Statistical Sequential Path Delay Variation Model with Lognormal Distribution for Low-Voltage Circuit

To solve the timing uncertainty in low-voltage circuits, this paper proposes an analytical sequential path delay model based on the lognormal distribution. Unlike previous works that primarily focus on combinational logic, our model provides a complete framework for sequential elements. It decouples inter-stage correlations within flip-flops through a linear delay transformation. The model’s key innovation is a one-shot characterization approach that dramatically reduces simulation time compared to traditional Monte Carlo methods. All experiments on the TSMC 28 nm process show high accuracy, with average errors below 6% against MC. Our model demonstrates significant prediction accuracy improvements of up to 10.2X over prior art, establishing a highly efficient and accurate solution for variation-aware sequential timing analysis.

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Publication Details

Journal
Applied Sciences
Published
2025-11-27
DOI
https://doi.org/10.3390/app152312560
Primary Topic
Low-power high-performance VLSI design
Type
article
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article

Statistical Sequential Path Delay Variation Model with Lognormal Distribution for Low-Voltage Circuit

Runkan Liu, Dongliang Zhao, Jingjing Guo
Applied Sciences
Low-power high-performance VLSI design
article

Statistical Sequential Path Delay Variation Model with Lognormal Distribution for Low-Voltage Circuit

Runkan Liu, Dongliang Zhao, Jingjing Guo
article en

Abstract

To solve the timing uncertainty in low-voltage circuits, this paper proposes an analytical sequential path delay model based on the lognormal distribution. Unlike previous works that primarily focus on combinational logic, our model provides a complete framework for sequential elements. It decouples inter-stage correlations within flip-flops through a linear delay transformation. The model’s key innovation is a one-shot characterization approach that dramatically reduces simulation time compared to traditional Monte Carlo methods. All experiments on the TSMC 28 nm process show high accuracy, with average errors below 6% against MC. Our model demonstrates significant prediction accuracy improvements of up to 10.2X over prior art, establishing a highly efficient and accurate solution for variation-aware sequential timing analysis.

Applied SciencesVol. 15(23)
Nanjing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 63%
Low-power high-performance VLSI design
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Statistical Sequential Path Delay Variation Model with Lognormal Distribution for Low-Voltage Circuit — Runkan Liu, Dongliang Zhao, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS