In order to ensure safe and fast transportation of wafers in 300 mm semiconductor factories, overhead transport systems (OHT) are primarily used. These systems consist of a rail network and vehicles. To avoid congestion and delays in production, high availability of individual rail sections is essential. In order to ensure this extensive preventive maintenance is required. In this paper, we focus on automatic checks for faults of the rail network by capturing the rail with optical sensors. Our objective is the identification of faults in real time. We considered the identification using artificial neural networks (ANN). Due to the lack of fixed rules designing an ANN we tested different topologies for our application and covered adaptation of ANN to the real conditions in the fab. As a result, our ANN provides accurate real time fault detection which allows a needs-based, resource-saving and efficient maintenance procedure for a reliable OHT and hence 24/7 semiconductor manufacturing.

中文翻译：

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人工神经网络故障检测在半导体厂架空运输系统中的应用

为了确保300mm半导体工厂晶圆的安全快速运输，主要使用高架运输系统（OHT）。这些系统由铁路网络和车辆组成。为避免生产中的拥堵和延误，各个轨道段的高可用性至关重要。为了确保这种广泛的预防性维护是必需的。在本文中，我们专注于通过使用光学传感器捕获铁路来自动检查铁路网络的故障。我们的目标是实时识别故障。我们考虑了使用人工神经网络（ANN）的识别。由于缺乏设计 ANN 的固定规则，我们为我们的应用测试了不同的拓扑结构，并涵盖了 ANN 对晶圆厂实际条件的适应性。因此，