91‐2: Diffusion‐Based AI Solutions for Stabilizing Automated OLED Cell Repair Processes and Enhancing New Product Performance

In the OLED manufacturing process, the Cell Repair process plays a critical role in preventing defect leakage by identifying defects using an optical scope and then either repairing or rejecting them. The causes of Pixel‐Off defects addressed during this process can be categorized into two types: floating foreign substances on the top of the cell and foreign substances between the layered structures. However, in Rule‐Based Auto Repair Systems that rely on image processing, distinguishing between these two types of defects is not feasible. As a result, the affected panels are subjected to final judgment by a separate inspector, leading to a combination of issues such as decreased equipment efficiency, reduced productivity, and the need for additional inspectors. Therefore, the company has introduced Image Deep Learning (DL) techniques to enable the automatic classification of defect types, achieving significant automation in the process. However, during the initial production phase of new products, the lack of sufficient defect data sets for DL training results in very low model accuracy, making its application impractical. This proposal addresses the DL accuracy issue in the early stages of new product production by adopting a defect generation method using diffusion‐based generative AI, significantly enhancing the performance of the DL classification system. This technique requires only a single defect sample image in the early stages of new product development, eliminating the need for months of defect data collection. At the same time, it enables the immediate deployment of the defect classification DL system, which was previously unusable during the initial mass production phase of new products. The proposed method has been successfully implemented in the production line process.