An SQP method for general nonlinear programs using only equality constrained subproblems
Mathematical Programming: Series A and B
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Modeling Within-Die Spatial Correlation Effects for Process-Design Co-Optimization
ISQED '05 Proceedings of the 6th International Symposium on Quality of Electronic Design
Circuit optimization using statistical static timing analysis
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Refined statistical static timing analysis through
Proceedings of the 43rd annual Design Automation Conference
Statistical timing based on incomplete probabilistic descriptions of parameter uncertainty
Proceedings of the 43rd annual Design Automation Conference
Robust estimation of parametric yield under limited descriptions of uncertainty
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Variations, margins, and statistics
Proceedings of the 2008 international symposium on Physical design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
In recent years, a large body of statistical static timing analysis and statistical circuit optimization techniques have emerged, providing important avenues to account for the increasing process variations in design. The realization of these statistical methods often demands the availability of statistical process variation models whose accuracy, however, is severely hampered by limitations in test structure design, test time and various sources of inaccuracy inevitably incurred in process characterization. Consequently, it is desired that statistical circuit analysis and optimization can be conducted based upon imprecise statistical variation models. In this paper, we present an efficient importance sampling based optimization framework that can translate the uncertainty in the process models to the uncertainty in parametric yield, thus offering the very much desired statistical best/worst-case circuit analysis capability accounting for unavoidable complexity in process characterization. Unlike the previously proposed statistical learning and probabilistic interval based techniques, our new technique efficiently computes tight bounds of the parametric circuit yields based upon bounds of statistical process model parameters while fully capturing correlation between various process variations. Furthermore, our new technique provides valuable guidance to process characterization. Examples are included to demonstrate the application of our general analysis framework under the context of statistical static timing analysis.