3.8 Proceedings Paper

Data-Driven Remaining Useful Life Prediction of QFN Packages on Board Level with On-Chip Stress Sensors

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/ECTC32696.2021.00150

Keywords

PHM; QFN; RUL

Funding

  1. Federal Government of Germany, Framework Program for Research and Innovation 2016-2020 Microelectronics - Innovation Drivers of Digitalization (Rahmenprogramm der Bundesregierung fur Forschung und Innovation 2016-2020 Mikroelektronik aus Deutschland - Inno [16ES0965]

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In this study, in-situ measurements of on-chip stress sensors were used to generate run-to-failure data sets, with physical failure analysis establishing the link between data and remaining useful life, enabling predictive maintenance of components.
Miniaturization of components and higher operating loads lead to reduced lifetimes. Prognostics and Health Management (PHM) enables predictive maintenance of components whose lifetime is shorter than that of the system they are part of. The key to PHM lies in sensor data that correlates with component degradation. In this study, run-to-failure data sets have been generated using in-situ measurements of on-chip stress sensors. Physical failure analysis has provided the link between the data and remaining useful life.

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