Quantifying Overlay Printing Registration Accuracy with Object Keypoint Detection for Automated Process Control in FPE Printing

Juhuhn Kim, Younsu Jung, Sajjan Parajuli, Sagar Shrestha, Jinhwa Park, Gyoujin Cho, Jong-Seok Lee

Abstract: Achieving high-precision overlay printing registration accuracy (OPRA) is a critical challenge in the flexible printed electronics (FPE) printing process, particularly in roll-to-roll (R2R) gravure printing. Conventional OPRA quantification methods, primarily based on template matching, suffer from instability under real-world conditions, such as poor contrast, severe noise, and morphological variations in printed register markers. In this study, we propose a deep learning-based framework for marker detection and OPRA quantification, addressing key limitations of traditional approaches. Our method enables accurate localization of marker centers, overcoming inaccuracies caused by ink translucency, occlusion, and motion-induced blurring. Furthermore, it facilitates automatic real-time OPRA assessment, enabling statistical process control in FPE printing. Experimental evaluations demonstrate the superior robustness and reliability of the proposed approach.

External IDs:dblp:conf/case/KimJPSPCL25