Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models

In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.Comment: 9th Symposium Driving Simulation 2023, Brunswick, German

Reliability and Data Analysis of Wearout Mechanisms for Circuits

The objective of this research is to develop methodologies for the failure analysis of circuits, as well as investigate the factors for accelerating testing for front-end-of-line time-dependent dielectric breakdown (FEOL TDDB). The separation of wearout mechanisms for circuits will be investigated, and the identification of failure modes for the failure samples will be analyzed. SRAMs and ring oscillators will be used to study the failure modes. The systematic and random errors for online monitoring of SRAMS will also be examined. Furthermore, the testing plans for acceleration testing will also be explored for ring oscillators. Error reduction through sampling will also be used to find the best testing conditions for accelerated testing. This work provides a way for engineers to better understand aging monitoring of circuits, and to design better testing to collect failure data.Ph.D