Model approximation and stabilization of reactive sputter processes

Woelfel, Christian; Bockhorn, Dirk; Awakowicz, Peter; Lunze, Jan

doi:10.1016/j.jprocont.2018.06.009

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…141 Following the identification of an ROM taking into account the nonlinear process dynamics due to surface poisoning, 142 they have devised a controller for process operation at the transition between poisoned and clean surface modes using the previous reduced model, combined with measurements from multipole resonance probe experiments. 143,144 An improved reduced model and novel control scheme (also based on the Berg model) has been introduced later, taking into account the plasma state and its dynamical behavior. 145 The control problem of delivering a specific thermals dose during application of a kHz atmospheric pressure plasma jet (APPJ) operated in helium has been detailed by Mesbah et al in a sequence of publications discussed in the following.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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Machine learning has had an enormous impact in many scientific disciplines. It has also attracted significant interest in the field of low-temperature plasma (LTP) modeling and simulation in past years. Its application should be carefully assessed in general, but many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and datadriven modeling. In this survey, we approach two main objectives: (a) we review the state-of-the-art, focusing on approaches to LTP modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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Section: Model Predictive Controlmentioning

confidence: 99%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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“…Woelfel had a different approach by studying the essential input-output interactions in different operating conditions in order to develop a simplified model that was conceived with the aim of being practical for control purposes [62,63,64,67]. Woelfel et al presented a control design method that is based on artificial neural networks and ordinary differential equations [66,65].…”

Section: Modeling For Control Structure Synthesismentioning

confidence: 99%

“…Some researchers focused on model reduction, in order to facilitate the implementation of less complex control algorithms. The work done by Woelfel et al concentrates on developing models that suffice the control goal [67]. It was mentioned that most results in the field were provided from either a vacuum science or a thin solid film approach, therefore most models developed were not usable for control purposes.…”

Section: Modeling For Control Structure Synthesismentioning

confidence: 99%

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Modeling reactive magnetron sputtering: a survey of different modeling approaches

Madarász

Kelemen

Kadar

2020

Acta Universitatis Sapientiae, Informatica

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The paper focuses on providing an insight into the current state of computational modeling regarding reactive magnetron sputtering systems. A detailed compilation of developed models is gathered and grouped into categories based on the phenomena being modeled. The survey covers models developed for the analysis of magnetron discharges, particle-surface interactions at the target and the substrate, as well as macroscopic models. Corresponding software packages available online are also presented. After gaining the necessary insight into the current state of research, a list of the most challenging tasks is given, comparing diffierent approaches, that have been used to combat the encountered difficulties. The challenges associated with modeling tasks range from analytical complexity, mathematical know-how used for model approximation and reduction, as well as optimization between computational load and result accuracy. As a conclusion, the future challenges are compiled into a list and a probable direction in modeling is given, that is likely to be further pursued.

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“…Classical studies [1][2][3] on the control of reactive sputtering mainly consider secondary process variables like pressures, optical emissions or voltages to allow high-rate sputtering [4]. The authors of the present paper have provided a controloriented process model and controller design method [5] with respect to the high-rate sputtering problem. The plasma and its properties are not directly considered by these approaches.…”

Section: Literaturementioning

confidence: 99%

Plasma State Control of Reactive Sputter Processes

Woelfel¹,

Oberberg²,

Awakowicz³

et al. 2019

International Conference of Control, Dynamic Systems, and Robotics

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Nonlinear reactive sputter processes are indispensable for the deposition of functional thin film layers. As the coating process is driven by a low pressure plasma the plasma state affects the thin film properties. The process behavior has specific properties. It is unstable, one specific input value can lead to different values of the plasma state and the same plasma state can be achieved by different input values. This unstable and ambiguous behavior requires a control system, which consists of a stabilizing controller, an estimation unit and a feedforward controller. In this paper, a design method for the plasma state control based of an input/output-model is proposed. Experiments show the effectiveness of the proposed control method.

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Model approximation and stabilization of reactive sputter processes

Cited by 15 publications

References 10 publications

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Modeling reactive magnetron sputtering: a survey of different modeling approaches

Plasma State Control of Reactive Sputter Processes

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