Proceedings of the 38th annual Design Automation Conference
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Fast Generation of Statistically-based Worst-Case Modeling of On-Chip Interconnect
ICCD '97 Proceedings of the 1997 International Conference on Computer Design (ICCD '97)
Statistical analysis of subthreshold leakage current for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
First-order incremental block-based statistical timing analysis
Proceedings of the 41st 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
Proceedings of the 42nd annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model
Proceedings of the 42nd annual Design Automation Conference
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Variability in sub-100nm SRAM designs
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Proceedings of the 43rd annual Design Automation Conference
Statistical logic cell delay analysis using a current-based model
Proceedings of the 43rd annual Design Automation Conference
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As technology rapidly scales, performance variations (delay, power etc.) arising from process variation are becoming a significant problem. The use of linear models has been proven to be very critical in many today's applications. Even for well-behaved performance functions, linearising approaches as well as quadratic model provide serious errors in calculating expected value, variance and higher central moments. In this paper, we present a novel approach to analyse the impacts of process variations with low efforts and minimum assumption. We formulate circuit performance as a function of the random parameters and approximate it by Taylor Expansion up to 4th order. Taking advantage of the knowledge about higher moments, we convert the Taylor series to characteristics of performance distribution. Our experiments show that this approach provides extremely exact results even in strongly non-linear problems with large process variations. Its simpleness, efficiency and accuracy make this approach a promising alternative to the Monte Carlo Method in most practical applications.