As autonomous driving is becoming reality, more advanced solutions to enhance reliability of safety relevant systems are demanded. One of such solutions is to consolidate prognostics and health management concepts. It can be accomplished by understanding the failure, recognizing it from the field signals, and eventually predicting it. This study focuses on the second step, namely recognizing the failure from sensor signals. The test vehicle used in the study is a Thin Quad Flat Package (TQFP) mounted on a printed circuit board (PCB). The package contains 8 piezoresistive stress sensors which replaces the functional die. The test vehicle is subjected to liquid thermal shock testing conditions, and the stress state changes inside the package are recorded by stress sensors. Each sensor contains 60 stress-measuring cells, which provides the internal stress state of the package with high resolution and accuracy. The internal stress state is used to identify the failure times and locations.

Original languageEnglish
Title of host publication2018 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2018
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-3
Number of pages3
ISBN (Electronic)978-1-5386-2359-6
DOIs
Publication statusPublished - 2018
EventEuroSimE 2018: 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems - Toulouse, France
Duration: 15 Apr 201818 Apr 2018

Conference

ConferenceEuroSimE 2018
Abbreviated titleEuroSimE 2018
CountryFrance
CityToulouse
Period15/04/1818/04/18

    Research areas

  • Stress, Liquids, Electric shock, Compounds, Delamination, Reliability, Piezoresistance

ID: 45595833