Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

Qinghua Zhu; Bin Li; Yan Hou; Hongpeng Li; Naiqi Wu

doi:10.1109/JAS.2023.123189

Volume 10 Issue 8

Aug. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(8): 1730-1742

Q. H. Zhu, B. Li, Y. Hou, H. P. Li, and N. Q. Wu, “Scheduling dual-arm multi-cluster tools with regulation of post-processing time,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1730–1742, Aug. 2023. doi: 10.1109/JAS.2023.123189

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Q. H. Zhu, B. Li, Y. Hou, H. P. Li, and N. Q. Wu, “Scheduling dual-arm multi-cluster tools with regulation of post-processing time,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1730–1742, Aug. 2023. doi: 10.1109/JAS.2023.123189

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Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

doi: 10.1109/JAS.2023.123189

Funds: This work was supported in part by the National Natural Science Foundation of China (61673123), the Natural Science Foundation of Guangdong Province, China (2020A151501482), the Science and Technology development fund (FDCT), Macau SAR (0083/2021/A2, 0015/2020/AMJ), and Research Fund of Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology (2020B1212030010)

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Author Bio:
Qinghua Zhu (Senior Member, IEEE) received the Ph.D. degree in industrial engineering from Guangdong University of Technology in 2013. From 2014 to 2015 and from 2017 to 2018, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, USA. He joined Guangdong University of Technology in 2003, and is currently an Associate Professor with the School of Computer Science and Technology. His research interests include intelligent manufacturing scheduling, cloud computing, edge computing, discrete event systems, and Petri nets

Bin Li received the B.S. degree in software engineering from Jiangxi University of Finance and Economics in 2020. He is currently a master student in computer technology at the School of Computer Science and Technology, Guangdong University of Technology. His research interests include intelligent manufacturing scheduling and discrete event systems

Yan Hou received the B.S. and M.S. degrees in computer science from Yangtze University, in 1999 and 2002, respectively, and the Ph.D. degree in industrial engineering from the Guangdong University of Technology in 2016. Since 2002, she has been with the School of Computer Science and Technology, Guangdong University of Technology where she is currently an Associate Professor. Her current research interests include production scheduling and optimization, information systems, and software engineering

Hongpeng Li received the B.S. degree in computer science and technology from University of Electronic Science and Technology of China, Zhongshan Institute in 2000. He is currently a master student in computer science and technology at the School of Computer Science and Technology, Guangdong University of Technology. His research interests include intelligent manufacturing scheduling and discrete event systems

Naiqi Wu (Fellow, IEEE) received the B.S. degree in electrical engineering from Anhui University of Science and Technology in 1982, the M.S. and Ph.D. degrees in systems engineering both from Xi’an Jiaotong University, in 1985 and 1988, respectively. From 1988 to 1995, he was with Shenyang Institute of Automation, Chinese Academy of Sciences, and from 1995 to 1998, with Shantou University. He moved to Guangdong University of Technology in 1998. He joined Macau University of Science and Technology, China, in 2013. He was a Visiting Professor at Arizona State University, USA, in 1999; New Jersey Institute of Technology, USA, in 2004; University of Technology of Troyes, France, from 2007 to 2009; and Evry University, France, from 2010 to 2011. He is currently a Chair Professor at the Department of Engineering Science and the Institute of Systems Engineering, Macau University of Science and Technology, China. His research interests include production planning and scheduling, manufacturing system modeling and control, discrete event systems, Petri net theory and applications, intelligent transportation systems, and energy systems. He is the author or co-author of one book, five book chapters, and over 200 peer-reviewed journal papers. He was an Associate Editor of the IEEE Transactions on Systems, Man, & Cybernetics, Part C, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, & Cybernetics: Systems, and Editor in Chief of Industrial Engineering Journal, and is an Associate Editor of Information Sciences
Corresponding author: Qinghua Zhu, e-mail: zhuqh@gdut.edu.cn
Received Date: 2022-04-25
Revised Date: 2022-06-24
Accepted Date: 2022-07-08

Available Online: 2023-06-05

Abstract

Abstract

As wafer circuit width shrinks down to less than ten nanometers in recent years, stringent quality control in the wafer manufacturing process is increasingly important. Thanks to the coupling of neighboring cluster tools and coordination of multiple robots in a multi-cluster tool, wafer production scheduling becomes rather complicated. After a wafer is processed, due to high-temperature chemical reactions in a chamber, the robot should be controlled to take it out of the processing chamber at the right time. In order to ensure the uniformity of integrated circuits on wafers, it is highly desirable to make the differences in wafer post-processing time among the individual tools in a multi-cluster tool as small as possible. To achieve this goal, for the first time, this work aims to find an optimal schedule for a dual-arm multi-cluster tool to regulate the wafer post-processing time. To do so, we propose polynomial-time algorithms to find an optimal schedule, which can achieve the highest throughput, and minimize the total post-processing time of the processing steps. We propose a linear program model and another algorithm to balance the differences in the post-processing time between any pair of adjacent cluster tools. Two industrial examples are given to illustrate the application and effectiveness of the proposed method.
- Cluster tool,
- optimization,
- scheduling

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References

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Aim to ensure high quality of high-end IC chips on a wafer
Find an optimal schedule of a dual-arm multi-cluster tool to regulate wafer post-processing time
Achieve the highest throughput and minimize the total post-processing time

Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

doi: 10.1109/JAS.2023.123189

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通讯作者: 陈斌, bchen63@163.com

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