Study of High-Brightness LED Samples Aged Under Stress Temperature Conditions: Electrical Characterizations and Signature Evolution Analysis

Light-emitting diode (LED) components used in lighting or backlighting applications remain operational for a long time, but in some cases, early catastrophic failures and degradation of the photocolorimetric characteristics are observed. Hence, manufacturers are highly interested in early detection of degradation mechanisms and the relationship between electrical signature evolutions and aging in order to predict the remaining lifetime of LED components. This paper describes a methodology to identify future characteristics degradation based on electrical signatures observed within the first hundred hours using an accelerating aging method. For that purpose, four kinds of high-power LEDs are submitted to temperature stress conditions that provide early aging electrical signatures. The first part of this paper describes the experimental setup and the aging protocol. In the second part, we present signatures obtained via current-voltage (I-V ) and capacitance-voltage (C-V ) data.

[1]  Cheng-I Chen,et al.  An efficient Prony's method for time-varying power system harmonic estimation , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[2]  F. Rachidi,et al.  Prony Series Representation for the Lightning Channel Base Current , 2012, IEEE Transactions on Electromagnetic Compatibility.

[3]  J. Driesen,et al.  Exponential parameters measurement using a modified Prony method , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[4]  A. Poppe,et al.  A general multi-domain LED model and its validation by means of AC thermal impedance , 2013, 29th IEEE Semiconductor Thermal Measurement and Management Symposium.

[5]  Donald Neamen,et al.  An Introduction to Semiconductor Devices , 2005 .

[6]  Fang Liu,et al.  A Study of Accelerated Life Test of White OLED Based on Maximum Likelihood Estimation Using Lognormal Distribution , 2012, IEEE Transactions on Electron Devices.

[7]  S. van Riesen,et al.  Accelerated ageing tests on III-V solar cells , 2003, 3rd World Conference onPhotovoltaic Energy Conversion, 2003. Proceedings of.

[8]  Gordon K. Smyth,et al.  A Modified Prony Algorithm for Exponential Function Fitting , 1995, SIAM J. Sci. Comput..

[9]  Nadarajah Narendran,et al.  Developing an accelerated life test method for LED drivers , 2009, Optical Engineering + Applications.

[10]  S. Hui,et al.  A general photo-electro-thermo-temporal theory for light-emitting diode (LED) systems , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[11]  E. Schubert,et al.  Junction–temperature measurement in GaN ultraviolet light-emitting diodes using diode forward voltage method , 2004 .

[12]  Z. Vaitonis,et al.  Measurement of the junction temperature in high-power light-emitting diodes from the high-energy wing of the electroluminescence band , 2008 .

[13]  S. Buso,et al.  Performance Degradation of High-Brightness Light Emitting Diodes Under DC and Pulsed Bias , 2008, IEEE Transactions on Device and Materials Reliability.

[14]  Alberto Bellini,et al.  A test bench for accelerated thermal ageing of III–V concentration solar cells using forward bias injection , 2011, 2011 IEEE Energy Conversion Congress and Exposition.

[15]  Kenneth Holmström,et al.  A review of the parameter estimation problem of fitting positive exponential sums to empirical data , 2002, Appl. Math. Comput..

[16]  Guido Carpinelli,et al.  Adaptive Prony method for waveform distortion detection in power systems , 2007 .

[17]  Dan Pitica,et al.  Accelerated ageing tests for predicting capacitor lifetimes , 2011, 2011 IEEE 17th International Symposium for Design and Technology in Electronic Packaging (SIITME).

[18]  Statistical analysis of diagnostic indicators during an accelerated ageing experiment for XLPE cable specimens , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  H. Bleichner,et al.  The ambipolar diffusion coefficient in silicon: Dependence on excess‐carrier concentration and temperature , 1994 .

[20]  J. Faiz,et al.  Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection , 2007, IEEE Transactions on Power Delivery.

[21]  S. Saliu,et al.  Estimation of evoked potentials using total least squares Prony technique , 2006, Medical and Biological Engineering and Computing.

[22]  H. Grubin The physics of semiconductor devices , 1979, IEEE Journal of Quantum Electronics.