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

Authors
Park, Il-HooLee, Song EunKim, YunjeongYou, Seung YeolKim, Young KwanKim, Gyu-Tae
Issue Date
2월-2022
Publisher
ELSEVIER
Keywords
OLEDs; Compact modeling; Deep learning; Lifetime assessment; Automatic successive measurements; 4,4 ' -N,N ' -dicarbazole-biphenyl (CBP)
Citation
ORGANIC ELECTRONICS, v.101
Indexed
SCIE
SCOPUS
Journal Title
ORGANIC ELECTRONICS
Volume
101
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135245
DOI
10.1016/j.orgel.2021.106404
ISSN
1566-1199
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.
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공과대학 (전기전자공학부)
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