Emerging ADAS Thermal Reliability Needs and Solutions

Abstract

Advanced driver assistance systems (ADASs) used for pedestrian detection, parking assist, night vision, blind-spot monitoring, collision avoidance, and other such capabilities have significantly enhanced car safety and reduced the risk of dangerous accidents. Their further evolution into a network of intelligent systems using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications is paving the way for autonomous driving. To support these advanced technologies, automotive electronics must be overhauled to enable machine learning capabilities, particularly deep learning, to transform the typical car into a smart system on wheels. Thermal reliability is critical because these high-power, intelligent electronics systems must last more than 10 years under often hostile thermal environments. This article presents an innovative multiphysics solution for thermal, thermal-aware electromigration, and thermal-induced stress analysis of a chip-package-system realized in 3DIC, an example of which is an AI system used in ADAS.