Lifetime Prediction of Ultraviolet Light-Emitting Diodes Using a Long Short-Term Memory Recurrent Neural Network

• Published in: IEEE electron device letters 2020-12, Vol.41 (12), p.1817-1820
• Main Authors: Jing, Zhou, Liu, Jie, Ibrahim, Mesfin Seid, Fan, Jiajie, Fan, Xuejun, Zhang, Guoqi
• Format: Article
• Language: English
• Subjects: Degradation; Failure detection; IESNA TM-21 recommendation; Industry standards; lifetime prediction; Light emitting diodes; Long short term memory; Maintenance engineering; Neural networks; Product development; recurrent neural network; Recurrent neural networks; Regression analysis; Robustness (mathematics); Statistical analysis; Ultraviolet LED; Ultraviolet radiation; Weibull distribution
• ISSN: 0741-3106; 1558-0563
• DOI: 10.1109/LED.2020.3034567

Ultraviolet light-emitting diodes (UV LEDs) play an important role in inactivating novel coronavirus pneumonia, but the lack of rapid lifetime prediction can easily cause untimely failure detection, long product development cycles, and high costs. This study predicts the lifetime of UV LEDs based on the long short-term memory (LSTM) recurrent neural network (RNN). First, the equipment setup was designed to conduct an aging test to obtain a predicted length of life for the UV LED samples using a Weibull distribution. Next, LSTM RNN was employed to predict the lifetime of the UV LEDs based on the radiation power degradation. The results were then compared with those from nonlinear least squares (NLS) regression recommended by the IESNA TM-21 industry standard. Finally, the robustness of the two methods was analyzed by changing the starting times of the predictions. The results showed that the LSTM RNN proposed in this letter reveals not only a 29.7% lower lifetime prediction error compared with the NLS regression, but also a more stable robustness. Thus, the LSTM RNN method is found to be more accurate and more robust in predicting the lifetime of UV LEDs.