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

Daniel Riegel, P. Gromala, B. Han, S. Rzepka
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引用次数: 1

Abstract

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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基于片上应力传感器的QFN封装板级剩余使用寿命预测
组件的小型化和更高的工作负载导致使用寿命缩短。预测和健康管理(PHM)支持对寿命短于其所属系统寿命的组件进行预测性维护。PHM的关键在于与部件退化相关的传感器数据。在这项研究中,使用片上应力传感器的原位测量生成了运行到故障的数据集。物理故障分析提供了数据和剩余使用寿命之间的联系。
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