Jaeho Lee;Jinhyeok Park;Sangpyo Hong;Illhoe Hwang;Seol Hwang;Young Jae Jang;Donghwi Shin;Jaeung Lee
{"title":"Autonomous Robot Orchestration Solution for OHT With Active Q Routing and Digital Twin FA: Factory Automation","authors":"Jaeho Lee;Jinhyeok Park;Sangpyo Hong;Illhoe Hwang;Seol Hwang;Young Jae Jang;Donghwi Shin;Jaeung Lee","doi":"10.1109/TSM.2025.3594651","DOIUrl":null,"url":null,"abstract":"The Autonomous Robot Orchestration Solution (AROS) is transforming the management of robot fleets by identifying the state and environment of each robot and enabling them to collaborate toward common goals. This paper introduces AROS and its application in controlling massive fleets of Overhead Hoist Transport (OHT) vehicles in semiconductor fabrication facilities. AROS leverages key technologies, including reinforcement learning algorithms, discrete event simulation, and real-time data collection through Digital Twin (DT). The DT replicates the real system in a virtual environment with real-time communication to optimize decision-making for OHTs. A key innovation of AROS is the introduction of active Q routing, a dynamic routing method that adapts to changing traffic conditions by predicting and adjusting travel times through discrete event simulation. Active Q routing enhances operational efficiency by mitigating congestion and reducing delays, even in highly dynamic environments. We demonstrate the effectiveness of AROS and active Q routing on OHT system performance, showcasing reductions in average delivery times and increases in delivery capacity. These findings are validated through real-world use cases in a large-scale semiconductor fab.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 3","pages":"404-412"},"PeriodicalIF":2.3000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Semiconductor Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11105537/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The Autonomous Robot Orchestration Solution (AROS) is transforming the management of robot fleets by identifying the state and environment of each robot and enabling them to collaborate toward common goals. This paper introduces AROS and its application in controlling massive fleets of Overhead Hoist Transport (OHT) vehicles in semiconductor fabrication facilities. AROS leverages key technologies, including reinforcement learning algorithms, discrete event simulation, and real-time data collection through Digital Twin (DT). The DT replicates the real system in a virtual environment with real-time communication to optimize decision-making for OHTs. A key innovation of AROS is the introduction of active Q routing, a dynamic routing method that adapts to changing traffic conditions by predicting and adjusting travel times through discrete event simulation. Active Q routing enhances operational efficiency by mitigating congestion and reducing delays, even in highly dynamic environments. We demonstrate the effectiveness of AROS and active Q routing on OHT system performance, showcasing reductions in average delivery times and increases in delivery capacity. These findings are validated through real-world use cases in a large-scale semiconductor fab.
期刊介绍:
The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.