An Integrated Failure and Threat Mode and Effect Analysis (FTMEA) Framework with Quantified Cross-Domain Correlation Factors for Automotive Semiconductors

• URL: http://arxiv.org/abs/2603.06299v1
• Date: Fri, 06 Mar 2026 14:03:52 GMT
• Title: An Integrated Failure and Threat Mode and Effect Analysis (FTMEA) Framework with Quantified Cross-Domain Correlation Factors for Automotive Semiconductors
• Abstract summary: This paper introduces an Integrated Failure and Threat Mode and Effect Analysis framework that systematically co-analyzes FuSa and cybersecurity.<n>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.<n>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.
• Score: 0.7974430263940756
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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