Performance evaluation of a permanent ring magnet based helicon plasma source for negative ion source research

2017 ◽  
Vol 88 (10) ◽  
pp. 103509 ◽  
Author(s):  
Arun Pandey ◽  
M. Bandyopadhyay ◽  
Dass Sudhir ◽  
A. Chakraborty
2019 ◽  
Vol 61 (6) ◽  
pp. 065003 ◽  
Author(s):  
A Pandey ◽  
Debrup Mukherjee ◽  
Dipshikha Borah ◽  
M Bandyopadhyay ◽  
Himanshu Tyagi ◽  
...  

2018 ◽  
Vol 89 (10) ◽  
pp. 103504 ◽  
Author(s):  
R. Agnello ◽  
M. Barbisan ◽  
I. Furno ◽  
Ph. Guittienne ◽  
A. A. Howling ◽  
...  

Author(s):  
Vipin Shukla ◽  
Mainak Bandyopadhyay ◽  
Vivek Pandya ◽  
Arun Pandey

Analytical expressions are used frequently for the determination and analysis of plasma parameters. Instead of relying on analytical expressions, the proposed method uses regression techniques supplemented with experimental data for the selected parameters (plasma potential). In the machine learning domain, this is equivalent to the creation of the training data set, building and training the model, and authenticating the result over a range of desired physical parameters. An experimental dataset is built using two axially movable Triple Langmuir Probe (TLPs) which measure the electron temperature, electron density, and electric potential of a plasma. The presented work is a first step towards developing an inclusive model with detailed kinetic simulations capable of characterizing the HELicon Experiment for Negative ion source (HELEN-I) with a single driver. Plasma potential is measured at different axial locations (z) by keeping pressure fixed at 6 mTorr.


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