Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
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
High-performance CMOS variability in the 65-nm regime and beyond
IBM Journal of Research and Development - Advanced silicon technology
Analysis and modeling of CD variation for statistical static timing
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 46th Annual Design Automation Conference
On confidence in characterization and application of variation models
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Hybrid modeling of non-stationary process variations
Proceedings of the 48th Design Automation Conference
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A procedure that decomposes measured parametric device variation into systematic and random components is studied by considering the decomposition process as selecting the most suitable model for describing on-die spatial variation trend. In order to maximize model predictability, the log-likelihood estimate called corrected Akaike information criterion is adopted. Depending on on-die contours of underlying systematic variation, necessary and sufficient complexity of the systematic regression model is objectively and adaptively determined. The proposed procedure is applied to 90-nm threshold voltage data and found the low order polynomials describe systematic variaiation very well. Designing cost-effective variation monitoring circuits as well as appropriate model determination of on-die variation are hence facilitated.