采用量子点和荧光粉的机器学习辅助混合封装白光发光二极管

IF 6.4 3区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Advanced Materials Technologies Pub Date : 2024-12-18 DOI:10.1002/admt.202401555

Fengyun Gao, Hao Yang, Changdong Tong, Yijun Lu, Zhong Chen, Weijie Guo

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引用次数: 0

摘要

白光发光二极管（wled）以其高亮度、高效率、长寿命等优点被广泛应用于液晶显示背光器件中。然而，在保持L50寿命的同时，提高wled的色域仍然是困难的。本文研究了采用不同量子点和荧光粉组合的发光二极管的发光特性。此外，采用两步加速应力法对wled的L50寿命进行了研究。最后，提出了一种集成机器学习模型来预测wled的L50寿命，预测精度较高，R2为0.986。

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Machine Learning-Assisted Hybrid Package of White Light-Emitting Diodes Employing Quantum Dots and Phosphor

White light-emitting diodes (WLEDs), known for their high brightness, high efficiency, and long lifetime, are widely utilized in the backlight of liquid crystal displays. However, it is still difficult to improve the color gamut of WLEDs while maintaining the L₅₀ lifetime. The luminous characteristics of WLEDs employing different combinations of quantum dots and phosphor are investigated in this work. Additionally, investigations on the L₅₀ lifetime for WLEDs are carried out by employing a two-step accelerated stress method. Finally, an ensemble machine learning model is proposed to predict the L₅₀ lifetime of WLEDs, achieving high predictive accuracy with R² of 0.986.

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来源期刊

Advanced Materials Technologies Materials Science-General Materials Science

CiteScore

10.20

自引率

4.40%

发文量

566

期刊介绍： Advanced Materials Technologies Advanced Materials Technologies is the new home for all technology-related materials applications research, with particular focus on advanced device design, fabrication and integration, as well as new technologies based on novel materials. It bridges the gap between fundamental laboratory research and industry.