Recognition of Ethylene Plasma Spectra 1D Data Based on Deep Convolutional Neural Networks

Abstract

As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application.