Machine Learning-Based Periodic Setup Changes for Semiconductor Manufacturing Machines
- Authors
- Lee, J.-H.[Lee, J.-H.]; Kim, H.-J.[Kim, H.-J.]; Kim, Y.[Kim, Y.]; Kim, Y.B.[Kim, Y.B.]; Kim, B.-H.[Kim, B.-H.]; Chung, G.-H.[Chung, G.-H.]
- Issue Date
- 2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- Proceedings - Winter Simulation Conference, v.2021-December
- Indexed
- SCOPUS
- Journal Title
- Proceedings - Winter Simulation Conference
- Volume
- 2021-December
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/97485
- DOI
- 10.1109/WSC52266.2021.9715383
- ISSN
- 0891-7736
- Abstract
- Semiconductor manufacturing machines, especially for photo-lithography processes, require large setup times when changing job types. Hence, setup operations do not often occur unless there is no job to be processed. In practice, a simulation-based method that predicts the incoming WIP is often used to determine whether changing machine setup states or not. The simulation-based method can provide useful information on the future production environment with a high accuracy but takes a long time, which can delay the setup change decisions. Therefore, this work proposes a machine learning-based approach that determines setup states of the machines. The proposed method shows better performance than several heuristic rules in terms of movement. © 2021 IEEE.
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- Appears in
Collections - Engineering > Department of Systems Management Engineering > 1. Journal Articles
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