scholarly journals Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy

2021 ◽  
Vol 92 (4) ◽  
pp. 043520
Author(s):  
C. M. Samuell ◽  
A. G. Mclean ◽  
C. A. Johnson ◽  
F. Glass ◽  
A. E. Jaervinen
Keyword(s):  
Machine Learning ◽  
Electron Temperature ◽  
Plasma Detachment

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.

Machine learning application to predict the electron temperature on the J-TEXT tokamak

2021 ◽  
Author(s):  
Jiaolong Dong ◽  
Jianchao Li ◽  
Yonghua Ding ◽  
Xiaoqing Zhang ◽  
Nengchao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electron Temperature

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Density threshold for plasma detachment in gas target

1997 ◽  
Vol 241-243 (1) ◽  
pp. 349-352
Author(s):  
N Ezumi
Keyword(s):  
Gas Target ◽  
Plasma Detachment ◽  
Density Threshold

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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