# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 1 Introduction  
Modern society is entering an era of digitalization with the Fourth Industrial Revolution, and the significance of display technology in visually representing various types of information has increased. Recently, OLED displays have garnered attention as the next generation display technology. However, the OLED market faces competitive challenges due to the low cost display strategies employed by global companies. Nevertheless, the OLED market is growing through continuous investments and technological developments by domestic companies [1]. If this growth continues steadily, the domestic OLED industry is expected to secure a competitive edge and evolve into a key player in the future market, leading to the development of next generation display technologies [2]. Therefore, this study aims to explore the growth potential of the domestic OLED industry and proposes strategies for its development. This study also focuses on improving the yield by applying an optimal deep learning algorithm to detect defects in OLED cells. Detecting defects within OLED cells is crucial for several reasons. Firstly, it aims to prevent the production of faulty items during the manufacturing process. Defective products can compromise quality and performance, potentially leading to unreliable products for consumers. Therefore, accurately identifying and addressing defects within OLED cells is of paramount importance. Furthermore, reducing the occurrence of product disposal due to defects is economically significant within the OLED industry. Identifying defects early and taking corrective actions can prevent the need for product disposal or rework, thereby resulting in cost savings. According to the study by [PERSON] et al., utilizing artificial intelligence technology to automatically detect defects has been explored [3]. They developed and validated a network that could automatically identify defects, such as scratches, on the surface of products. This research emphasizes the potential for enhancing product quality and reducing the number of defective items through effective defect detection methods. Efficiently identifying and addressing cell defects can increase production yield. This can contribute to the advancement of OLED display manufacturing technology and have positive effects on the OLED display market. The yield of OLEDs is determined throughout the production process, from manufacturing to encapsulation. The careful detection and identification of defects or issues are crucial during the OLED panel manufacturing process. Generally, OLED cell defects arise owing to the use of faulty materials, improper process conditions, or impacts during transportation in the manufacturing process [4]. The encapsulation process, which occurs after the preceding manufacturing steps, involves covering the OLED panel with encapsulation glass to protect it from external influences and ensure long term usage without interference. The encapsulation process is essential because the organic materials and electrodes in OLEDs are highly sensitive to oxygen and moisture, which can lead to the loss of luminous characteristics. This step directly preserves or enhances the lifespan of the OLED panels [5]. However, cell defects occur even with the encapsulation process used to improve the OLED yield. This is because defects can be introduced during the manufacturing process through impurity infiltration or the introduction of moisture and oxygen into organic materials. Despite thorough processing during manufacturing and encapsulation, complete detection and discrimination between defects during the inspection is challenging [6]. During the inspection process, visual inspection by human operators may result in misjudgments, where good OLED panels may be incorrectly classified as defective or vice versa. Defects in OLEDs can be caused by degradation during panel production, which can be attributed to physical factors such as temperature, humidity, and pressure.