.2778061

[23]

10.1109/tvlsi.2016.2601606

[24]

10.1364/oe.23.004242

[25]

10.1145/2872334.2872360

[26]

Wei Wu, Fang Gong, Gengsheng Chen, and Lei He. 2014. A fast and provably bounded failure analysis of memory circuits in high dimensions. In Proceedings of the 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 424–429. 10.1109/aspdac.2014.6742928

[27]

10.1145/2593069.2593202

[28]

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

Dongbin Xiu, George Em Karniadakis

SIAM Journal on Scientific Computing 10.1137/s1064827501387826

[29]

10.1109/tvlsi.2014.2336851

[30]

10.1109/tcad.2014.2369505

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Details

Published: Nov 02, 2021
Vol/Issue: 27(2)
Pages: 1-23
License: View

Authors

X

Xiao Shi