Physics-of-failure-based prognostics for electronic products

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Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, background): “Approaches to RUL and degradation level predictions can be grouped into two broad categories, with a third hybrid one straddling the first two: (i) model-based approaches, which build failure models based on the detailed analysis of the physical nature of the failure mechanism under consideration [8”
A prognostics and health management roadmap for information and electronics-rich systems (2010, background): “This approach is based on the understanding that failures occur due to fundamental mechanical, chemical, electrical, thermal, and radiation processes [29].”
A Review On Transient Thermal Management of Electronic Devices (2021, background): “Fatigue due to temporal temperature oscillations leads to failure at locations such as the die-attach, wire bonds, solder leads, bond pads, and vias [11].”
Remaining useful life estimation in prognostics using deep convolution neural networks (2018, methodology): “Generally, the existing methods for PHM can be grouped into three main categories, i.e. model-based approaches [4], data-driven approaches [5] and hybrid approaches [6].”
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, background): “, physics-of-failure (PoF)-based approaches [3], data-driven approaches [4], and their hybridization [5].”