Deep Learning Segmentation Modeling for SiN, SiO2 Film Deposition Process Defect of High Bandwidth Memory

• Published in: Journal of semiconductor technology and science Vol. 23; no. 5; pp. 251 - 257
• Main Authors: Whoang, Intae, Cho, Chinkwan, Hong, Jin-Hee, Son, Dong-Hee, Lim, Byung-Yoon, Kim, Jin-Pyung, Bang, Kijun
• Format: Journal Article
• Language: English
• Published: 대한전자공학회 01.10.2023
• Subjects: 전기공학; deep learning; segmentation; HBM; WLP; chemical vapor deposition film; TSV
• ISSN: 1598-1657; 2233-4866; 2233-4866; 1598-1657
• DOI: 10.5573/JSTS.2023.23.5.251

At SK hynix Wafer Level Package (WLPKG) line, there are plenty of measuring and inspection steps to ensure the quality of High Bandwidth Memory (HBM). Although most of the measuring and inspection steps are handled automatically, some of the steps still need confirmation from line operators with their naked eye and skills. Since the operators' skills are different, sometimes it causes human errors, and these risks become chronic problems for the company. To solve this problem, Package & Test (P&T) group at SK hynix has been steadily promoting the inspection automation system using deep learning. However, deep learning has the disadvantage of not providing interpretation information, such as which area is actually defective in the target image and its shape for the ‘Excellent’ result of outputs. In this paper, we will introduce cases in which defect patterns are automatically extracted from inspection images taken during the SiN / SiO2 film deposition process by using two deep-learning segmentation models. The performance of the technology to be introduced was demonstrated by comparing the Mean IoU value between the extracted defect image and label mask. Through the proposed technology, we intend to contribute to unmanned inspection verification tasks in the future and accelerate the realization of Industry 4.0. KCI Citation Count: 0