A Study on OLED Cell Simulation and Detection Phases Based on the A2G Algorithm for Artificial Intelligence Application

In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 14; p. 8016
Main Authors Han, Dong-Hun, Jeong, Yeong-Hoon, Kang, Min-Soo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Accuracy
Algorithms
Artificial intelligence
big data
Computer simulation
Deep learning
Defects
Humidity
Machine learning
Manufacturers
Manufacturing
Monte Carlo simulation
OLED
Organic light emitting diodes
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Summary:In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13148016