Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th 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
Statistical delay computation considering spatial correlations
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Prediction of leakage power under process uncertainties
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Accurate and analytical statistical spatial correlation modeling for VLSI DFM applications
Proceedings of the 45th annual Design Automation Conference
Characterizing Intra-Die Spatial Correlation Using Spectral Density Method
ISQED '08 Proceedings of the 9th international symposium on Quality Electronic Design
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the conference on Design, automation and test in Europe
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Proceedings of the 46th Annual Design Automation Conference
Design for Manufacturability and Statistical Design: A Constructive Approach
Design for Manufacturability and Statistical Design: A Constructive Approach
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
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Accurate characterization of spatial variation is essential for statistical performance analysis and modeling, post-silicon tuning, and yield analysis. Existing approaches for spatial modeling either assume that: (i) non-stationarities arise due to a smoothly varying trend component or that (ii) the process is stationary within regions associated with a predefined grid. While such assumptions may hold when profiling certain classes of variations, a number of recent modeling studies suggest that non-stationarities arise from both shifts in the process mean as well as fluctuations in the variance of the process. In order to provide a compact model for non-stationary process variations, we introduce a new hybrid spatial modeling framework that models the spatially varying random field as a union of non-overlapping rectangular regions where the process is assumed to be locally-stationary within each region. To estimate the parameters in our hybrid spatial model, we develop a host of techniques to both estimate the change-points in the random field and to find an appropriate partitioning of the chip into disjoint regions where the field is locally-stationary. We verify our models and results on measurements collected from 65nm FPGAs.