First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
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
A general framework for accurate statistical timing analysis considering correlations
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Fast second-order statistical static timing analysis using parameter dimension reduction
Proceedings of the 44th annual Design Automation Conference
Non-linear statistical static timing analysis for non-Gaussian variation sources
Proceedings of the 44th annual Design Automation Conference
Principle Hessian direction based parameter reduction with process variation
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Adjustment-based modeling for timing analysis under variability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A unified multi-corner multi-mode static timing analysis engine
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Robust spatial correlation extraction with limited sample via L1-norm penalty
Proceedings of the 16th Asia and South Pacific Design Automation Conference
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This paper presents an adjustment-based modeling framework for Statistical Static Timing Analysis (SSTA) when the dimension of parameter variability is high. Instead of building a complex model between the circuit timing and parameter variability, we build a model which adjusts an approximate variation-aware timing into an accurate one. The intuition is that it is simpler to build a model which adjusts an approximate estimate into an accurate one. It is also more efficient to obtain an approximate circuit timing model. The combination of these two observations makes the use of an adjustment-based model a good choice for SSTA with high dimension of parameter variability. To build the adjustment model, we use a simulation-based approach, which is based on Gaussian Process. Combined with intelligent sampling, we show that an adjustment-based model can more effectively capture the nonlinearity of the circuit timing with respect to parameter variability compared to polynomial modeling. We also show that with only 200 samples of the circuit timing and 42 independent parameter variations, adjustment-based modeling obtains higher accuracy than direct SSTA using quadratic modeling.