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

Abstract 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 L 50 lifetime. The luminous characteristics of WLEDs employing different combinations of quantum dots and phosphor are investigated in this work. Additionally, investigations on the L 50 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 L 50 lifetime of WLEDs, achieving high predictive accuracy with R 2 of 0.986.

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Publication Details

Journal
Advanced Materials Technologies
Published
2024-12-18
DOI
https://doi.org/10.1002/admt.202401555
Citations
1
Primary Topic
Quantum Dots Synthesis And Properties
Type
article
Field-Weighted Citation Impact
0.12

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article

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

Yijun Lü, Changdong Tong, Weijie Guo, Hao Yang et al.
1 citations
Advanced Materials Technologies
Quantum Dots Synthesis And Properties
0.12
article

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

Yijun Lü, Changdong Tong, Weijie Guo, Hao Yang, Zhong Chen, Fengyun Gao
article en
1 citations

Abstract

Abstract 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 L 50 lifetime. The luminous characteristics of WLEDs employing different combinations of quantum dots and phosphor are investigated in this work. Additionally, investigations on the L 50 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 L 50 lifetime of WLEDs, achieving high predictive accuracy with R 2 of 0.986.

Advanced Materials TechnologiesVol. 10(8)
Xiamen University (CN)
National Natural Science Foundation of China, Fujian Provincial Department of Science and Technology
Affordable and clean energy
Openalex Percentile: Top 57%
Quantum Dots Synthesis And Properties
0.12
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Machine Learning‐Assisted Hybrid Package of White Light‐Emitting Diodes Employing Quantum Dots and Phosphor — Yijun Lü, Changdong Tong, et al. · Advanced Materials Technologies (2024) | TGRS Research Map | TGRS