The development of Integrated Circuits for the Automotive sector imposes on major challenges. ISO26262 compliance, as part of this process, entails complex analysis for the evaluation of potential random hardware faults. This paper proposes a systematic approach to identify faults that do not disrupt safety-critical functionalities and consequently can be considered Safe. By deploying code coverage and Formal verification techniques, our methodology enables the classification of faults that are unclassified by other technologies, improving ISO26262 compliance. Our results, in combination with Fault Simulation, achieved a Diagnostic Coverage of 93% in a CAN Controller. These figures allow an initial assessment for an ASIL B configuration of the IP.

The current demands for autonomous driving generated momentum for an increase in research in the different technologies required for these applications. Nonetheless, the limited access to representative designs and industrial methodologies poses a challenge to the research community. Considering this scenario, there is a high demand for an open-source solution that could support development of research targeting automotive applications. This paper presents the current status of AutoSoC, an automotive SoC benchmark suite that includes hardware and software elements and is entirely open-source. The objective is to provide researchers with an industrial-grade automotive SoC that includes all essential components, is fully customizable, and enables analysis of functional safety solutions and automotive SoC configurations. This paper describes the available configurations of the benchmark including an initial assessment for ASIL B to D configurations.

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The development of Integrated Circuits for the Automotive sector imposes in complex challenges. ISO26262 Functional Safety requirements entail extensive Fault Injection campaigns and complex analysis for the evaluation of deployed Software Tools. This paper proposes a methodology to improve Fault Analysis Tools Confidence Level (TCL) by detecting errors in the classification of faults. By combining the strengths of Automatic Test Pattern Generators (ATPG), Formal Methods and Fault Injection Simulators we are able to automatically generate a Test Environment that enables the validation of the tools and provide supplementary information about the design behavior. Our results showed fault detection rates above 99% including information to improve ISO26262 metrics calculation.

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