Proceedings of the 39th annual Design Automation Conference
Statistical timing for parametric yield prediction of digital integrated circuits
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
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
Non-gaussian statistical parameter modeling for SSTA with confidence interval analysis
Proceedings of the 2006 international symposium on Physical design
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-Gaussian statistical timing analysis using second-order polynomial fitting
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
PiCAP: a parallel and incremental capacitance extraction considering stochastic process variation
Proceedings of the 46th Annual Design Automation Conference
Non-Gaussian statistical timing analysis using second-order polynomial fitting
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 47th Design Automation Conference
Stochastic analog circuit behavior modeling by point estimation method
Proceedings of the 2011 international symposium on Physical design
Process variation effects on circuit performance: TCAD simulation of 256-Mbit technology [DRAMs]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Asymptotic waveform evaluation for timing analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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
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The most challenging problem in the current block-based statistical static timing analysis (SSTA) is how to handle the max operation efficiently and accurately. Existing SSTA techniques suffer from limited modeling capability by using a linear delay model with Gaussian distribution, or have scalability problems due to expensive operations involved to handle non-Gaussian variation sources or nonlinear delays. To overcome these limitations, we propose efficient algorithms to handle the max operation in SSTA with both quadratic delay dependency and non-Gaussian variation sources simultaneously. Based on such algorithms, we develop an SSTA flow with quadratic delay model and non-Gaussian variation sources. All the atomic operations, max and add, are calculated efficiently via either closed-form formulas or low dimension (at most 2-D) lookup tables. We prove that the complexity of our algorithm is linear in both variation sources and circuit sizes, hence our algorithm scales well for large designs. Compared to Monte Carlo simulation for non-Gaussian variation sources and nonlinear delay models, our approach predicts the mean, standard deviation and 95% percentile point with less than 2% error, and the skewness with less than 10% error.