Diagnosis of helicon plasma by local OES

Cui, Ruilin; Han, Ruoyu; Yang, Kaiyi; Zhu, Wanying; Wang, Yueqing; Zhang, Zun; Ouyang, Jiting

doi:10.1088/1361-6595/ab56dc

Cited by 35 publications

(47 citation statements)

References 43 publications

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“…The efficiency of ionization was promoted accordingly. This characteristic of mode transition was reported previously by our group [30–32] as well as the others [34–36].…”

Section: Resultssupporting

confidence: 87%

“…Since 811.53 nm emission from the transition 3p 5 the DL on axis. Besides, the intensity of 811.53 nm line can be converted to the electron density by certain relations [31,32]. Hence the corresponding electron density is also displayed here in Figure 3b.…”

Section: Experiments and Resultsmentioning

confidence: 95%

“…Since 811.53 nm emission from the transition 3p 5 ( 2 P 0 3/2 )4p[5/2] 3 → 3p 5 ( 2 P 0 3/2 )4s[3/2] 0 2 is correlated with the density of metastable state argon atoms as well as the electron density [30], result of Figure 3b also approximately indicates the distribution of electron density, roughly the plasma density, across the DL on axis. Besides, the intensity of 811.53 nm line can be converted to the electron density by certain relations [31, 32]. Hence the corresponding electron density is also displayed here in Figure 3b.…”

Section: Resultsmentioning

confidence: 99%

“…With this method, the plasma potential can be obtained and applied into the analysis of the DL. The method for measuring the optical spectrum of Ar helicon plasma is local optical emission spectrum [31,32]. The optical spectrum is collected by a probe comprised of a hollow ceramic cylindrical tube 5 mm in diameter and a thin optical fibre fixed inside.…”

Section: Experimental Set-up and Diagnostic Methodsmentioning

confidence: 99%

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Effect of magnetic field on double layer in argon helicon plasma

et al. 2020

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The electric double layer in argon helicon plasma by using floating electrostatic probe and local optical emission spectroscopy under different external magnetic field was investigated. Results show that electrons generated in the plasma source can be magnetized by the magnetic field and transported by the gradient of diverging magnetic field in the diffusion chamber, giving rise to the hollowed distribution of electron temperature. The electron temperature on axis has a sudden decrease, leading to the sudden decrease of plasma density as well as the plasma potential, forming a double layer structure. The position of double layer moves along with the position of diverging magnetic field, and the potential drop increases along with the strength of magnetic field. The structure of the external magnetic field plays the decisive factor for the double layer in helicon plasma.

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“…The efficiency of ionization was promoted accordingly. This characteristic of mode transition was reported previously by our group [30–32] as well as the others [34–36].…”

Section: Resultssupporting

confidence: 87%

Section: Experiments and Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Experimental Set-up and Diagnostic Methodsmentioning

confidence: 99%

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Effect of magnetic field on double layer in argon helicon plasma

et al. 2020

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“…Ruilin Cui talked about the experimental results of discharge characteristics and heating mechanism of argon helicon plasma in different wave modes from Langmuir probe, magnetic probe, and local optical emission spectroscopy (LOES) [40]. It is shown that there exist three or four distinct wave modes (i.e., number of half-wavelengths along the axial direction m z 1, 3, 5, or 7) under different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

First Helicon Plasma Physics and Applications Workshop

2022

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The First Helicon Plasma Physics and Applications Workshop was held on September 23−24, 2021, through Zoom Cloud Meeting, instead of in an on-site gathering, due to the COVID-19 pandemic. It was convened by Rod Boswell (IOC) and Guangnan Luo (LOC), and organised by Lei Chang’s group. The workshop attracted 110 registrations and ∼100 online audiences from ∼30 affiliations. There were 33 presentations covering the various fundamental physics of helicon plasma and its applications to space electric propulsion, material processing, and magnetic confinement fusion. This paper highlights the presentations, discussions, and perspectives given in the workshop, serving as reference for the helicon community.

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Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Cheng

Xia

Xiong

2022

Contributions to Plasma Physics

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To find a fast and reliable way predicting the energy deposition of helicon plasmas, this work focuses on machine learning algorithms. Data generation model and the distribution property of the source data are studied, then the classical algorithms and deep neural network (DNN) are built, and these algorithms are studied to test the performance on the energy deposition datasets. Both decision tree classifier (DTC) and support vector machine (SVM) find the electron temperature is the noise feature, and when it is included in the feature vector, the performance will degrade. Therefore feature selection needs be done to obtain high accuracy. For DNN, by directly changing the numbers of the hidden layers and units, test accuracy exceeds 0.95 when hidden layer is greater than three. The feature selection is automatically performed and the learning process is simpler in DNN. When the splitting ratio is varied, the generalization performance of DTC and SVM fluctuates, whereas DNN exhibits no evident change. Compared with the classical algorithms, DNN shows better stability when the source data are changed. The calculations suggest machine learning technique is a promising choice to predict the energy deposition of helicon plasmas.

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Diagnosis of helicon plasma by local OES

Cited by 35 publications

References 43 publications

Effect of magnetic field on double layer in argon helicon plasma

Effect of magnetic field on double layer in argon helicon plasma

First Helicon Plasma Physics and Applications Workshop

Study of the energy deposition of helicon plasmas driven by machine learning algorithms

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