Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes

Cuili Chen; Xiongfeng Lin; Xian Gang Wu; Hui Bao; Longjia Wu; Xiangmin Hu; Yongyou Zhang; Di Yang; Wenjun Hou; Weiran Cao; Haizheng Zhong

doi:10.1021/acs.nanolett.3c01491

Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes

Cuili Chen, Xiongfeng Lin, Xian Gang Wu, Hui Bao, Longjia Wu, Xiangmin Hu^*, Yongyou Zhang, Di Yang, Wenjun Hou, Weiran Cao, Haizheng Zhong^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

The operational stability of the blue quantum dot light-emitting diode (QLED) has been one of the most important obstacles to initialize its industrialization. In this work, we demonstrate a machine learning assisted methodology to illustrate the operational stability of blue QLEDs by analyzing the measurements of over 200 samples (824 QLED devices) including current density-voltage-luminance (J-V-L), impedance spectra (IS), and operational lifetime (T95@1000 cd/m²). The methodology is able to predict the operational lifetime of the QLED with a Pearson correlation coefficient of 0.70 with a convolutional neural network (CNN) model. By applying a classification decision tree analysis of 26 extracted features of J-V-L and IS curves, we illustrate the key features in determining the operational stability. Furthermore, we simulated the device operation using an equivalent circuit model to discuss the device degradation related operational mechanisms.

Original language	English
Pages (from-to)	5738-5745
Number of pages	8
Journal	Nano Letters
Volume	23
Issue number	12
DOIs	https://doi.org/10.1021/acs.nanolett.3c01491
Publication status	Published - 28 Jun 2023

Keywords

blue QLED
equivalent circuit model
machine learning
stability analysis

Access to Document

10.1021/acs.nanolett.3c01491

Cite this

Chen, C., Lin, X., Wu, X. G., Bao, H., Wu, L., Hu, X., Zhang, Y., Yang, D., Hou, W., Cao, W., & Zhong, H. (2023). Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes. Nano Letters, 23(12), 5738-5745. https://doi.org/10.1021/acs.nanolett.3c01491

@article{c3df452e0151456eaa8c6c19458399d1,

title = "Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes",

abstract = "The operational stability of the blue quantum dot light-emitting diode (QLED) has been one of the most important obstacles to initialize its industrialization. In this work, we demonstrate a machine learning assisted methodology to illustrate the operational stability of blue QLEDs by analyzing the measurements of over 200 samples (824 QLED devices) including current density-voltage-luminance (J-V-L), impedance spectra (IS), and operational lifetime (T95@1000 cd/m2). The methodology is able to predict the operational lifetime of the QLED with a Pearson correlation coefficient of 0.70 with a convolutional neural network (CNN) model. By applying a classification decision tree analysis of 26 extracted features of J-V-L and IS curves, we illustrate the key features in determining the operational stability. Furthermore, we simulated the device operation using an equivalent circuit model to discuss the device degradation related operational mechanisms.",

keywords = "blue QLED, equivalent circuit model, machine learning, stability analysis",

author = "Cuili Chen and Xiongfeng Lin and Wu, {Xian Gang} and Hui Bao and Longjia Wu and Xiangmin Hu and Yongyou Zhang and Di Yang and Wenjun Hou and Weiran Cao and Haizheng Zhong",

note = "Publisher Copyright: {\textcopyright} 2023 American Chemical Society.",

year = "2023",

month = jun,

day = "28",

doi = "10.1021/acs.nanolett.3c01491",

language = "English",

volume = "23",

pages = "5738--5745",

journal = "Nano Letters",

issn = "1530-6984",

publisher = "American Chemical Society",

number = "12",

}

TY - JOUR

T1 - Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes

AU - Chen, Cuili

AU - Lin, Xiongfeng

AU - Wu, Xian Gang

AU - Bao, Hui

AU - Wu, Longjia

AU - Hu, Xiangmin

AU - Zhang, Yongyou

AU - Yang, Di

AU - Hou, Wenjun

AU - Cao, Weiran

AU - Zhong, Haizheng

PY - 2023/6/28

Y1 - 2023/6/28

N2 - The operational stability of the blue quantum dot light-emitting diode (QLED) has been one of the most important obstacles to initialize its industrialization. In this work, we demonstrate a machine learning assisted methodology to illustrate the operational stability of blue QLEDs by analyzing the measurements of over 200 samples (824 QLED devices) including current density-voltage-luminance (J-V-L), impedance spectra (IS), and operational lifetime (T95@1000 cd/m2). The methodology is able to predict the operational lifetime of the QLED with a Pearson correlation coefficient of 0.70 with a convolutional neural network (CNN) model. By applying a classification decision tree analysis of 26 extracted features of J-V-L and IS curves, we illustrate the key features in determining the operational stability. Furthermore, we simulated the device operation using an equivalent circuit model to discuss the device degradation related operational mechanisms.

AB - The operational stability of the blue quantum dot light-emitting diode (QLED) has been one of the most important obstacles to initialize its industrialization. In this work, we demonstrate a machine learning assisted methodology to illustrate the operational stability of blue QLEDs by analyzing the measurements of over 200 samples (824 QLED devices) including current density-voltage-luminance (J-V-L), impedance spectra (IS), and operational lifetime (T95@1000 cd/m2). The methodology is able to predict the operational lifetime of the QLED with a Pearson correlation coefficient of 0.70 with a convolutional neural network (CNN) model. By applying a classification decision tree analysis of 26 extracted features of J-V-L and IS curves, we illustrate the key features in determining the operational stability. Furthermore, we simulated the device operation using an equivalent circuit model to discuss the device degradation related operational mechanisms.

KW - blue QLED

KW - equivalent circuit model

KW - machine learning

KW - stability analysis

UR - http://www.scopus.com/inward/record.url?scp=85164211116&partnerID=8YFLogxK

U2 - 10.1021/acs.nanolett.3c01491

DO - 10.1021/acs.nanolett.3c01491

M3 - Article

C2 - 37294577

AN - SCOPUS:85164211116

SN - 1530-6984

VL - 23

SP - 5738

EP - 5745

JO - Nano Letters

JF - Nano Letters

IS - 12

ER -

Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this