Numerical Initial Value Problems in Ordinary Differential Equations
Numerical Initial Value Problems in Ordinary Differential Equations
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Correlation-aware statistical timing analysis with non-gaussian delay distributions
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Fast statistical circuit analysis with finite-point based transistor model
Proceedings of the conference on Design, automation and test in Europe
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A taylor series methodology for analyzing the effects of process variation on circuit operation
Proceedings of the 19th ACM Great Lakes symposium on VLSI
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The dynamic behavior of a VLSI circuit can be described by a system of differential-algebraic equations. When some circuit elements are affected by process variations, the dynamic behavior of the circuit will deviate from its nominal trajectory. Monte-Carlo-type random sampling methods are widely used to estimate the trajectory deviation. However they can be quite time-consuming when the dimension of the parameter space is large. This paper offers an alternative solution by casting the problem into the theoretic frame work of non-linear non-Gaussian filtering. To estimate the mean and variance of the time-dependent circuit trajectory, we develop a method based on unscented transformation, which is an efficient Bayesian analysis sampling technique. Theoretically the method has linear runtime complexity. Experimental results show that compared to traditional Monte-Carlo methods, the new method can achieve over 10x speedup with less than 2% error.