Design of a run‐to‐run controller based on discrete sliding‐mode observer

Wang, Haiyan; Pan, Tianhong; Ding, Shihong; Chen, Shan

doi:10.1002/asjc.2275

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…The system was shown to outperform EWMA and double EWMA controllers in the rejection of IMA disturbances with a shift or drift (as occurs in tool aging). It also performed better under plant-model mismatch and had better tolerance for metrology delay [102].…”

Section: Semiconductor Manufacturingmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (`soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (`sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.

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Section: Semiconductor Manufacturingmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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“…Over the past decades, run-to-run (RtR) control has become a crucial technology for improving the quality of semiconductor manufacturing processes (Liu et al, 2018). Although research on RtR control has been extensively investigated, the issue of anti-disturbance remains a major focus (Tan et al, 2020; Wang et al, 2021). As the workhorse of RtR control, exponential weighted moving average (EWMA) schemes have shown promising results in semiconductor manufacturing processes due to its simplicity and intuitiveness (Wang et al, 2010; Zhong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning-assisted extended state observer for run-to-run control in the semiconductor manufacturing process

Ma,

Pan

2024

Transactions of the Institute of Measurement and Control

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In the semiconductor manufacturing process, extended state observer (ESO)-based run-to-run (RtR) control is an intriguing solution. Although an ESO-RtR control strategy can effectively compensate for the lumped disturbance, appropriate gains are required. In this article, a cutting-edge deep reinforcement learning (DRL) technique is integrated into ESO-RtR, and a composite control framework of DRL-ESO-RtR is developed. In particular, the well-trained DRL agent serves as an assisted controller, which produces appropriate gains of ESO. The optimized ESO then presents a preferable control recipe for the manufacturing process. Under the RtR framework, the gain adjustment problem of ESO is formulated as a Markov decision process. An efficient state space and reward function are wisely designed using the system’s observable information. Correspondingly, the gain of the ESO is adaptively adjusted to cope with changing environmental disturbances. Finally, a twin-delayed deep deterministic policy gradient algorithm is employed to implement the suggested scheme. The feasibility and superiority of the developed method are validated in a deep reactive ion etching process. Comparative results demonstrate that the presented scheme outperforms the ordinary ESO-RtR controller in terms of disturbance rejection.

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Discrete Active Disturbance Rejection Control for Semiconductor Manufacturing Processes With Dynamic Models

Wang

Pan

Chen

2023

IEEE Trans. Semicond. Manufact.

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Design of a run‐to‐run controller based on discrete sliding‐mode observer

Cited by 4 publications

References 40 publications

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

Deep reinforcement learning-assisted extended state observer for run-to-run control in the semiconductor manufacturing process

Discrete Active Disturbance Rejection Control for Semiconductor Manufacturing Processes With Dynamic Models

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