An Integrated Failure and Threat Mode and Effect Analysis (FTMEA) Framework with Quantified Cross-Domain Correlation Factors for Automotive Semiconductors
This framework provides a unified, traceable methodology for risk assessment in critical automotive systems, addressing the challenge of ensuring both functional safety and cybersecurity for complex semiconductor devices by overcoming the limitations of conventional, siloed analyses.
The paper introduces an Integrated Failure and Threat Mode and Effect Analysis (FTMEA) framework to systematically co-analyze functional safety and cybersecurity in automotive semiconductors. It quantifies interdependencies using Cross-Domain Correlation Factors (CDCFs) derived from expert knowledge, static analysis, and empirical data, leading to a modified Risk Priority Number (RPN) calculation. A case study on an automotive ASIC configuration register demonstrates that the FTMEA uncovers previously masked cross-domain risks and improves mitigation strategy effectiveness compared to baseline FMEA/TARA.
The automotive industry faces increasing challenges in ensuring both functional safety (FuSa) and cybersecurity for complex semiconductor devices. Traditional Failure Mode and Effects Analysis (FMEA) primarily addresses safety-related failure modes, often overlooking synergistic vulnerabilities and shared consequences with cybersecurity threats. This paper introduces an Integrated Failure and Threat Mode and Effect Analysis (FTMEA) framework that systematically co-analyzes FuSa and cybersecurity. A cornerstone of this framework is the introduction of rigorously defined Cross-Domain Correlation Factors (CDCFs), which quantify the interdependencies and mutual influences between safety-related failures and cybersecurity threats. These factors are derived from a combination of structured expert knowledge, static structural analysis metrics (e.g., Controllability/Observability), and validated against empirical data from fault/attack injection campaigns. We propose a modified Risk Priority Number (RPN) calculation that systematically integrates these correlation factors, enabling a more accurate and transparent prioritization of risks that span both domains. A detailed case study involving an automotive ASIC configuration register proves the practical application of the FTMEA. We present explicit mapping tables, quantitative CDCF values, and a comparative analysis against a baseline FMEA/TARA (Threat Analysis and Risk Assessment), illustrating how the integrated approach uncovers previously masked cross-domain risks, improves mitigation strategy effectiveness, and provides a clear quantitative justification for the derived correlation values. This framework offers a unified, traceable, methodology for risk assessment in critical automotive systems, thereby overcoming the limitations of conventional analyses and promoting optimized, cross-disciplinary development.