Automatic Fault Detection in Rails of Overhead Transport Systems for Semiconductor Fabs

Abstract

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, normally extensive preventive maintenance is required. In this article, we focus on automatic checks for faults of the rail network by capturing an area of the rail with optical sensors. Our objective is the identification of faults in real time. We considered the identification with a basic determining approach as well as the application of artificial neural networks (ANN). Due to the lack of fixed rules designing an ANN we tested different topologies for our application. As a result, our ANN provides accurate real time fault detection which allows a needs-based, resource-saving and efficient maintenance procedure for 24/7 semiconductor manufacturing.