Author(s)

Yuxing Ma ^1,2, Wenhan Fu ^1,3

Affiliation(s)

¹ Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China
² Applied Materials(China)co., Ltd., Shanghai, China
³ School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Wenhan Fu

ABSTRACT

With the improvement of the precision of semiconductor equipment, the traditional equipment operation and maintenance (O&M) mode is difficult to meet the real-time and predictive monitoring requirements of wafer manufacturing equipment, while existing digital twin systems for equipment lack demand-oriented design. Therefore, this study takes scanning electron microscope (SEM) equipment as the research object and proposes a demand analysis framework of digital twin system based on "Affinity Diagram (KJ) Method + Analytic Hierarchy Process (AHP)". Firstly, summarized specific requirements for the digital twin system of SEM equipment through the Affinity Diagram Method. Then, the AHP was used to quantify the priority and determine the importance and priority of the requirements. The system was built and applied according to the results of requirement analysis. Through practical application and data verification, it is confirmed that the digital twin system established based on this method has achieved remarkable results in equipment O&M management and cost reduction. This study provides a scientific requirement analysis method for the design of digital twin systems for semiconductor equipment, and offers practical reference for the intelligent O&M upgrading of the industry.

KEYWORDS

Digital Twin, Semiconductor Equipment, Requirement Analysis, Affinity Diagram Method, Analytic Hierarchy Process, Operation and Maintenance Management

CITE THIS PAPER

Yuxing Ma, Wenhan Fu, Demand Analysis and Effectiveness Evaluation of Digital Twin System for Semiconductor Equipment Operation and Maintenance. Industrial Engineering and Innovation Management (2025) Vol. 8: 112-119. DOI: http://dx.doi.org/10.23977/ieim.2025.080216.

REFERENCES

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[2] Ran, Y., Zhou, X., Lin, P., Wen, Y., & Deng, R. (2019). A Survey of Predictive Maintenance: Systems, Purposes and Approaches. ArXiv, abs/1912.07383.
[3] Han, N., Seo, J., Ha, J., Oh, J., Lee, J., Jeong, B., Park, J., Kim, G., & Joo, Y. (2025). Development of Autonomous Failure Maintenance System for Semiconductor Manufacturing. Proceedings of the 34th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2025: 5723-5730.
[4] Khan, T., Khan, U., Khan, A., Mollan, C., Morkvenaite-Vilkonciene, I., & Pandey, V. (2025). Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems. Machines, 13(6), 481.
[5] Hassan, M., Svadling, M., & Björsell, N. (2023). Experience from implementing digital twins for maintenance in industrial processes. Journal of Intelligent Manufacturing, 35, 875–884.
[6] Moyne, J., & Iskandar, J. (2017). Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing. Processes, 5(3), 39.

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