Autonomous Thin-Film Profile Predictions for Inkjet-Printed OLEDs from Aerial Microscopic Images using Deep Learning

Thin-film measurements for display panels are essential for display research because they provide insight into improving the quality of display panels. To ensure the high-quality fabrication of display panels, display thin films are dried inside the vacuum chamber of a drying device. For this reason, with conventional thin film measurement techniques, it is difficult to observe in real time the surface profile changes of thin films inside the vacuum chamber. In this work, we present an approach to predict three-dimensional (3D) surface profiles from microscopic images of thin films captured from an aerial perspective using a U-Net-based prediction model. The U-Net-based prediction model can extract complex spatial features and correlations from input images by inferring three-dimensional shape structures. Results from the proposed approach show that various surface profiles of organic thin film can be predicted with a low error rate of approximately 1.3%. Furthermore, the approach offers remote real-time monitoring of the surface profiles of thin films only with aerial microscope images, thus facilitating potential advancements in numerous fields of printed electronics as well as thin films for displays.