Non-parametric statistical static timing analysis: an SSTA framework for arbitrary distribution

Authors:
Masanori Imai;Takashi Sato;Noriaki Nakayama;Kazuya Masu
Affiliations:
Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology and Semiconductor Technology Academic Research Center;Integrated Research Institute, Tokyo Institute of Technology;Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology;Integrated Research Institute, Tokyo Institute of Technology
Venue:
Proceedings of the 45th annual Design Automation Conference
Year:
2008

Citing 7
Cited 1

First-order incremental block-based statistical timing analysis

Proceedings of the 41st annual Design Automation Conference
Fast statistical timing analysis handling arbitrary delay correlations

Proceedings of the 41st annual Design Automation Conference
STAC: statistical timing analysis with correlation

Proceedings of the 41st annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model

Proceedings of the 42nd annual Design Automation Conference
Statistical critical path analysis considering correlations

ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Statistical timing analysis using bounds and selective enumeration

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Statistical characterization of standard cells using design of experiments with response surface modeling

Proceedings of the 48th Design Automation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new statistical STA framework based on Monte Carlo analysis that can deal with arbitrary statistical distribution and delay models. Order statistics (non-parametrics) is consistently adopted by which the timing analysis and criticality calculation become distribution-independent. To make Monte Carlo process computationally practical, delays are handled as vectors so that iterations are eliminated. The vector dimension or required number of Monte Carlo iterations which guarantees no timing violation at any user-specified probability is analytically determined. A path criticality metric using order statistics is also defined. Experimental results using various delay models show the validity and usefulness of our proposed algorithm.