Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique

Il Hoo Park; Song Eun Lee; Yunjeong Kim; Seung Yeol You; Young Kwan Kim; Gyu Tae Kim

doi:10.1016/j.orgel.2021.106404

Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique

Il Hoo Park
, Song Eun Lee
, Yunjeong Kim
, Seung Yeol You
, Young Kwan Kim^*
, Gyu Tae Kim

^*Corresponding author for this work

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

13 Citations (Scopus)

Abstract

Simple and efficient lifetime modeling of organic light emitting diodes (OLED) are suggested by in-situ successive AC/DC measurements with reinforcement assessments of machine learning. AC/DC device parameters of phosphorescent OLED devices with multiple transport layers are monitored and analyzed by third-order parallel R//C circuit model with deep learning algorithm. The prediction efficiency of the lifetime assessment is enhanced by combining in-situ AC/DC device parameters, reducing the assessment time compared to conventional constant-stress test methods.

Original language	English
Article number	106404
Journal	Organic Electronics
Volume	101
DOIs	https://doi.org/10.1016/j.orgel.2021.106404
Publication status	Published - 2022 Feb

Bibliographical note

Funding Information:
This work was supported by Samsung Display Co. Ltd. Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT (NRF-2017M3A7B4049119), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2015R1A6A1A03031833).

Funding Information:
This work was supported by Samsung Display Co. Ltd., Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT ( NRF-2017M3A7B4049119 ), and Basic Science Research Program through the National Research Foundation of Korea ( NRF ) funded by the Ministry of Education (No. 2015R1A6A1A03031833 ).

Publisher Copyright:
© 2021

Keywords

4,4′-N,N′-dicarbazole-biphenyl (CBP)
Automatic successive measurements
Compact modeling
Deep learning
Lifetime assessment
OLEDs

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Biomaterials
General Chemistry
Condensed Matter Physics
Materials Chemistry
Electrical and Electronic Engineering

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10.1016/j.orgel.2021.106404

Cite this

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title = "Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique",

abstract = "Simple and efficient lifetime modeling of organic light emitting diodes (OLED) are suggested by in-situ successive AC/DC measurements with reinforcement assessments of machine learning. AC/DC device parameters of phosphorescent OLED devices with multiple transport layers are monitored and analyzed by third-order parallel R//C circuit model with deep learning algorithm. The prediction efficiency of the lifetime assessment is enhanced by combining in-situ AC/DC device parameters, reducing the assessment time compared to conventional constant-stress test methods.",

keywords = "4,4′-N,N′-dicarbazole-biphenyl (CBP), Automatic successive measurements, Compact modeling, Deep learning, Lifetime assessment, OLEDs",

author = "Park, \{Il Hoo\} and Lee, \{Song Eun\} and Yunjeong Kim and You, \{Seung Yeol\} and Kim, \{Young Kwan\} and Kim, \{Gyu Tae\}",

note = "Funding Information: This work was supported by Samsung Display Co. Ltd. Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT (NRF-2017M3A7B4049119), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2015R1A6A1A03031833). Funding Information: This work was supported by Samsung Display Co. Ltd., Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT ( NRF-2017M3A7B4049119 ), and Basic Science Research Program through the National Research Foundation of Korea ( NRF ) funded by the Ministry of Education (No. 2015R1A6A1A03031833 ). Publisher Copyright: {\textcopyright} 2021",

year = "2022",

month = feb,

doi = "10.1016/j.orgel.2021.106404",

language = "English",

volume = "101",

journal = "Organic Electronics",

issn = "1566-1199",

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T1 - Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique

AU - Park, Il Hoo

AU - Lee, Song Eun

AU - Kim, Yunjeong

AU - You, Seung Yeol

AU - Kim, Young Kwan

AU - Kim, Gyu Tae

N1 - Funding Information: This work was supported by Samsung Display Co. Ltd. Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT (NRF-2017M3A7B4049119), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2015R1A6A1A03031833). Funding Information: This work was supported by Samsung Display Co. Ltd., Nano-Material Technology Development Program through the NRF funded by Ministry of Science and ICT ( NRF-2017M3A7B4049119 ), and Basic Science Research Program through the National Research Foundation of Korea ( NRF ) funded by the Ministry of Education (No. 2015R1A6A1A03031833 ). Publisher Copyright: © 2021

PY - 2022/2

Y1 - 2022/2

N2 - Simple and efficient lifetime modeling of organic light emitting diodes (OLED) are suggested by in-situ successive AC/DC measurements with reinforcement assessments of machine learning. AC/DC device parameters of phosphorescent OLED devices with multiple transport layers are monitored and analyzed by third-order parallel R//C circuit model with deep learning algorithm. The prediction efficiency of the lifetime assessment is enhanced by combining in-situ AC/DC device parameters, reducing the assessment time compared to conventional constant-stress test methods.

AB - Simple and efficient lifetime modeling of organic light emitting diodes (OLED) are suggested by in-situ successive AC/DC measurements with reinforcement assessments of machine learning. AC/DC device parameters of phosphorescent OLED devices with multiple transport layers are monitored and analyzed by third-order parallel R//C circuit model with deep learning algorithm. The prediction efficiency of the lifetime assessment is enhanced by combining in-situ AC/DC device parameters, reducing the assessment time compared to conventional constant-stress test methods.

KW - 4,4′-N,N′-dicarbazole-biphenyl (CBP)

KW - Automatic successive measurements

KW - Compact modeling

KW - Deep learning

KW - Lifetime assessment

KW - OLEDs

UR - https://www.scopus.com/pages/publications/85120621668

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DO - 10.1016/j.orgel.2021.106404

M3 - Article

AN - SCOPUS:85120621668

SN - 1566-1199

VL - 101

JO - Organic Electronics

JF - Organic Electronics

M1 - 106404

ER -

Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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