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

TL;DR

This paper introduces an error propagation method to quantify uncertainties in FMEDA safety metrics, improving the reliability and transparency of ASIC safety verification for automotive systems.

Contribution

It presents a novel error propagation approach for FMEDA metrics, including confidence intervals and an Error Importance Identifier, enhancing analysis accuracy and guidance.

Findings

Provides confidence intervals for SPFM and LFM

Identifies primary sources of uncertainty in safety metrics

Improves robustness of ASIC safety verification

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.