A statistical static timing analysis considering correlations between delays
Proceedings of the 2001 Asia and South Pacific Design Automation Conference
STAC: statistical timing analysis with correlation
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
Statistical Timing Analysis for Intra-Die Process Variations with Spatial Correlations
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Robust extraction of spatial correlation
Proceedings of the 2006 international symposium on Physical design
Non-gaussian statistical parameter modeling for SSTA with confidence interval analysis
Proceedings of the 2006 international symposium on Physical design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Accurate and analytical statistical spatial correlation modeling for VLSI DFM applications
Proceedings of the 45th annual Design Automation Conference
Statistical timing analysis using bounds and selective enumeration
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Correlation-Preserved Statistical Timing With a Quadratic Form of Gaussian Variables
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Design and Analysis of a Robust Carbon Nanotube-Based Asynchronous Primitive Circuit
ACM Journal on Emerging Technologies in Computing Systems (JETC)
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With the significant advancement of statistical timing and yield analysis algorithms, there is a strong need for accurate and analytical spatial correlation models. In this paper, we propose a novel spatial correlation modeling method that can not only capture the general spatial correlation relationship but also can generate highly accurate and analytical models. Our method, based on singular value decomposition, can generate sequences of polynomial weighted by the singular values. Experimental results from foundry measurement data show that our proposed approach is 3x accuracy improvement over several distance based spatial correlation modeling methods.