Name
Title | ||
|---|---|---|
Exploiting correlation kernels for efficient handling of intra-die spatial correlation, with application to statistical timing |
Abstract | ||
|---|---|---|
Intra-die manufacturing variations are unavoidable in nanoscale processes. These variations often exhibit strong spatial correlation. Standard grid-based models assume model parameters (grid-size, regularity) in an ad hoc manner and can have high measurement cost. The random field model overcomes these issues. However, no general algorithm has been proposed for the practical use of this model in statistical CAD tools. In this paper, we propose a robust and efficient numerical method, based on the Galerkin technique and Karhunen Loéve Expansion, that enables effective use of the model. We test the effectiveness of the technique using a Monte Carlo-based Statistical Static Timing Analysis algorithm, and see errors less than 0.7%, while reducing the number of random variables from thousands to 25, resulting in speedups of up to 100x. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1403375.1403583 | Proceedings of the conference on Design, automation and test in Europe |
Keywords | Field | DocType |
numerical method,random field,monte carlo method,monte carlo,robustness,testing,nanotechnology,generic algorithm,monte carlo methods,galerkin method,kernel,spatial correlation,random variable,random variables,algorithm design and analysis | Mathematical optimization,Monte Carlo method,Random variable,Statistical static timing analysis,Spatial correlation,Random field,Algorithm design,Computer science,Parallel computing,Algorithm,Robustness (computer science),Grid | Conference |
ISSN | ISBN | Citations |
1530-1591 | 978-3-9810801-4-8 | 3 |
PageRank | References | Authors |
0.47 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amith Singhee | 1 | 347 | 22.94 |
| Sonia Singhal | 2 | 34 | 2.31 |
| Rob A. Rutenbar | 3 | 2283 | 280.48 |