Alleviation of OHT vehicle congestion in semiconductor FAB with dynamic link weight control: A reinforcement learning approach

초록

With the continuous growth in semiconductor demand, semiconductor manufacturers have expanded the capacity of fabrication (FAB) facilities. Consequently, an Automated Material Handling System (AMHS), which controls Overhead Hoist Transport (OHT) vehicles, has become larger and more complex. In large-scale FAB environments characterised by dense track networks, frequent congestion often leads to bottlenecks, decreasing overall productivity. However, traditional static routing approaches for each OHT fail to effectively manage real-time congestion, resulting in traffic being concentrated on specific areas and causing operational inefficiencies. To address this issue, we propose a reinforcement learning-based Dynamic Link Weight Control (DLWC) method. By partitioning the AMHS layout into multiple areas, the DLWC controls the link weights that influence OHT route selection, leading OHTs to choose less congested paths. Simulation experiments demonstrate that the proposed DLWC method outperforms conventional rule-based approaches in terms of both throughput and lead time, validating the practical effectiveness of congestion control in large-scale AMHS environments.

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