Keyword: network
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MOP08 Automatic classification of plasma states in an ECR-type ion source plasma, luminosity, ECR, ion-source 45
 
  • A. Fernández-Rua, I. Arredondo, R. Justo, P. Usabiaga, J. Feuchtwanger
    University of the Basque Country (UPV/EHU), Leioa, Spain
  • J. Feuchtwanger
    Ikerbasque, Bilbao, Spain
  
  In this paper we present the methodology used to acquire the data needed to obtain and train a neural network that will be used in an ECR source to infer the state of the plasma. All the data is the combination of the control signals and a set of non-intrusive measurements that can be accessed during normal operation. For this purpose, machine learning techniques are explored. First, a set of characterisation experiments are carried out in which the state of the plasma is detected for different operating conditions that are fed to a clustering algorithm. Second, a supervised learning paradigm is adopted to train a neural network that is capable of determining the state of the plasma at different working states. The variables that are controlled are: the input RF power and gas flow, the non-intrusive measurements that are acquired are: transmitted and reflected RF power and a ccd camera is used to measure the relative luminosity of the plasma. Based on these variables the state of the plasma is determined. This methodology has been applied to the low-power ECR source in which low-density hydrogen plasmas are generated at the IZPILab laboratory of the University of the Basque Country.  
DOI • reference for this paper ※ doi:10.18429/JACoW-ECRIS2024-MOP08  
About • Received ※ 13 September 2024 — Revised ※ 07 February 2025 — Accepted ※ 28 February 2025 — Issued ※ 25 March 2025
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THA3 Waveguide DC breaks with optimized impedance matching networks GUI, ion-source, impedance, simulation 162
 
  • M.K. Covo, B. Ninemire, D.S. Todd, D.Z. Xie, J. Cruz Duran, J.Y. Benitez, J.P. Garcia, L. Phair, M.B. Johnson, P. Bloemhard
    LBNL, Berkeley, CA, USA
  
  A custom 18 GHz waveguide DC break with a built-in impedance matching network, consisting of two inductive irises adjacent to a capacitive gap assembled around a quartz disk, was built for VENUS and simulated using the ANSYS High Frequency Structure Simulator, a finite element analysis tool. The DC break effectively doubled the RF power available for plasma production at the secondary frequency of 18 GHz while maintaining a DC isolation of 32 kV. Measurements of the forward and reflected power coefficients, performed with a network analyzer, showed excellent agreement with the simulations [1]. Additionally, an extended study was conducted to tailor the frequencies of 28, 35, and 45 GHz using WR-34, WR-28, and WR-22 waveguides with built-in impedance matching networks, aiming to predict performance for our upcoming 4th generation low-power, multi-frequency operation of the MARS-D ion source.
[1] M. Kireeff Covo et al., “Inductive Iris Impedance Matching Network for a Compact Waveguide DC Break”, IEEE Transactions on Microwave Theory and Techniques, early access 2024. doi:10.1109/TMTT.2024.3409470. 		 
slides icon Slides THA3 [1.702 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-ECRIS2024-THA3  
About • Received ※ 13 September 2024 — Revised ※ 09 October 2024 — Accepted ※ 30 January 2025 — Issued ※ 18 May 2025
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