Paper Info
Title
A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications.
Abstract
Fabrication process variations are a major source of yield degradation in the nano-scale design of integrated circuits (IC), microelectromechanical systems (MEMS) and photonic circuits. Stochastic spectral methods are a promising technique to quantify the uncertainties caused by process variations. Despite their superior efficiency over Monte Carlo for many design cases, these algorithms suffer from the curse of dimensionality; i.e., their computational cost grows very fast as the number of random parameters increases. In order to solve this challenging problem, this paper presents a high-dimensional uncertainty quantification algorithm from a big-data perspective. Specifically, we show that the huge number of (e.g., $1.5 times 10^{27}$) simulation samples in standard stochastic collocation can be reduced to a very small one (e.g., $500$) by exploiting some hidden structures of a high-dimensional data array. This idea is formulated as a tensor recovery problem with sparse and low-rank constraints; and it is solved with an alternating minimization approach. Numerical results show that our approach can simulate efficiently some ICs, as well as MEMS and photonic problems with over 50 independent random parameters, whereas the traditional algorithm can only handle several random parameters.
Year
Venue
Field
2016
arXiv: Computational Engineering, Finance, and Science
Uncertainty quantification,Tensor,Computer science,Minification,Artificial intelligence,Array data type,Spectral method,Integrated circuit,Mathematical optimization,Monte Carlo method,Algorithm,Curse of dimensionality,Machine learning
DocType
Volume
ISSN
Journal
abs/1611.02256
IEEE Transactions on Component, Packaging and Manufacturing Technology, 2017
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Zheng Zhang112512.54
Tsui-Wei Weng2757.35
Luca Daniel349750.96