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Open Access

End-to-End Scheduling of Single-Arm Cluster Tools with Multiple Wafer Types

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510006, China
School of Electrical Engineering, Hanyang University, Ansan 15588, Republic of Korea
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Abstract

The rapid advancement in circuit manufacturing technology has enabled the reduction in circuit width. Consequently, a single wafer can now accommodate more chips, allowing for smaller lot sizes to meet diverse customer needs. Manufacturers aim to use a single production line to simultaneously produce multiple types of wafers for various customizations. However, this presents two challenges: (1) Different types of wafers require different processing routes, necessitating a highly adaptable scheduling algorithm for producing various wafer types in the same production line; (2) producing multiple wafer types involves several state transitions, whereas existing research primarily focuses on single-state transitions. To address these challenges, we propose an end-to-end scheduling method for single-arm cluster tools handling multiple wafer types (ESM-SMWT). First, ESM-SMWT employs a genetic algorithm to optimize the sequence in which wafers enter the cluster tool, improving the utilization of shared and parallel processing modules. Next, integer programming is used to achieve end-to-end scheduling, from the processing of the first wafer at the start of a lot to the completion of the last wafer. Additionally, the new robotic arm strategy we propose significantly reduces the number of robotic arm activities. Through theoretical proofs and extensive experiments, the practical effectiveness of ESM-SMWT is validated.

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Complex System Modeling and Simulation
Pages 164-178

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Cite this article:
Li B, Zhong J, Liu W-L, et al. End-to-End Scheduling of Single-Arm Cluster Tools with Multiple Wafer Types. Complex System Modeling and Simulation, 2026, 6(2): 164-178. https://doi.org/10.23919/CSMS.2025.0012

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Received: 06 November 2024
Revised: 21 April 2025
Accepted: 27 April 2025
Published: 02 June 2025
© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).