Recently, the transportation capability of the automated material handling system (AMHS) has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it can limit the FAB production capacity. In this study, we propose a prediction method for a machine allocation problem of production scheduling in consideration with the AMHS’s constraints. The proposed method dynamically targets a machine for the next process by identifying diverse production conditions. We use a deep neural network-based dynamic scheduling method considering the overall production environment, which includes the remaining processing time, facility states, transportation time and traffic congestion, work-in-process distribution, and intermediate buffer states. To demonstrate the superiority and efficiency of the proposed method, we conducted experimental studies to compare the proposed model with the existing priority rule-based and analytic models under the static and dynamic environments. From the results, we verify that the proposed dynamic scheduling system can enhance the performance of existing AMHS and reduce machine starvation and production losses.

中文翻译：

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基于深度学习的半导体制造动态调度，具有高度不确定的自动化材料处理系统能力

最近，自动化材料处理系统 (AMHS) 的运输能力已成为半导体制造设施 (FAB) 的主要障碍，因为它会限制 FAB 的生产能力。在这项研究中，我们提出了一种考虑到 AMHS 约束的生产调度机器分配问题的预测方法。所提出的方法通过识别不同的生产条件动态地将机器定位为下一个过程。我们使用基于深度神经网络的动态调度方法考虑整体生产环境，包括剩余加工时间、设施状态、运输时间和交通拥堵、在制品分布和中间缓冲状态。为了证明所提出方法的优越性和效率，我们进行了实验研究，以在静态和动态环境下将所提出的模型与现有的基于优先级规则的分析模型进行比较。从结果中，我们验证了所提出的动态调度系统可以提高现有 AMHS 的性能并减少机器饥饿和生产损失。