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Zhen Ding Technology Holding Limited, a global PCB industry leader, and National Tsing Hua University (NTHU) have achieved significant results through their industry-academia collaboration, with five smart manufacturing research papers selected for presentation at the 22nd IEEE International Conference on Automation Science and Engineering (IEEE CASE 2026), held August 17-21 in Shenyang, China.
IEEE CASE is a leading international conference in automation science and intelligent systems. The recognition highlights Zhen Ding’s continued investment in advanced technology R&D and supports the company’s digital transformation and development of smart factories worldwide.
The Zhen-Ding & NTHU Joint Research Center, established in 2020, focuses on PCB smart manufacturing and advanced process technologies. The center has entered its second five-year phase, with total R&D funding exceeding NT$100 million across both phases. Its current research focuses on AI-enabled smart manufacturing, high-end IC substrates, supply chain resilience, and heterogeneous integration.
Zhen Ding Chairman Charles Shen said the achievements reflect the strong collaboration between NTHU faculty, students, and Zhen Ding employees. The company will continue to strengthen industry-academia cooperation and scholarship programs to develop future talent in PCB and semiconductor technologies.
At IEEE CASE 2026, Yu-Chieh Huang, Head of Zhen Ding’s Intelligent Technology R&D and Translation Center, chaired a special session on smart manufacturing solutions for PCB and semiconductor industries. The five joint research projects addressed production process optimization, smart logistics scheduling, supply chain resilience, raw material risk management, and AI-enabled management for high-mix, low-volume manufacturing environments.
One featured paper, “Online Hierarchical RF Signal Anomaly Detection and Localization Technology,” developed by NTHU master’s student Chen-Yi Kuo, presents an AI-based system for semiconductor Advanced Process Control. The system combines multi-scale partitioning, weighted voting, and AI agents to detect defects and identify abnormal frequencies within seconds, improving anomaly diagnosis and production efficiency.
The technology was developed with guidance from Zhen Ding industry experts and validated in actual production environments. Chen-Yi Kuo has since joined Zhen Ding, highlighting the role of the industry-academia partnership in developing high-potential talent.
