A big-data approach to handle process variations: Uncertainty quantification by tensor recovery
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6. SPI.pdf
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Author(s)
Zhang, Zheng
Weng, Tsui-Wei
Daniel, Luca
Date Issued
June 2016
Journal
Proceedings of the 2016 IEEE 20th Workshop on Signal and Power Integrity (SPI)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Zheng, Tsui-Wei Weng, and Luca Daniel. “A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery.” 2016 IEEE 20th Workshop on Signal and Power Integrity (SPI), 8-11 May 2016, Turin Italy, IEEE, 2016.
Version
Author's final manuscript
Abstract
Stochastic spectral methods have become a popular technique to quantify the uncertainties of nano-scale devices and circuits. They are much more efficient than Monte Carlo for certain design cases with a small number of random parameters. However, their computational cost significantly increases as the number of random parameters increases. This paper presents a big-data approach to solve high-dimensional uncertainty quantification problems. Specifically, we simulate integrated circuits and MEMS at only a small number of quadrature samples; then, a huge number of (e.g., 1.5×1027) solution samples are estimated from the available small-size (e.g., 500) solution samples via a low-rank and tensor-recovery method. Numerical results show that our algorithm can easily extend the applicability of tensor-product stochastic collocation to IC and MEMS problems with over 50 random parameters, whereas the traditional algorithm can only handle several random parameters.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/SaPIW.2016.7496314
