Statistical delay computation considering spatial correlations

  • Authors:
  • Aseem Agarwal;David Blaauw;Vladimir Zolotov;Savithri Sundareswaran;Min Zhao;Kaushik Gala;Rajendran Panda

  • Affiliations:
  • University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;Motorola, Inc., Austin, TX;Motorola, Inc., Austin, TX;Motorola, Inc., Austin, TX;Motorola, Inc., Austin, TX;Motorola, Inc., Austin, TX

  • Venue:
  • ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Process variation has become a significant concern for static timing analysis. In this paper, we present a new method for path-based statistical timing analysis. We first propose a method for modeling inter- and intra-die device length variations. Based on this model, we then present an efficient method for computing the total path delay probability distribution using a combination of device length enumeration for inter-die variation and an analytical approach for intra-die variation. We also propose a simple and effective model of spatial correlation of intra-die device length variation. The analysis is then extended to include spatial correlation. We test the proposed methods on paths from an industrial high-performance microprocessor and present comparisons with traditional path analysis which does not distinguish between inter- and intra-die variations. The characteristics of the device length distributions were obtained from measured data of 8 test chips with a total of 17688 device length measurements. Spatial correlation data was also obtained from these measurements. We demonstrate the accuracy of the proposed approach by comparing our results with Monte-Carlo simulation.