Open AccessPlasma optical emission spectroscopy based on feedforward neural network
Author(s) -
Yanfei Wang,
Xi-Ming Zhu,
Mingzhi Zhang,
Sheng-Feng Meng,
Jun-Wei Jia,
Hua Chai,
Yan Wang,
Zhiliang Ning
Publication year - 2021
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.70.20202248
Subject(s) - artificial neural network , standard deviation , computer science , feedforward neural network , plasma , spectroscopy , computational physics , optics , materials science , physics , artificial intelligence , statistics , mathematics , quantum mechanics
Optical emission spectroscopy (OES) has been widely applied to plasma etching, material processing, development of plasma equipment and technology, as well as plasma propulsion. The collisional-radiative model used in OES is affected by the deviation of fundamental data such as collision cross sections, thus leading to the error in diagnostic results. In this work, a novel method is developed based on feedforward neural network for OES. By comparing the error characteristics of the new method with those of the traditional least-square diagnostic method, it is found that the neural network diagnosis method can reduce the transmission of basic data deviation to the diagnosis results by identifying the characteristics of the spectral vector. This is confirmed by the experimental results. Finally, the mechanism of the neural network algorithm against fundamental data deviation is analyzed. This method also has a good application prospect in plasma parameter online monitoring, imaging monitoring and mass data processing.