An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing
Rapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11071687/ |
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| Summary: | Rapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term traffic congestion in the OHT railway accurately. However, the congestion prediction is a significant challenge due to the complexity of investigating all traffic conditions and dynamic traffic changes. Several studies have utilized machine learning approaches to address these concerns, but limitations arise in predicting the short-term congestion due to the performance bias stemming from large input features. Recurrent neural networks are effective in predicting traffic flow in transportation. However, they may not be suitable for OHT railway congestion prediction due to unpredictable loading/unloading events and varying traffic volumes. Therefore, this study proposes an integrated neural network-based method where multiple neural networks are trained considering current conditions of the railway network and expected changes in traffic conditions. To verify the effectiveness of the proposed method, a simulated dataset was used to reflecting real-world semiconductor fabrication. The experiment results demonstrate that the proposed method outperforms existing methods, including machine learning- and deep learning-based methods. |
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| ISSN: | 2169-3536 |