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.

翻译：精确可靠的安全指标对于汽车系统中ASIC的功能安全验证至关重要。传统的FMEDA（失效模式、影响与诊断分析）指标，如SPFM（单点故障指标）和LFM（潜伏故障指标），依赖于失效模式分布（FMD）和诊断覆盖率（DC）估计的精度。这种依赖常导致显著的、未量化的不确定性以及对专家判断的依赖，从而损害了安全分析的质量。本文提出了一种新颖的方法，将误差传播理论引入FMEDA安全指标的计算中。通过量化SPFM和LFM的最大偏差并提供置信区间，我们的方法提供了分析质量的直接度量。此外，我们引入了一个误差重要性标识符（EII）来定位不确定性的主要来源，从而指导针对性的改进。该方法显著增强了FMEDA的透明度和可信度，使得为满足ISO 26262合规性要求的ASIC安全验证更加稳健，解决了功能安全领域一个长期存在的未解问题。