Modelling of a miniature microwave driven nitrogen plasma jet and comparison to measurements

Klute, Michael; Ke­man­eci, Efe; Porteanu, Horia‐Eugen; Stefanović, Ilija; Heinrich, W.; Awakowicz, Peter; Brinkmann, Ralf Peter

doi:10.1088/1361-6595/ac04bc

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…This reaction set is described as the 'chemistry' for that plasma. However, assembling plasma chemistries is far from straightforward since even for relatively simple systems such as a microwave molecular nitrogen plasma some 15 species are necessary to characterise the plasma including: the seven lowest vibrationally excited states of the nitrogen molecule in the ground state N 2 (X 1 Σ + g )ν = 0 to 6, the metastable molecule N 2 (A 3 Σ + u ), the ground state atom N ( 4 S), two metastable atoms N ( 2 D) and N ( 2 P ) and five ionic species N , N + , N + 2 , N + 3 N + 4 [708]. With these 15 species more than 100 'reactions' may be necessary to define the inherent plasma chemistry, most of which have never been measured.…”

Section: Materials Databasementioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Section: Materials Databasementioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Nowadays, there are rapidly growing interest in developing 2D miniaturized plasma for surface modification and chemical vapor deposition. [20][21][22] Besides, there are also many studies on cold atmospheric plasma jets in the kHz and MHz frequency bands. In 2015, Robert et al developed a plasma gun for the study of helium and neon plasma propagation inside a dielectric tube.…”

Section: Introductionmentioning

confidence: 99%

A novel two‐dimensional atmospheric pressure plasma jet device

Zhong

et al. 2021

Plasma Processes & Polymers

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A novel two-dimensional atmospheric pressure plasma jet device is proposed for large-area processing. A finite element algorithm is adopted to obtain the optimized dimensions of the designed device based on microwave power reflection coefficient |S 11 | and electric field intensity. The plasma parameters of electron density n e and electron temperature T e within 0.1 ms after microwave power input were analyzed. When plasma is fully excited, n e is close to 3.5 × 10 18 m −3 , and T e is calculated to be 2 eV. Besides, an experimental system is established, and microwave power of 70 W can process 30 L of argon per minute, which shows a high energy efficiency. Two plasma jets with a width of 10 mm and a length of 5 mm

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A global plasma and surface model of hydrogen/methane inductively coupled discharge to analyze hydrocarbon plasma–surface interactions in extreme-ultraviolet lithography machines

Kemaneci,

von Keudell,

Heijmans

et al. 2024

Journal of Applied Physics

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Hydrocarbon contamination is associated with light transmission losses in modern lithography machines, which contain extreme-ultraviolet-induced plasma. A volume-averaged global and deposition/etch surface model of a reference hydrogen/methane inductive discharge is developed to investigate the plasma–surface interactions. The simulation results are validated against a wide variety of experiments and verified with respect to multiple sets of computational data. The deposition rate is calculated for a variation in methane impurity (10–10 000 ppm), power, pressure, and net mass flow. The simulations conclude that the hydrocarbon plasma deposition can be minimized by reducing methane impurity and excluding solid organic structures.

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Modelling of a miniature microwave driven nitrogen plasma jet and comparison to measurements

Cited by 4 publications

References 20 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

A novel two‐dimensional atmospheric pressure plasma jet device

A global plasma and surface model of hydrogen/methane inductively coupled discharge to analyze hydrocarbon plasma–surface interactions in extreme-ultraviolet lithography machines

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