# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 3 Research Methodology  
### Generating Virtual Dark Spot Image Dataset  
This study aimed to identify OLED dark spot images. However, obtaining real OLED dark spot image data is challenging owing to the confidentiality of the OLED industry. In addition, direct data generation methods are time consuming and costly. Therefore, inspired by this research report, we used a virtual data generation method to create a dataset of OLED dark spots. The data for the virtual dark spots were generated by considering the humidity exposure conditions, and the criteria for defects were established after 10,000 h of the OLED lifetime. The FEM was employed to analyze the growth of dark spots over time and to identify the factors influencing their distribution and size. The analysis results generated virtual dark spot data using the FEM Solver program [19]. Figure 1 depicts real and dark spot images generated using the FEM Solver. Figure 1a shows the dark spots formed inside the OLED cell. Figure 1b shows the virtual dark spot images generated using the FEM Solver program (V6.0). The figure shows the actual dark spot images and virtual dark spot images generated using the FEM Solver. Figure 1c shows the initial dark spot that has been magnified 100 times. The size of the dark spot is 1.2 \(\upmu\)m. The generated dark spot dataset consisted of 2000 images, classified into pass and non-pass groups based on their initial state and the state after 10,000 h. The pixel values and color intensities, represented as bit values, were adjusted for the classified dark spot images. According to a study, optical inspection systems are highly noise-sensitive [25]. Therefore, image filtering must be performed using the adjusted pixel values for defect detection. This approach improves the efficiency of filtering algorithms and reduces error rates when filtering algorithms are applied to images. Therefore, the attribute values were transformed to utilize the virtually generated image data for model training and validation. The original image data had a color depth of 24 bits and a resolution of 760 \(\times\) 609 pixels,  
and the file format was set to PNG. Finally, the modified image data were transformed into gray-scale images. A preprocessing procedure was carried out on the 1300 training dataset images utilized during the training process. This step aimed to enhance the model's performance by making adjustments to the image data. The original image dimensions were resized from 760 \(\times\) 609 to 300 \(\times\) 300, and the bit value representing the color format was set to 24. Additionally, to promote diversity and improve the generalization capability of the data, image augmentation was performed. This involved applying various transformations to the images to enhance the model's performance. To facilitate this, the image dimensions of the training dataset were set to 224 \(\times\) 224, and a batch size of 32 was chosen to perform data augmentation using the generated image data. Following the same procedure, the training dataset images were also processed for the validation dataset. In this process, the image dimensions of the validation dataset were resized from 760 \(\times\) 609 to 300 \(\times\) 300, and the color format bit value was set to 24. Similarly, data augmentation was applied to the validation dataset to improve the model's performance and generalization capability. The image dimensions were adjusted to 224 \(\times\) 224, and a batch size of 32 was employed for data augmentation. Consistent preprocessing steps were thus applied to the validation dataset images as well. After converting the image data format, a comparative analysis was conducted between the actual OLED dark spot dataset and the virtually generated dark spot dataset. Table 1 summarizes the virtually generated dark spot image data. A total of 2000 data samples were created and used in the training and validation processes.