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

Park, I.-H.; Lee, S.E.; Kim, Y.; You, S.Y.; Kim, Y.K.; Kim, G.-T.

doi:10.1016/j.orgel.2021.106404

상세 보기

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

Park, I.-H.;
Lee, S.E.;
Kim, Y.;
You, S.Y.;
Kim, Y.K.;
외 1명

Citations

WEB OF SCIENCE

12

Citations

SCOPUS

13

초록

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. © 2021

키워드

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

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

저자: Park, I.-H.; Lee, S.E.; Kim, Y.; You, S.Y.; Kim, Y.K.; Kim, G.-T.

DOI: 10.1016/j.orgel.2021.106404

발행일: 2022-02-01

유형: Article

저널명: Organic Electronics

권: 101