Failure Mechanisms and Reliability Issues in LEDs

Pecht, Michael; Chang, Moon-Hwan

doi:10.1007/978-1-4614-3067-4_3

Cited by 24 publications

(7 citation statements)

References 172 publications

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“…As an LED is powered, power dissipated P LED at the junction increases its temperature, as a result LED forward voltage V F decreases, causing the LED to draw more current as junction temperature increases. This situation is illustrated in Figure 5a, if the current is not limited, junction temperature increases drawing more current, which heats it up more, causing increased current, potentially leading to a situation known as Thermal runaway, a condition leading to the destruction of the LED (Arnold, 2004;Fan et al, 2011;Liu & Fang, 2012;Lu et al, 2009;Meneghesso et al, 2002;Pecht & Chang, 2013).…”

Section: Thermal Runaway Problemmentioning

confidence: 99%

Understanding multi-domain compact modeling of light-emitting diodes

Shailesh

2021

Cogent Engineering

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Section: Thermal Runaway Problemmentioning

confidence: 99%

Understanding multi-domain compact modeling of light-emitting diodes

Shailesh

2021

Cogent Engineering

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“…In terms of reliability and life analysis of LED systems, researchers have carried out a lot of basic research work around its failure mode and mechanism [2][3][4] . M. H. Chang of the University of Maryland systematically collated and analyzed the failure modes, causes, effects, and mechanisms of LED [4] .…”

Section: Introductionmentioning

confidence: 99%

Key Failure Mode Identification Method of LED System Based on Fuzzy TOPSIS

Zhang

Hao

Wang

et al. 2023

J. Phys.: Conf. Ser.

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The premise of the system reliability analysis is to effectively discern the patterns of product failures at all levels. To deal with the problem that the diversity of failure modes of complex products represented by LED systems cannot be analyzed comprehensively, in this paper, a key failure mode identification method based on Fuzzy TOPSIS is proposed. First, the construction method of the failure relevance tree is given to support the comprehensive identification of potential failure modes. Then, the decision rule of node failure weight is mined by the rough set method, the systematic evaluation of the node failure weight is realized with the TOPSIS method, and the key failure modes are identified. Finally, a case validation of the proposed method in this paper is performed.

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“…Nowadays, LEDs are widely used in different sectors including street lighting, traffic lighting, advertising display backlights, aviation lighting, indoor lighting, communication devices, automotive lighting, and medical equipment [1,2]. LED-based SSL is known for its benefit in providing lower energy consumption, higher reliability, longer lifetime, compactness in size, and eco-friendliness compared to their traditional counterparts [3]. Low energy consumption, which ultimately helps in energy-saving programs, is one of the benefits of high-power white LEDs.…”

Section: Introductionmentioning

confidence: 99%

System level reliability assessment for high power light-emitting diode lamp based on a Bayesian network method

et al. 2021

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The increased system complexity in electronic products brings challenges in a system level reliability assessment and lifetime estimation. Traditionally, the graph model-based reliability block diagrams (RBD) and fault tree analysis (FTA) have been used to assess the reliability of products and systems. However, these methods are based on deterministic relationships between components that introduce prediction inaccuracy. To fill the gap, a Bayesian Network (BN) method is introduced that considers the intricacies of the high-power light-emitting diode (LED) lamp system and the functional interaction among components for reliability assessment and lifetime prediction. An accelerated degradation test was conducted to analyze the evolution of the degradation and failure of components that influence the system level lifetime and performance of LED lamps. The Gamma process and Weibull distribution are used for component level lifetime prediction. The junction tree algorithm was deployed in the BN structure to estimate the joint probability distributions of the lifetime states. The degradation and prediction results showed that LED modules contribute a major part for lumen degradation of LED lamps followed by drivers and the least effect is from diffuser and reflector. The BN based lifetime estimation results also exhibited an accurate prediction as validated with the Gamma process and such improved reliability assessment outcomes are beneficial to LED manufacturers and customers. Thus, the proposed approach is effective to evaluate and address the long-term reliability assessment concerns of high-reliability LED lamps and fulfill the guarantee of high prediction accuracy in less time and cost-effective manner.

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Failure Mechanisms and Reliability Issues in LEDs

Cited by 24 publications

References 172 publications

Understanding multi-domain compact modeling of light-emitting diodes

Understanding multi-domain compact modeling of light-emitting diodes

Key Failure Mode Identification Method of LED System Based on Fuzzy TOPSIS

System level reliability assessment for high power light-emitting diode lamp based on a Bayesian network method

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