A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning

• Published in: Applied sciences Vol. 13; no. 18; p. 10129
• Main Authors: Chung, Myung-Ae, Kim, Tae-Hoon, Kim, Kyung-A, Kang, Min-Soo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Algorithms; Artificial intelligence; Circuit components; Cost control; dark spot; Data mining; Datasets; Deep learning; Defective products; Defects; Humidity; Light-emitting diodes; machine learning; Manufacturing; OLED; South Korea
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app131810129

In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can arise owing to various factors, but dark spots are predominantly identified and studied. Therefore, actual dark spot images were required for this study. However, obtaining real data is challenging because of security concerns in the OLED industry. Therefore, a Solver program utilizing the finite element method (FEM) was employed to generate 2000 virtual dark spot images. The generated images were categorized into two groups: initial images of dark spots and images obtained after 10,000 h. The pixel values of these dark spot images were adjusted for efficient recognition and analysis. Furthermore, CNN, ResNet-50, and VGG-16 were implemented to apply the optimal deep learning algorithms. The results showed that the VGG-16 algorithm performed the best. A defect detection model was created based on the performance metrics of the deep learning algorithms. The model was trained using 1300 dark spot images and validated using 600 dark spot images. The validation results indicated an accuracy of 0.988 and a loss value of 0.026. A defect detection model that utilizes the VGG-16 algorithm was considered suitable for distinguishing dark spot images. To test the defect detection model, 100 images of dark spots were used. The experimental results indicated an accuracy of 89%. The images were classified as acceptable or defective based on the threshold values. By applying the VGG-16 deep learning algorithm to the defect detection model, we can enhance the yield of OLED products, reduce production costs, and contribute significantly to the advancement of OLED display manufacturing technology.