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

  • Haejoong Kim
  • Dae-Eun LimEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)


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).


Storage allocation WIP allocation Machine targeting Dispatching Scheduling Deep learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Samsung ElectronicsHwaseongRepublic of Korea
  2. 2.Kangwon National UniversityChuncheonRepublic of Korea

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