A general probabilistic framework for worst case timing analysis
Proceedings of the 39th annual Design Automation Conference
Proceedings of the 39th annual Design Automation Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Block-based Static Timing Analysis with Uncertainty
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
"AU: Timing Analysis Under Uncertainty
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
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
Statistical delay computation considering spatial correlations
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Statistical timing analysis using bounds and selective enumeration
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical gate delay model for multiple input switching
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Statistical static timing analysis: A survey
Integration, the VLSI Journal
A Gaussian mixture model for statistical timing analysis
Proceedings of the 46th Annual Design Automation Conference
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Statistical static timing analysis (SSTA) is emerging as a solution for predicting the timing characteristics of digital circuits under process variability. For computing the statistical max of two arrival time probability distributions, existing analytical SSTA approaches use the results given by Clark in [8]. These analytical results are exact when the two operand arrival time distributions have jointly Gaussian distributions. Due to the nonlinear max operation, arrival time distributions are typically skewed. Furthermore, nonlinear dependence of gate delays and non-gaussian process parameters also make the arrival time distributions asymmetric. Therefore, for computing the max accurately, a new approach is required that accounts for the inherent skewness in arrival time distributions. In this work, we present analytical solution for computing the statistical max operation.1 First, the skewness in arrival time distribution is modeled by matching its first three moments to a so-called skewed normal distribution. Then by extending Clark's work to handle skewed normal distributions we derive analytical expressions for computing the moments of the max. We then show using initial simulations results that using a skewness based max operation has a significant potential to improve the accuracy of the statistical max operation in SSTA while retaining its computational efficiency.