Abstract
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.
Highlights
Plasma processing technology has played a crucial role in the surface modification of different materials, such as electronics, energy storage, automotive, health, or environmental applications [1,2]
In this study, we propose a non-invasive approach for monitoring plasma parameters such as 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
Summary
Plasma processing technology has played a crucial role in the surface modification of different materials, such as electronics, energy storage, automotive, health, or environmental applications [1,2]. In order to use the OES method to determine the Te and ne , one usually applies the so-called line intensity ratio method, which requires a relative intensity calibrated spectroscopic system and a suitable physics-based model for excited species in the plasmas to be investigated, such as the coronal model or the collisional radiative model (CRM) [6,7,8] The emphasis of these models is the identification of major production and depopulation processes under different plasma discharge conditions and for different kinds of excited species. Due to the tremendous efforts in this area [9], the line intensity ratio method is expected to be further developed in the future This physics-based model approach is still limited in industrial application to plasma processes that use mixture gases [3]. In this study, we propose a non-invasive approach for monitoring plasma parameters such as electron temperature and density inside a radio-frequency (RF) plasma nitridation device using optical emission spectroscopy (OES) in conjunction with multivariate data analysis.
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