<inline-formula> <tex-math notation="LaTeX">$In\ Situ$ </tex-math> </inline-formula> Failure Detection of Electronic Control Units Using Piezoresistive Stress Sensor

Abstract

Recent advancements in automotive technologies, most notably autonomous driving, demand electronic systems much more complex than realized in the past. The automotive industry has been forced to adopt advanced consumer electronics to satisfy the demand, and thus it becomes more challenging to assess system reliability while adopting the new technologies. The system level reliability can be enforced by implementing a process called condition monitoring. In this paper, a piezoresistive silicon based stress sensor is implemented to detect physical damages in outer molded electronic control units (ECU) subjected to reliability testing conditions. The test vehicle consists of six DPAK power packages and three stress sensors mounted on a Printed Circuit Board (PCB). A unique algorithm is proposed and implemented to handle the data obtained from the piezoresistive stress sensing cells. The accuracy of measured data is examined by Finite Element method (FEM), and the physical changes are validated with Scanning Acoustic Microscope (SAM).

Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

“…As one of the popular approach in prognostics, ANN has been implemented to study transformers, [ 144 ] aircraft actuator components, [ 145 ] bearings, [ 146 ] nuclear turbo‐generators, [ 147 ] electronic packages, [ 148 ] etc. However, application of ANN methods for high‐power white LEDs lifetime estimation was not very common until Sutharssan [ 149 ] demonstrated a basic neural network for lifetime prediction of LEDs.…”