This news describes a use case developed in the context of iRel40 (WP5 – T5.3.1) and presented at the ESREL 2023 conference. The main goal of such a use case is to show that it has been possible to exploit an all-digital hardware monitoring system at run-time to improve reliability with a focus on verification activities. For such a purpose, a pacemaker has been developed in two versions. One version has been based on a Commercial Off-The-Shelf microcontroller, where some properties have been verified by means of a classical offline traces analysis approach. The other version has been based on a soft-core Field Programmable Gate Array, where the same properties have been verified at run-time by means of the adopted all-digital hardware monitoring system. The comparison of the two verification approaches shows how it is possible (i) to reduce the time needed to perform verification and (ii) to provide the opportunity to verify more complex properties with respect to classical Built-In Self-Test approaches.
Burn-in (BI), i.e., the operation of devices under accelerated conditions, such as high temperature, is a well-established technique to screen out devices that fail in early life, so-called early failures. Within the iRel40 project, Infineon Technologies Austria AG developed a novel concept, that applies AI models to reduce Burn-in efforts, whilst keeping the device reliability unchanged.
Within the iRel40 project, Joshua Lommes from Fraunhofer IFAM has developed a barrier coating for electronic components. The barrier reduces the permeation of gases, e.g., water vapour, oxygen as well as harmful gases, which promotes the failure of an electronical high-performance device. With a sufficient barrier the lifetime and the reliability of such components can be enhanced drastically. The first developments were published in Microelectronic Engineering and highlighted in a German journal for paint and coatings. We are happy to share, that the latter article was nominated for a young scientist award.
The perception system of automated vehicles is a crucial component, that enables to sense and understand the environment of the vehicle. The system's accuracy and robustness are essential to ensure the safety and reliability of automated vehicles. Realistic usage profiles for automated applications are required to test and validate the system’s components already during the development phase. In iRel40, Virtual Vehicle developed such realistic annual usage profiles for a shuttle pod application, including driving, as well as weather profiles. Applying these profiles to predict the system temperature in a LIDAR, which in turn affects the predicted lifetime of the investigated device, describes one possible application of the developed profiles.
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