32‐2: Multi AI Approaches for Improving OLED Display Pattern Repair in Manufacturing Processes

Abstract

The increasing resolution and complexity of OLED (organic light-emitting diode) panels have led to a rise in defects during the manufacturing process. Repairing these defects is crucial for enhancing the competitiveness of OLED manufacturing. The skill level of inspection operators has traditionally been the foundation of the repair system. However, an Image Processing-based Rule Base Auto Repair System has partially replaced the reliance on inspection operators. Yet, it falls short of completely replacing them without additional verification and re-repair. To overcome the challenges of detecting overlap areas between layers and detecting defects in gap regions, which are major causes of reduced accuracy in repair area detection, we explored the use of DL (deep learning) techniques. In this proposal, we introduced segmentation techniques commonly used in autonomous driving and medical fields to improve the performance of layer detection for repair targets. By developing a DL Auto Repair Process based on accurate segmentation detection of repair targets, we established a stable system by combining classification DL and detection DL. The proposed technology has led to the development of an Auto Repair System that eliminates the need for inspection operators for the specific defects. The proposed method has been successfully applied in real manufacturing processes