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

Research output: Contribution to journalArticlepeer-review

1 Citation (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 languageEnglish
Article number106404
JournalOrganic Electronics
Volume101
DOIs
Publication statusPublished - 2022 Feb

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
  • Chemistry(all)
  • Condensed Matter Physics
  • Materials Chemistry
  • Electrical and Electronic Engineering

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