Work

Development of optical emission spectroscopy method with neural network model: Case study of determining the electron density in a xenon microwave discharge

Year: 2024

Type: article

Abstract: Optical emission spectroscopy (OES) is an important technique for plasma diagnostics. Random deviation is inevitable during the measurement of plasma emission spectra due to the imperfection of instruments and other interferences. On the other hand, inaccuracies in the collision cross-section data can lead to distortion of the collisional-radiative (CR) model. The coupling of theoretical and experimental error factors can pose difficulties for accurate diagnostics of plasma. This work presents the development of the OES method for xenon plasma that employs a neural network model to integrate prior information on the characteristics of instrument noise and model distortions, thereby improving the accuracy of OES diagnostics. The neural network model takes emission line ratios as input and normalized electron density as output and is trained using a dataset that is generated with a CR model and an instrument disturbance model. The neural network-based OES method is implemented to determine the electron density in a microwave discharge chamber and compared with a traditional OES method with a multi-variant fitting technique. A significant improvement on relative deviation of diagnostic results is observed, which promises a good prospect for further development.

Source: Journal of Applied Physics

DOI: 10.1063/5.0243484

Authors: Yan-Fei Wang, Xi-Ming Zhu

Institutions: Harbin Institute of Technology, Ministry of Industry and Information Technology

Language: English

Topic: Plasma Diagnostics and Applications

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funder: National Natural Science Foundation of China