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Nicolas Gerlin, Endri Kaja, Fabian Vargas, Li Lu, Anselm Breitenreiter, Junchao Chen, Markus Ulbricht, Maribel Gomez, Ares Tahiraga, Sebastian Prebeck, Eyck Jentzsch, Milos Krstic, Wolfgang Ecker
Abstract: Electronic systems can be submitted to hostile environments leading to bit-flips or stuck-at faults and, ultimately, a system malfunction or failure. In safety-critical applications, the risks of such events should be managed to prevent injuries or material damage. This paper provides a comprehensive overview of the challenges associated with designing and verifying safe and reliable systems, as well as the potential of the RISC-V architecture in addressing these challenges.We present several state-of-the-art safety and reliability verification techniques in the design phase. These include a highly-automated verification flow, an automated fault injection and analysis tool, and an AI-based fault verification flow. Furthermore, we discuss core hardening and fault mitigation strategies at the design level. We focus on automated SoC hardening using model-driven development and resilient processing based on sensing and prediction for space and avionic applications.By combining these techniques with the inherent flexibility of the RISC-V architecture, designers can develop tailored solutions that balance cost, performance, and fault tolerance to meet the requirements of various safety-critical applications in different safety domains, such as avionics, automotive, and space. The insights and methodologies presented in this paper contribute to the ongoing efforts to improve the dependability of computing systems in safety-critical environments.
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Adwait Inamdar, Michiel van Soestbergen, Amar Mavinkurve, Willem van Diel, GuoQi Zhang
Abstract: Moulding compounds used for encapsulating electronics typically occupy a large portion of package volume and are most exposed to the external environment. Under harsh conditions such as high temperature, humidity, and mechanical vibrations, constituent materials of electronic components degrade, resulting in a change in their thermal, mechanical, electrical, and chemical behaviour. High-temperature ageing of electronic packages causes the oxidation of epoxy moulding compounds (EMC), forming a layer exhibiting significantly different thermomechanical properties. This reflects in the modified mechanical behaviour of the entire package, which accelerates certain failure modes and affects component reliability. Thus, it is crucial to consider gradual degenerative changes in EMC for a more accurate estimation of the component lifetime. This paper proposes a three-step modelling approach to replicate thermo-chemical changes in package encapsulation. A parametric geometry of a test package was incorporated with the ageing stage-dependent changes in thermomechanical properties of the oxidized layer. The mechanical behaviour of oxidized EMC at multiple stages of thermal ageing (at 150°C for up to 3000 hours) was first experimentally characterized and then validated using warpage measurements on thermally aged test packages and Finite Element (FE) simulations. Lastly, a trend-based interpolation of material model parameters for intermediate stages of ageing was followed, and a continuously updated degradation model (physics-based Digital Twin) was achieved. The proposed model is capable of reproducing degraded stages of the test package under thermal ageing along with its modified thermomechanical behaviour. Its limitations and significance in the domain of health monitoring of microelectronics are also discussed.
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Selin Solmaz, Georg Stettinger, Franz Wottawa
Abstract: Automated Vehicles (AVs) are designed to enhance road safety by utilizing Automated Driving Systems (ADS) that leverage behavioral competencies within the targeted Operational Design Domain (ODD). However, operation within the current ODD always carries a residual risk that must be kept within acceptable limits to ensure safe and robust operation. This paper proposes a system-level residual risk management strategy for ACC/LKA behavioral competencies, which comprises a receive- monitor-transmit concept for hierarchical monitoring functional- ities, a system-level residual risk management strategy, and fault injection campaigns to challenge the implemented multi-layer monitoring functionalities. The proposed strategy is implemented ACC/LKA-driven benchmark example, which demonstrates the efficient and effective handling of residual risks at the system level. The study concludes that targeted ODD and/or related behavioral competence reductions are a promising approach to maintaining the residual risk within acceptable limits. Index Terms—residual risk, operational design domain, be- haviour competence, monitoring, health status, fault-injection
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Franz Wotawa
Abstract: System monitoring is essential for detecting failures during operation and ensuring reliability. A monitoring system obtains observations and checks their consistency concerning requirements formalized as properties. However, finding property violations does not necessarily mean finding the causes. In this paper, we contribute to the latter and suggest introducing model-based diagnosis for root cause identification. We do this by adding information regarding the source of observations. Furthermore, we suggest implementing properties using ordinary programming languages from which we can obtain a formal model directly. Finally, we explain the process of integrating diagnosis into monitoring and show its value using a case study from the automotive domain.
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Xinhai Zhang, Jianbo Tao, Kaige Tan, Martin Törngren, Jose Manuel Gaspar Sanchez, Muhammad Rusyadi Ramli, Xin Tao, Magnus Gyllenhammar, Franz Wotawa, Member, Naveen Mohan, Member, Mihai Nica and Hermann Felbinger
Abstract: Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion
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