Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Fast statistical timing analysis handling arbitrary delay correlations
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
Toward stochastic design for digital circuits: statistical static timing analysis
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
On Statistical Timing Analysis with Inter- and Intra-Die Variations
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Statistical Timing Analysis using Levelized Covariance Propagation
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Statistical Timing Analysis with Extended Pseudo-Canonical Timing Model
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Proceedings of the 42nd annual Design Automation Conference
Correlation-aware statistical timing analysis with non-gaussian delay distributions
Proceedings of the 42nd annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model
Proceedings of the 42nd annual Design Automation Conference
A general framework for accurate statistical timing analysis considering correlations
Proceedings of the 42nd annual Design Automation Conference
Asymptotic probability extraction for non-normal distributions of circuit performance
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Parameterized block-based non-gaussian statistical gate timing analysis
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Block based statistical timing analysis with extended canonical timing model
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Non-gaussian statistical parameter modeling for SSTA with confidence interval analysis
Proceedings of the 2006 international symposium on Physical design
Non-gaussian statistical interconnect timing analysis
Proceedings of the conference on Design, automation and test in Europe: Proceedings
A framework for statistical timing analysis using non-linear delay and slew models
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
An accurate sparse matrix based framework for statistical static timing analysis
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Non-linear statistical static timing analysis for non-Gaussian variation sources
Proceedings of the 44th annual Design Automation Conference
Non-Gaussian statistical timing analysis using second-order polynomial fitting
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Reversible statistical max/min operation: concept and applications to timing
Proceedings of the 49th Annual Design Automation Conference
Journal of Electronic Testing: Theory and Applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
| Hi-index | 0.03 |
For nanometer manufacturing, process variation causes significant uncertainty for circuit performance verification. Statistical static timing analysis (SSTA) is thus developed to estimate timing distribution under process variation. Most existing SSTA techniques have difficulty in handling the non-Gaussian variation distribution and nonlinear dependence of delay on variation sources. To address this problem, we first propose a new method to approximate the max operation of two non-Gaussian random variables through second-order polynomial fitting. With such approximation, we then present new non-Gaussian SSTA algorithms for three delay models: quadratic model, quadratic model without crossing terms (semiquadratic model), and linear model. All the atomic operations (max and sum) of our algorithms are performed by closed-form formulas; hence, they scale well for large designs. Experimental results show that compared to the Monte Carlo simulation, our approach predicts the mean, standard deviation, skewness, and 95% percentile point within 1%, 1%, 6%, and 1% error, respectively.