# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 5 Conclusions  
In this study, deep learning algorithms were applied to detect defects in OLED cells. After comparing and analyzing various deep learning algorithms, VGG-16 was selected as the optimal algorithm. To facilitate defect identification, image data were necessary; however, due to the unique characteristics of the OLED industry, obtaining real image data was challenging. Therefore, a total of 2000 virtual dark spot image data were generated using the FEM Solver program. To select the optimal algorithm, a comparative analysis of the CNN, ResNet-50, and VGG-16 algorithms was conducted. Among them, VGG-16 demonstrated the best results, achieving an accuracy of 0.982, loss value of 0.053, specificity of 0.981, and recall of 0.985. VGG-16 was selected to create an OLED cell defect discrimination model based on these performance metrics. The model was trained using 1300 defect images and validated using 600 images, resulting in an accuracy of 0.988 and a loss value of 0.026. The performance of the model indicated its potential applicability in research. In the testing phase, the model achieved an accuracy of 0.89 with 100 defect images. Based on the study, it was possible to identify cell defects by implementing a cell defect classification model using the VGG-16 algorithm. While the study did not utilize actual images of dark spots, it successfully simulated the process of dark spot formation using virtual images, which were driven by relevant physical phenomena. While the current OLED industry relies on human inspection for defect detection, utilizing artificial intelligence technology to identify cell defects could contribute to the advancement of display manufacturing techniques. This applicability extends not only to the OLED industry but also to manufacturing and inspection processes within the broader electronics sector. Furthermore, the superior performance of the VGG-16 in image recognition highlights its ability to detect and classify cell defects efficiently. These results suggest that using deep learning technology for defect detection and classification can decrease the number of defective products and enhance production efficiency. Production costs can also be reduced by automating the defect discrimination process. Overall, this study highlights the potential contributions of deep learning in advancing the OLED display industry by improving production efficiency and quality. For future research, we propose a study that focuses on detecting defects in OLED cells by generating high-quality images using additional variables. Furthermore, by utilizing high-performance cameras for image data generation and improving the defect detection system with expert input, research on cell defect detection is expected to advance with greater precision.  
Figure 4: Result of non-pass dataset. (**a**) Initial dark spot shows the form at the time of the first occurrence of a dark spot. (**b**) The form of the dark spot after 10,000 h.  
**Author Contributions:** Author Contributions: M.-A.C. and T.-H.K.: Writing--Original Draft, Data Curation, Software, and Visualization. K.-A.K.: Writing--Review and Editing. M.-S.K.: Conceptualization, Validation, Writing--Review and Editing, and Project Administration. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by the Institute of Information and Communications Technology Planning and Evaluation (www.iitp.kr, accessed on 1 March 2022), funded by the Ministry of Science and ICT (MSIT, Republic of Korea). (Project Number: 2022-0-00317).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and codes generated or used during the study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.