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Sensitizable Statistical Timing Analysis

  • Rajesh Garg
  • Sunil P. Khatri
Chapter

Abstract

This chapter presents the sensitizable statistical timing analysis (StatSense) methodology, developed to remove the pessimism due to two sources of inaccuracy, which plague current statistical static timing analysis (SSTA) tools. Specifically, the StatSense approach implicitly eliminates false paths, and also uses different delay distributions for different input transitions for any gate. StatSense consists of two phases. In the first phase, a set of N logically sensitizable vector transitions which result in the largest delays for a circuit are obtained. In the second phase, these delay-critical sensitizable input vector transitions are propagated using a Monte-Carlo-based technique, to obtain the delay distribution at the outputs of the design. The specific input transitions at any gate are known after the first phase. The second phase performs Monte-Carlo-based SSTA, using the appropriate gate delay distribution corresponding to the particular input transition for each gate. The StatSense approach is able to significantly improve the accuracy of SSTA analysis. The circuit delay distribution obtained using StatSense closely matches with that obtained by SPICE-based Monte-Carlo simulations.

Keywords

Arrival Time Input Transition Vector Transition Combinational Circuit Gate Delay 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag US 2010

Authors and Affiliations

  1. 1.HillsboroUSA
  2. 2.Department of Electrical and Computer EngineeringTexas A & M UniversityCollege StationUSA

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