Stochastic finite elements: a spectral approach
Stochastic finite elements: a spectral approach
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Statistical analysis of subthreshold leakage current for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the 43rd annual Design Automation Conference
Karhunen-Loève approximation of random fields by generalized fast multipole methods
Journal of Computational Physics - Special issue: Uncertainty quantification in simulation science
Prediction of leakage power under process uncertainties
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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
Hotspot: acompact thermal modeling methodology for early-stage VLSI design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Full-chip thermal analysis for the early design stage via generalized integral transforms
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Statistical timing analysis under spatial correlations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
ISAC: Integrated Space-and-Time-Adaptive Chip-Package Thermal Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
An efficient method for analyzing on-chip thermal reliability considering process variations
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 0.00 |
In this work, we develop a statistical thermal simulator including the effect of spatial correlation under within-die process variations. This method utilizes the Karhunen-Loève (KL) expansion to model the physical parameters, and apply the Polynomial Chaoses (PCs) and the stochastic Galerkin method to tackle stochastic heat transfer equations. We demonstrate the accuracy and efficiency of our simulator by comparing with the Monte Carlo simulation, and point out that the stochastic thermal analysis is essential to provide a robust estimation of temperature distribution for the thermal-aware design flow.