Plasma optical emission spectroscopy based on feedforward neural network

被引：6

作者：

Wang Yan-Fei ^{[1
]}

Zhu Xi-Ming ^{[1
,2
,3
]}

Zhang Ming-Zhi ^{[3
]}

Meng Sheng-Feng ^{[1
]}

Jia Jun-Wei ^{[3
]}

Chai Hao ^{[3
]}

Wang Yang ^{[1
]}

Ning Zhong-Xi ^{[1
,2
]}

机构：

[1] Harbin Inst Technol, Harbin 150001, Peoples R China

[2] Minist Ind & Informat Technol, Key Lab Aerosp Plasma Prop, Harbin 150001, Peoples R China

[3] Beijing Orient Inst Measurement & Test, Beijing 100086, Peoples R China

来源：

ACTA PHYSICA SINICA | 2021年 / 70卷 / 09期

基金：

中国国家自然科学基金;

关键词：

optical emission spectroscopy; feedforward neural network; collisional-radiative model;

D O I：

10.7498/aps.70.20202248

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：12

共 40 条

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