On Hierarchical Statistical Static Timing Analysis

TL;DR

This paper introduces a novel hierarchical statistical static timing analysis method that generates compact, accurate timing models considering process variations and module correlations, significantly improving efficiency over Monte Carlo simulations.

Contribution

It proposes a new approach for generating small, accurate timing models for hierarchical timing analysis that accounts for module correlations, enhancing accuracy and efficiency.

Findings

Timing models are 80% smaller than original circuits.

The method achieves accuracy comparable to Monte Carlo simulations.

Analysis is three orders of magnitude faster than Monte Carlo.

Abstract

Statistical static timing analysis deals with the increasing variations in manufacturing processes to reduce the pessimism in the worst case timing analysis. Because of the correlation between delays of circuit components, timing model generation and hierarchical timing analysis face more challenges than in static timing analysis. In this paper, a novel method to generate timing models for combinational circuits considering variations is proposed. The resulting timing models have accurate input-output delays and are about 80% smaller than the original circuits. Additionally, an accurate hierarchical timing analysis method at design level using pre-characterized timing models is proposed. This method incorporates the correlation between modules by replacing independent random variables to improve timing accuracy. Experimental results show that the correlation between modules strongly affects the delay distribution of the hierarchical design and the proposed method has good accuracy compared with Monte Carlo simulation, but is faster by three orders of magnitude.

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Full text

See pages 1-last of Hierarchical_SSTA_DATE2009.PDF