Sonia Singhal
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sonia Singhal has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Amith Singhee
Carnegie Mellon University, Pittsburgh, PA, USA
1 shared publication - Rob A. Rutenbar
Carnegie Mellon University
1 shared publication
Latest Publications
- 1 March 2008
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15301591,p. 856-861
Exploiting Correlation Kernels for Ef£cient Handling of Intra-Die Spatial Correlation, with Application to Statistical Timing
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 Leld 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 Loeve 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 100 x.
- 1 March 2008
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15301591,p. 856-861
Exploiting Correlation Kernels for Ef£cient Handling of Intra-Die Spatial Correlation, with Application to Statistical Timing
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 Leld 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 Loeve 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 100 x.