Name
Abstract | ||
|---|---|---|
Process variations have a growing impact on circuit performance for today's integrated circuit (IC) technologies. The non-Gaussian delay distributions as well as the correlations among delays make statistical timing analysis more challenging than ever. In this paper, the authors presented an efficient block-based statistical timing analysis approach with linear complexity with respect to the circuit size, which can accurately predict non-Gaussian delay distributions from realistic nonlinear gate and interconnect delay models. This approach accounts for all correlations, from manufacturing process dependence, to re-convergent circuit paths to produce more accurate statistical timing predictions. With this approach, circuit designers can have increased confidence in the variation estimates, at a low additional computation cost. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/DAC.2005.193777 | DAC |
Keywords | Field | DocType |
circuit size,circuit reliability,integrated circuit technology,accurate statistical timing prediction,nongaussian delay distributions,linear complexity,integrated circuit modelling,statistical analysis,non-gaussian delay distribution,circuit optimisation,design aids,circuit complexity,correlation-aware statistical timing analysis,integrated circuit design,delay model,integrated circuit,process variations,network analysis,circuit path,circuit performance,approach account,electronic engineering computing,analysis approach,circuit designer,circuit design,algorithm design and analysis,process variation,predictive models,propagation delay | Delay calculation,Statistical static timing analysis,Circuit complexity,Computer science,Circuit reliability,Real-time computing,Electronic engineering,Integrated circuit design,Static timing analysis,Process variation,Network analysis | Conference |
ISSN | ISBN | Citations |
0738-100X | 1-59593-058-2 | 102 |
PageRank | References | Authors |
4.40 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaping Zhan | 1 | 171 | 8.85 |
| Andrzej J. Strojwas | 2 | 465 | 50.68 |
| Xin Li | 3 | 709 | 48.36 |
| Lawrence T. Pileggi | 4 | 1886 | 204.82 |
| David Newmark | 5 | 143 | 6.59 |
| Mahesh Sharma | 6 | 105 | 4.86 |