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Data-driven aggregate modeling of a semiconductor wafer fab to predict WIP levels and cycle time distributions

Patrick C. Deenen (), Jeroen Middelhuis (), Alp Akcay () and Ivo J. B. F. Adan ()
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Patrick C. Deenen: Eindhoven University of Technology
Jeroen Middelhuis: Eindhoven University of Technology
Alp Akcay: Eindhoven University of Technology
Ivo J. B. F. Adan: Eindhoven University of Technology

Flexible Services and Manufacturing Journal, 2024, vol. 36, issue 2, No 7, 567-596

Abstract: Abstract In complex manufacturing systems, such as a semiconductor wafer fabrication facility (wafer fab), it is important to accurately predict cycle times and work-in-progress (WIP) levels. These key performance indicators are commonly predicted using detailed simulation models; however, the detailed simulation models are computationally expensive and have high development and maintenance costs. In this paper, we propose an aggregate modeling approach, where each work area, i.e., a group of functionally similar workstations, in the wafer fab is aggregated into a single-server queueing system. The parameters of the queueing system can be derived directly from arrival and departure data of that work area. To obtain fab-level predictions, our proposed methodology builds a network of aggregate models, where the network represents the entire fab consisting of different work areas. The viability of this method in practice is demonstrated by applying it to a real-world wafer fab. Experiments show that the proposed model can make accurate predictions, but also provide insights into the limitations of aggregate modeling.

Keywords: Semiconductor manufacturing; Discrete-event simulation; Aggregate modeling; Work-in-progress; Cycle time; Effective processing time (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10696-023-09501-1

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