Dr. Chin-Yih Lin is an Associate Professor of Research in the Department of Industrial, Manufacturing and Systems Engineering (IMSE) at The University of Texas at El Paso (UTEP), with an appointment supported by the UT System Regents’ Research Excellence Program (RREP). His research integrates digital twins, Bayesian optimization, reinforcement learning, generative AI, and time-series foundation models to advance virtual metrology, process optimization, predictive maintenance, and intelligent factory operations in semiconductor manufacturing. Before joining UTEP, he served as a Research Specialist at National Cheng Kung University, leading AI-enabled semiconductor workflow projects, and as a Staff Specialist at Infineon Technologies AG, advancing virtual metrology and machine learning applications in manufacturing. His scholarly contributions include IEEE journal and conference publications, book chapters published by Wiley–IEEE Press, and U.S. patents and patent applications. His honors include the 2025 Taiwan National Invention Award (Silver Medal) and a fifth-place worldwide Scopus-based ranking in the research topic “Semiconductor Device Manufacturing, Neural Networks, and Process Control” (T.31043). He is an IEEE Senior Member and a member of the IEEE Robotics and Automation Society and the IEEE Computational Intelligence Society. His professional service includes reviewing for IEEE Transactions on Automation Science and Engineering, contributing to the IEEE P4128 working group on AI-enabled manufacturing deviation management, and serving as a 2026 Science Communication Ambassador for the IEEE Robotics and Automation Society. Further information about his research, publications, teaching, and collaborations is available on his personal website [https://sites.google.com/view/chin-yih-lin-semicon-ai-lab/home].
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| Term | Course | Section | Syllabus |
|---|---|---|---|
| Fall 2026 | MFG 5390 - Special Topics | 18616 | |
| Fall 2026 | IE 4395 - Special Topics Industrial Engr | 18615 |
| Term | Course | Section | Syllabus |
|---|---|---|---|
| Spring 2026 | MFG 5390 - Special Topics | 28474 | |
| Spring 2026 | ECE 5390 - Special Topics Electrical Engr | 28576 | |
| Spring 2026 | MECH 4395 - Special Topics in Mech. Engr. | 28583 | |
| Spring 2026 | MECH 5390 - Special Topics Mechanical Engr | 28584 |
| Term | Course | Section | Evaluation |
|---|---|---|---|
| Spring 2026 | MFG 5390 - Special Topics | 28474 | Evaluation |
| Spring 2026 | MECH 4395 - Special Topics in Mech. Engr. | 28583 | Evaluation |
Dr. Chin-Yih Lin is an Associate Professor of Research in the Department of Industrial, Manufacturing and Systems Engineering (IMSE) at The University of Texas at El Paso (UTEP), with an appointment supported by the UT System Regents’ Research Excellence Program (RREP). His research integrates digital twins, Bayesian optimization, reinforcement learning, generative AI, and time-series foundation models to advance virtual metrology, process optimization, predictive maintenance, and intelligent factory operations in semiconductor manufacturing. Before joining UTEP, he served as a Research Specialist at National Cheng Kung University, leading AI-enabled semiconductor workflow projects, and as a Staff Specialist at Infineon Technologies AG, advancing virtual metrology and machine learning applications in manufacturing. His scholarly contributions include IEEE journal and conference publications, book chapters published by Wiley–IEEE Press, and U.S. patents and patent applications. His honors include the 2025 Taiwan National Invention Award (Silver Medal) and a fifth-place worldwide Scopus-based ranking in the research topic “Semiconductor Device Manufacturing, Neural Networks, and Process Control” (T.31043). He is an IEEE Senior Member and a member of the IEEE Robotics and Automation Society and the IEEE Computational Intelligence Society. His professional service includes reviewing for IEEE Transactions on Automation Science and Engineering, contributing to the IEEE P4128 working group on AI-enabled manufacturing deviation management, and serving as a 2026 Science Communication Ambassador for the IEEE Robotics and Automation Society. Further information about his research, publications, teaching, and collaborations is available on his personal website [https://sites.google.com/view/chin-yih-lin-semicon-ai-lab/home].
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