A novel multiple-stress-based predictive model of LEDs for rapid lifetime estimation

作者：Huang, Su-Dan; Zhou, Lin; Cao, Guang-Zhong^*; Liu, Huai-Yuan; Hu, Yi-Min; Jing, Gang; Cao, Ming-Gao; Xiao, Wen-Peng; Liu, Yan^*

来源：Microelectronics Reliability, 2017, 78(Nov.): 46-52.

DOI：10.1016/j.microrel.2017.07.094

• Adaptive genetic algorithm (AGA)
• Multiple stresses
• Light-emitting diodes (LEDs)
• Lifetime estimation
• Predictive model

This paper proposes a novel multiple-stress-based predictive model (MSBPM) to rapidly assess the lifetime of light-emitting diodes (LEDs). The MSBPM addresses the lifetime estimation of LEDs with respect to temperature, humidity, and current; these three stresses are rarely considered simultaneously in the assessment of reliability. Using several degradation data sets from accelerated life tests (ALTs) without using extrapolation method, a designed adaptive genetic algorithm is employed to identify five unknown parameters of the MSBPM. A simulation of the proposed MSBPM is presented as validation. By applying the degradation data from the ALTs under high stresses, an MSBPM for the LEDs is established. Under the nominal conditions of 25 degrees C/22.5% RH and a current of 0.35 A, the lifetime of the LED is estimated using the established MSBPM. The effectiveness of the proposed MSBPM is further verified through the estimated results.

• 出版日期2017-11
• 单位深圳清华大学研究院; 深圳大学