Deep-Learning-Based Storage-Allocation Approach to Improve the AMHS Throughput Capacity in a Semiconductor Fabrication Facility

Abstract

Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) in South Korea have become a new and major bottleneck. This is mainly because the number of long-distance transportation requests has increased as the FAB area has widened. This paper presents a deep-learning-based adaptive method for the storage-allocation problem to improve the AMHS throughput capacity. The AMHS in this research consists of overhead hoist transfer transports (OHTs), a unified rail for the OHTs, etc. The main problem involves scheduling (or designating) an intermediate buffer, e.g., a stocker or a side track buffer, for a single lot. Thus far, a static optimization approach has been widely applied to the problem. This research shows that a learning-based adaptive storage-allocation strategy can increase the AMHS capacity in terms of throughput. The deep-learning model considers various production conditions, including processing time, transportation time, and the distribution of works in process (WIP).