Abstract
In order to help the engineers to improve quality level and reduce the failure rate in using of LED product, we should realize the failure mode, failure mechanisms and root causes of this kind of products. First, more than 300 failure analysis cases about LED product have been carried out by the authors. And the authors made a summary of these cases. The main failure modes of LED products are introduced. Then, based on these failure modes, the possible mechanism of each type of failure mode and the root causes of failures are analyzed in detail. Finally, a lot of cases are used to support the analysis in this article. All failure modes, corresponding failure mechanisms and root causes are summarized in a form for reference to engineers and technicians engaged in failure analysis and product improvement. The failure mode of LED products in the use is no light, or intermittently no light, or light intensity weakening, or device burnout. But its failure mechanism and related reasons are quite complex, such as the chip quality, packaging process, heat dissipation channel design and derating design related to LED.
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References
GJB548-96 Test methods and procedures for microelectronic devices, China Planning Press, China, 1996.
J. Dong and A. Pandharipande, Lumen Depreciation Diagnosis in Modulated LED Lighting Systems, in IEEE Photonics Technology Letters, vol. 25, no. 15, pp. 1466–1469, Aug.1, 2013. https://doi.org/10.1109/LPT.2013.2267591
Y. Zhou, Failure Mechanism of Gold-Plated Contacts in Mobile Phones Under Dust Contamination Jointing With Micromotion Condition. IEEE Transactions on Components, Packaging and Manufacturing Technology, 5 (2015) 337–344. https://doi.org/10.1109/TCPMT.2015.2393374.
L. Cui, J. Huang and F. Zhang, Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical-Horizontal Synchronization Signal Analysis. IEEE Transactions on Industrial Electronics, 64 (2017) 8695–8706. https://doi.org/10.1109/TIE.2017.2698359.
S. Zhang, J.H. Park and K.W. Paik, Joint Morphologies and Failure Mechanisms of Anisotropic Conductive Films (ACFs) During a Power Handling Capability Test for Flex-On-Board Applications. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6 (2016) 1820–1826. https://doi.org/10.1109/TCPMT.2016.2619878.
S. Zhang and K.W. Paik, A Study on the Failure Mechanism and Enhanced Reliability of Sn58Bi Solder Anisotropic Conductive Film Joints in a Pressure Cooker Test Due to Polymer Viscoelastic Properties and Hydroswelling. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6 (2016) 216–223. https://doi.org/10.1109/TCPMT.2015.2481458.
P. Yang, Y. Yang, Y. Wang, J. Gao, N. Sui, X. Chi, L. Zou and H. Zhang, Spontaneous emission of semiconductor quantum dots in inverse opal SiO2photonic crystals at different temperatures. Luminescence, 31 (2015) 4–7. https://doi.org/10.1002/bio.3000.
X. Chi, Y. Wang, J. Gao, Q. Liu, N. Sui, J. Zhu, X. Li, H. Yang, L. Zou, J. Kou and H. Zhang, Study of photoluminescence characteristics of CdSe quantum dots hybridized with Cu nanowires. Luminescence, 31 (2016) 1298–1301. https://doi.org/10.1002/bio.3101.
J. Fan, F. Wang, Q. Sun, F. Bin, F. Liang and X. Xiao, Hybrid RVM–ANFIS algorithm for transformer fault diagnosis, in IET Generation, Transmission & Distribution, vol. 11, no. 14, pp. 3637–3643, 9 28 2017. doi: https://doi.org/10.1049/iet-gtd.2017.0547
J. Xia, Y. Guo, B. Dai and X. Zhang, Sensor Fault Diagnosis and System Reconfiguration Approach for an Electric Traction PWM Rectifier Based on Sliding Mode Observer, in IEEE Transactions on Industry Applications, vol. 53, no. 5, pp. 4768–4778, Sept.-Oct. 2017. doi: https://doi.org/10.1109/TIA.2017.2715816
H. Huang, S. Ding, L. Zhao, H. Huang, L. Chen, H. Gao and S.H. Ahmed, Real-Time Fault Detection for IIoT Facilities Using GBRBM-Based DNN. IEEE Internet of Things Journal, 7 (2020) 5713–5722. https://doi.org/10.1109/jiot.2019.2948396.
S. Basheer, G. Usha Devi, M.K. Priyan and P. Parthasarathy, Network Support Data Analysis for Fault Identification Using Machine Learning, International Journal of Software Innovation, vol. 7, no. 2, pp. 41–49, Apr 2019. doi:https://doi.org/10.4018/ijsi.2019040104
K. Turksoy, A. Roy and A. Cinar, Real-Time Model-Based Fault Detection of Continuous Glucose Sensor Measurements. IEEE Transactions on Biomedical Engineering, 64 (2017) 1437–1445. https://doi.org/10.1109/TBME.2016.2535412.
C.-T. Yang, W.-L. Chou, C.-H. Hsu and A. Cuzzocrea, On Improvement of Cloud Virtual Machine Availability with Virtualization Fault Tolerance Mechanism. The Journal of Supercomputing, 69 (2013) 1103–1122. https://doi.org/10.1007/s11227-013-1045-1.
M. Mocanu, C. Unger, M. Pfost, P. Waltereit and R. Reiner, Thermal Stability and Failure Mechanism of Schottky Gate AlGaN/GaN HEMTs. IEEE Transactions on Electron Devices, 64 (2017) 848–855. https://doi.org/10.1109/TED.2016.2633725.
Acknowledgements
This work was supported by Science and Technology Projects Funded by State Grid Corporation of China (5200202024105A0000) and Henan Province Key R&D and Promotion Special Project (212102210016).
Funding
Science and Technology Projects Funded by State Grid Corporation of China, 5200202024105A0000, Hao-wei Yao, Henan Province Key R&D and Promotion Special Project, 212102210016, Hao-wei Yao.
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Yao, Hw., Li, Yx., Zhang, Y. et al. Failure Mechanism and Analysis Diagnosis of LED. MAPAN 37, 195–206 (2022). https://doi.org/10.1007/s12647-021-00518-1
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DOI: https://doi.org/10.1007/s12647-021-00518-1