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Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique
- Authors
- Park, I.-H.; Lee, S.E.; Kim, Y.; You, S.Y.; Kim, Y.K.; Kim, G.-T.
- Issue Date
- 1-Feb-2022
- Publisher
- Elsevier B.V.
- Keywords
- 4,4′-N,N′-dicarbazole-biphenyl (CBP); Automatic successive measurements; Compact modeling; Deep learning; Lifetime assessment; OLEDs
- Citation
- Organic Electronics, v.101
- Journal Title
- Organic Electronics
- Volume
- 101
- 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. © 2021
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Related Researcher
- Kim, Young Kwan
- Engineering (Applied Science)