Advances in LED Solder Joint Reliability Testing and Prediction

Zhang, J.; Zhang, Guo Qi

doi:10.1007/978-3-319-58175-0_12

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…Chang et al [240] developed a prognostics-based anomaly detection technique, called the similarity-based metric test, to identify anomalies without using historical libraries of healthy and unhealthy data. Zhang [241] also studied the fast qualification of solder reliability in SSL systems. Qian et al [215] developed an accelerated test method based on the lumen maintenance boundary curve for luminous flux depreciation to solve some problems in IES-TM-28 and reduce the test time to less than 2,000 h at an elevated temperature.…”

Section: Developing a Unified Standard For Anomaly Detection And Qual...mentioning

confidence: 99%

A Review of Prognostic Techniques for High-Power White LEDs

Sun

Jiang

Yung

et al. 2017

IEEE Trans. Power Electron.

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High-power white light-emitting diodes (LEDs) have attracted much attention due to their versatility in a variety of applications and growing demand in markets such as general lighting, automotive lamps, communications devices, and medical devices. In particular, the need for high reliability and long lifetime poses new challenges for the research and development, production, and application of LED lighting. Accurate and effective prediction of the lifetime or reliability of LED lighting has emerged as one of the key issues in the solid-state lighting field. Prognostics is an engineering technology that predicts the future reliability or determines the remaining useful lifetime (RUL) of a product by assessing the extent of deviation or degradation of a product from its expected normal operating conditions. Prognostics bring benefits to both LED developers and users, such as optimizing system design, shortening qualification test times, enabling condition-based maintenance for LED-based systems, and providing information for return-on-investment (ROI) analysis. This paper provides an overview of the prognostic methods and models that have been applied to both LED devices and LED systems, especially for use in long-term operational conditions. These methods include statistical regression, static Bayesian network, Kalman filtering, particle filtering, artificial neural network, and physics-based methods. The general concepts and main features of these methods, the advantages and disadvantages of applying these methods, as well as LED application case studies, are discussed. The fundamental issues of prognostics and photo-electro-thermal (PET) theory for LED systems are also discussed for clear understanding of the reliability and lifetime concepts for LEDs. Finally, the challenges

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Section: Developing a Unified Standard For Anomaly Detection And Qual...mentioning

confidence: 99%