Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

Fazhi Song; Ning Cui; Shuaiqi Chen; Kai Zhang; Yang Liu; Xinkai Chen; Jiubin Tan

doi:10.1109/JAS.2024.124968

Volume 12 Issue 1

Jan. 2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(1): 198-214

F. Song, N. Cui, S. Chen, K. Zhang, Y. Liu, X. Chen, and J. Tan, “Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 198–214, Jan. 2025. doi: 10.1109/JAS.2024.124968

Citation:

F. Song, N. Cui, S. Chen, K. Zhang, Y. Liu, X. Chen, and J. Tan, “Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 198–214, Jan. 2025. doi: 10.1109/JAS.2024.124968

Citation:

F. Song, N. Cui, S. Chen, K. Zhang, Y. Liu, X. Chen, and J. Tan, “Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 198–214, Jan. 2025. doi: 10.1109/JAS.2024.124968

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

doi: 10.1109/JAS.2024.124968

Funds: This work was supported by National Natural Science Foundation of China (52375530, 52075132), Natural Science Foundation of Heilongjiang Province (YQ2022E025), State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment (Guangdong University of Technology) (JMDZ202312), Fundamental Research Funds for the Central Universities (HIT.OCEF.2024034), China Postdoctoral Science Foundation (2019M651278, 2020T130155), Heilongjiang Province Postdoctoral Science Foundation (LBH-Z19066), and Space Drive and Manipulation Mechanism Laboratory of BICE and National Key Laboratory of Space Intelligent Control, No BICE-SDMM-2024-01

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Author Bio:
Fazhi Song (Member, IEEE) received the Ph.D. degree in control science and engineering from Harbin Institute of Technology, in 2019. From March 2017 to March 2018, he was a joint-training Ph.D. student with National University of Singapore, Singapore. He is currently an Associate Professor with Harbin Institute of Technology. His research interests include machine learning, system identification and motion control with applications in high-precision mechanical servo systems

Ning Cui received the B.E. degree in automation from the School of Mechanical and Electrical Engineering, Northeast Forestry University, in 2006, and the M.S. degree in microelectronics and solid-state electronics from the School of Aeronautics, Harbin Institute of Technology, in 2009. He is currently working toward the Ph.D. degree in instrumentation science and technology from the School of Instrument Science and Engineering, Harbin Institute of Technology. His research focuses on motion control with applications in high-precision mechanical servo systems

Shuaiqi Chen received the B.E. degree in electrical engineering and automation from the School of Electronic and Information Engineering, Changchun University of Science and Technology, in 2016, and the M.S. degree in control engineering from the School of Mechatronics and Electronic Engineering, Changchun University of Technology, in 2020. He is currently working toward the Ph.D. degree in control science and engineering from the School of Aeronautics, Harbin Institute of Technology. His research focuses on model predictive control and its application to high-precision motion systems

Kai Zhang received the B.S. degree from the Department of Navigational Technology, Dalian Maritime University, in 2015, the M.S. degree from the Department of Traffic and Transportation Engineering, Dalian Maritime University, in 2017, and the Ph.D. degree from the Department of Control Science and Engineering, Harbin Institute of Technology, in 2021. He is an Associate Professor with the School of Instrument Science and Engineering, Harbin Institute of Technology, and a Senior Visiting Scholar in the Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China. His research interests include event-triggered control, adaptive control, multiagent systems, and networked systems

Yang Liu (Member, IEEE) received the B.S. degree in automation, the M.S. degree and the Ph.D. degree in control science and engineering from Harbin Institute of Technology, in 2004, 2006, 2011, respectively. He was a Research Fellow in Queen’s University Belfast from 2014 to 2015. Now, he is a Professor in Harbin Institute of Technology. His current research interests include system identification and precision motion control

Xinkai Chen (Fellow, IEEE) received his Ph.D. degree in engineering from Nagoya University, Japan, in 1999. He is currently a professor in the Department of Electronic Information Systems, Shibaura Institute of Technology, Japan. His research interests include adaptive control, smart materials, hysteresis, sliding mode control, machine vision, and observer. Dr. Chen has served as Senior Editor of IEEE/ASME Transactions on Mechatronics, and Associate Editor of several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Industrial Electronics, etc. He has also severed for international conferences as organizing committee members including General Chairs, Program Chairs, General Co-Chairs, Program Co-Chairs, etc. He is a member of The Engineering Academy of Japan

Jiubin Tan received the B.S., M.S., and Ph.D. degrees in instrument science and technology from the Harbin Institute of Technology, in 1982, 1987, and 1991, respectively. Since 1995, he has been a Professor with the Department of Instrument Science and Technology, Harbin Institute of Technology. In 2017, he became an Academician of the Chinese Academy of Engineering. He has 132 granted patents including 21 international patents. He has drafted 26 standards, authored or coauthored more than 310 technical papers, and authored three monographs. His research interests include precision measurement and instrument technology, ultraprecision optomechatronics equipment technology, and precision laser measurement technology
Corresponding author: Kai Zhang, e-mail: kaizhang0116@hit.edu.cn; Yang Liu, e-mail: hitlg@hit.edu.cn
Received Date: 2024-07-18
Revised Date: 2024-09-08
Accepted Date: 2024-10-02

Available Online: 2024-10-16

Abstract

Abstract

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control (ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer (ESO) based adaptive ILC approach is proposed in the frequency domain. Despite being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.
- Extended state observer,
- learning control,
- model uncertainties,
- motion control,
- stochastic noise

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References

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The influence of model uncertainties on the inversion-based ILC is examined, highlighting the substantial dependence of convergence on both phase and magnitude uncertainties. Additionally, the ILC performance in the presence of stochastic noise is investigated, revealing the conflicting requirement for the learning coefficient in terms of accelerating convergence and enhancing convergence accuracy. While comparable conclusions can be found in existing literature, this work rigorously substantiates these findings
The proposed method employs a simplified system model, which is readily obtainable in practice, as opposed to the high-accuracy model required in model-based ILC or the absence of a model in data-driven ILC. The resulting model uncertainties are compensated for by developing an ESO, resulting in an equivalent rigid-body plant with a unit mass. Consequently, despite being model-based, the proposed method requires minimal modeling effort while still achieving high performance
To mitigate the impact of stochastic noise on convergence accuracy without sacrificing convergence speed, an adaptation mechanism is employed for the learning coefficient. Importantly, the adaptation mechanism is developed without any involvement of model knowledge, and hence adheres to a data-driven approach
Experimental comparisons are performed on a wafer stage, illustrating the effectiveness of the proposed method and highlighting its enhanced performance compared with existing model-based and data-driven approaches

Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

doi: 10.1109/JAS.2024.124968

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通讯作者: 陈斌, bchen63@163.com

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