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

Zhou Jing, Jie Liu, Mesfin Seid Ibrahim, Jiajie Fan, Xuejun Fan, Guoqi Zhang

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Article number9244084
Pages (from-to)1817-1820
Number of pages4
JournalIEEE Electron Device Letters
Volume41
Issue number12
DOIs
Publication statusPublished - 2020

Keywords

  • IESNA TM-21 recommendation
  • lifetime prediction
  • long short-term memory
  • recurrent neural network
  • Ultraviolet LED

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