Soft Error Probability Estimation of Nano-scale Combinational Circuits

2025-08-17

AI Summary

As technology scales down and supply voltages decrease, soft error probability (SEP) in nanoscale circuits is increasingly affected by the coupled effects of process variation (PV) and aging—yet existing methods either neglect this coupling or rely on computationally expensive Monte Carlo simulations, hindering scalability for large-scale reliability optimization. This paper proposes the first analytical framework jointly modeling PV and aging, introducing an enhanced electrical masking model integrated with a statistical SEP computation methodology. Leveraging theoretical derivation validated by Monte Carlo simulation, the approach achieves high accuracy while significantly improving computational efficiency. Experimental results demonstrate a ~2.5% reduction in computational overhead compared to conventional Monte Carlo simulation, without sacrificing estimation accuracy. The framework thus provides an efficient, scalable foundation for reliability-driven design of large digital circuits.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

As technology scales, nano-scale digital circuits face heightened susceptibility to single event upsets (SEUs) and transients (SETs) due to shrinking feature sizes and reduced operating voltages. While logical, electrical, and timing masking effects influence soft error probability (SEP), the combined impact of process variation (PV) and aging-induced degradation further complicates SEP estimation. Existing approaches often address PV or aging in isolation, or rely on computationally intensive methods like Monte Carlo simulations, limiting their practicality for large-scale circuit optimization. This paper introduces a novel framework for SEP analysis that holistically integrates PV and aging effects. We propose an enhanced electrical masking model and a statistical methodology to quantify soft error probability under process and aging variations. Experimental results demonstrate that the proposed approach achieves high accuracy while reducing computational overhead by approximately 2.5% compared to Monte Carlo-based methods. This work advances the design of reliable nano-scale circuits by enabling efficient, accurate SEP estimation in the presence of manufacturing variability and long-term transistor degradation.

Problem

• Estimating soft error probability in nano-scale circuits considering process variation and aging
• Addressing limitations of existing methods that isolate PV or aging effects
• Reducing computational overhead while maintaining high accuracy in SEP analysis

Innovation

• Holistic integration of PV and aging effects
• Enhanced electrical masking model for SEP
• Statistical methodology for efficient SEP estimation