Plasma State Control of Reactive Sputter Processes

Woelfel, Christian; Oberberg, Moritz; Awakowicz, Peter; Lunze, Jan

doi:10.11159/cdsr19.107

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…developed MPC schemes for radio-frequency reactive magnetron sputtering fundamentally based on the Berg model 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 …”

Section: Reviewmentioning

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: Reviewmentioning

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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“…The plasma model proposed in section 4 is necessary for the development of a control concept, where as the implemented controller operates without a process model. This is a major upgrade compared to the preliminary control-theoretic approach in [7], where an identified model was needed for the whole operation time. Experiments are shown in sections 1, 4 and 6 as a proof of concept for the motivation of a plasmabased control instrument (figure 2), for the identification of a control-oriented model (figure 8) and the applicability of the instrument in an argon-oxygen mixture with an aluminum target (figure 11).…”

Section: Introductionmentioning

confidence: 99%

The Multipole Resonance Probe-based controller: a technology to investigate plasma-based deposition

Woelfel¹,

Oberberg²,

Berger³

et al. 2019

J. Inst.

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With the objective to improve plasma-based deposition processes new sensors for the investigation of the plasma-surface interaction have been developed over the last years. However, the direct access and the reproducible operation of specific plasma conditions are yet unconsidered but required to receive related measurement results. Hence, desired unstable operation points have to be stabilized by feedback controllers, which are also able to automatically compensate process drifts. Existing control systems for plasma-based deposition rely on secondary process variables, but these variables cannot be correlated with the plasma variables as a general rule and, therefore, a direct study of the plasma is limited. To lift this restriction a novel electron-plasma-frequency-based control instrument is proposed, which uses the Multipole Resonance Probe, a feedback controller and a signal-based estimator. The control system directly measures the electron plasma frequency, online estimates the process mode and calculates a related control input to achieve and hold a prescribed working point. Experiments are shown as a proof of concept for the motivation of a plasma-based control system, the identification of a control-oriented model and the applicability of the instrument in an argon-oxygen mixture.

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Plasma State Control of Reactive Sputter Processes

Cited by 2 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

The Multipole Resonance Probe-based controller: a technology to investigate plasma-based deposition

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