Comparative determination of the electron temperature in Ar- and N2-plasmas with electrostatic probes, optical emission spectroscopy OES and energy dispersive mass spectrometry EDMS

2001 ◽  
Vol 15 (1) ◽  
pp. 49-56 ◽  
Author(s):  
G. Crolly ◽  
H. Oechsner
Keyword(s):  
Mass Spectrometry ◽  
Electron Temperature ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Energy Dispersive ◽  
Comparative Determination

scholarly journals Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

Coatings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1221
Author(s):  
Jun-Hyoung Park ◽  
Ji-Ho Cho ◽  
Jung-Sik Yoon ◽  
Jung-Ho Song
Keyword(s):  
Machine Learning ◽  
Electron Temperature ◽  
Real Time ◽  
Electron Density ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Plasma Parameters ◽  
Plasma Nitridation

We present a non-invasive approach for monitoring plasma parameters such as the electron temperature and density inside a radio-frequency (RF) plasma nitridation device using optical emission spectroscopy (OES) in conjunction with multivariate data analysis. Instead of relying on a theoretical model of the plasma emission to extract plasma parameters from the OES, an empirical correlation was established on the basis of simultaneous OES and other diagnostics. Additionally, we developed a machine learning (ML)-based virtual metrology model for real-time Te and ne monitoring in plasma nitridation processes using an in situ OES sensor. The results showed that the prediction accuracy of electron density was 97% and that of electron temperature was 90%. This method is especially useful in plasma processing because it provides in-situ and real-time analysis without disturbing the plasma or interfering with the process.

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