Death, taxes and failing chips
Proceedings of the 40th annual Design Automation Conference
Design and reliability challenges in nanometer technologies
Proceedings of the 41st annual Design Automation Conference
Toward a systematic-variation aware timing methodology
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
A Statistical Gate-Delay Model Considering Intra-Gate Variability
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical analysis of SRAM cell stability
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
A framework for statistical timing analysis using non-linear delay and slew models
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Characterizing process variation in nanometer CMOS
Proceedings of the 44th annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Asymptotic Probability Extraction for Nonnormal Performance Distributions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Analog test metrics estimates with PPM accuracy
Proceedings of the International Conference on Computer-Aided Design
An efficient control variates method for yield estimation of analog circuits based on a local model
Proceedings of the International Conference on Computer-Aided Design
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We present a generic data-driven nonparametric analyzer (GDNA) to estimate the impact of process variations on device properties and circuit functionalities in nanometer technologies. The mathematical framework of GDNA uses a kernel estimator that eliminates the need for a priori assumption of the nature of variation (i.e., no a priori choice is required for the density of a random variable). Furthermore, a generic tail probability estimator is developed that uses the kernel estimator to predict low occurrence probabilities using a small set of observed samples. Verifications using statistical simulations show that GDNA can reliably predict variability in device/circuit properties and can hence facilitate technology development and circuit design under process variation.