Quantifying Uncertainty in FMEDA Safety Metrics: An Error Propagation Approach for Enhanced ASIC Verification

2026-03-23

AI Summary

This work addresses the limitations of traditional Failure Modes, Effects, and Diagnostic Analysis (FMEDA) in automotive ASIC functional safety verification, where expert judgment is used to estimate failure mode distributions and diagnostic coverage without quantifying associated uncertainties, thereby compromising reliability. For the first time, error propagation theory is systematically integrated into FMEDA to construct uncertainty models for both failure mode distributions and diagnostic coverage. This enables quantitative computation of the maximum deviations and confidence intervals for the Single-Point Fault Metric (SPFM) and Latent Fault Metric (LFM). Furthermore, an Error Importance Indicator (EII) is introduced to trace the key contributors driving overall uncertainty. The proposed approach significantly enhances the transparency and credibility of FMEDA, offering a scientifically rigorous and quantifiable foundation for compliance with ISO 26262.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Accurate and reliable safety metrics are paramount for functional safety verification of ASICs in automotive systems. Traditional FMEDA (Failure Modes, Effects, and Diagnostic Analysis) metrics, such as SPFM (Single Point Fault Metric) and LFM (Latent Fault Metric), depend on the precision of failure mode distribution (FMD) and diagnostic coverage (DC) estimations. This reliance can often leads to significant, unquantified uncertainties and a dependency on expert judgment, compromising the quality of the safety analysis. This paper proposes a novel approach that introduces error propagation theory into the calculation of FMEDA safety metrics. By quantifying the maximum deviation and providing confidence intervals for SPFM and LFM, our method offers a direct measure of analysis quality. Furthermore, we introduce an Error Importance Identifier (EII) to pinpoint the primary sources of uncertainty, guiding targeted improvements. This approach significantly enhances the transparency and trustworthiness of FMEDA, enabling more robust ASIC safety verification for ISO 26262 compliance, addressing a longstanding open question in the functional safety community.

Problem

• FMEDA
• safety metrics
• uncertainty quantification
• ASIC verification
• functional safety

Innovation

• error propagation
• FMEDA
• safety metrics
• uncertainty quantification
• Error Importance Identifier