# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 4 Results  
In this study, a set of 100 virtually generated defect images was employed to assess the defect detection model. The defect detection model successfully recognized 100 images with defects. Among these, 25 initial defect images were classified as \"pass\" and 25 defect images after 10,000 h of testing. Among the 50 tested images, the model accurately classified 47 images as \"pass,\" while the remaining 3 images were classified as \"non-pass\". In order to illustrate the formed dark spots in detail, they have been marked with red circles, indicating the presence of dark spots in OLED cells.  
Figure 3 shows the results of applying the virtually generated defect images to the OLED cell-defect detection model. The model analyzes the size and distribution of defects inside the OLED cell to classify them as either pass or non-pass. The generated defect  
image in Figure 3a is the initial defect image. The defect detection model classified it as \"pass\" with a confidence score of 0.999, indicating that the defect image Figure 3a was classified as a defect-free sample. In contrast, Figure 3b represents a defective image after 10,000 h. After 10,000 h, dark spots have formed due to humidity, causing the shapes of the dark spots to superposition. As a result, a dispersed pattern emerges around the center of the dark spots.  
Analysis of the morphology of this defect revealed that, over time, the defects overlapped and darkened in appearance. In the case of the superposition defect image Figure 3b, the model confidently classified it as \"pass\" with a score of 1, indicating that it was also classified as a defect-free sample.  
Figure 4 shows the results of applying the virtually generated defect images to the defect detection model. A total of 50 defect images were used, including 25 initial defect images classified as non-pass and 25 defect images after 10,000 h. The model correctly classified 42 out of 50 images as non-pass and the remaining seven images as passes. The criteria for defect detection are based on the distribution area, size, and number of defects in the image data. Notably, the initial defect image Figure 4a was classified as a non-pass with a confidence score of 0.904, whereas the defect image after 10,000 h Figure 4b showed overlapping defect patterns and was classified as a non-pass with a confidence score of 0.136.  
The defect detection model used a classification threshold of 0.95. Scores equal to or higher than 0.95 were considered passes, while scores below 0.95 were considered non-pass. The overall accuracy for classifying 100 defect images, including initial and 10,000 h images, was 0.89. When applying real image data to the model for validation, it was possible to achieve an accuracy level of around 90%. These results demonstrate the effectiveness of the OLED cell defect detection model in accurately identifying and classifying defects based on the specified criteria.  
Figure 3: Result of 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.