Name
Title | ||
|---|---|---|
A big-data approach to handle process variations: Uncertainty quantification by tensor recovery |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SaPIW.2016.7496314 | 2016 IEEE 20th Workshop on Signal and Power Integrity (SPI) |
Keywords | Field | DocType |
big-data approach,process variations,tensor recovery,stochastic spectral methods,high-dimensional uncertainty quantification problems,tensor-product stochastic collocation | Small number,Mathematical optimization,Stochastic optimization,Monte Carlo method,Uncertainty quantification,Spectral method,Quadrature (mathematics),Integrated circuit,Mathematics,Collocation | Journal |
Volume | ISSN | Citations |
abs/1603.06119 | 2475-9481 | 1 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Zhang | 1 | 125 | 12.54 |
| Tsui-Wei Weng | 2 | 75 | 7.35 |
| Luca Daniel | 3 | 497 | 50.96 |