Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

Coatings

Jung-Ho Song¹  Jun-Hyoung Park¹  Jung-Sik Yoon²  Ji-Ho Cho² 

[1] Fundamental Technology Research Division, Institute of Plasma Technology, Korea Institute of Fusion Energy, Gunsan-si 54004, Korea;Plasma E. I. Convergence Research Center, Korea Institute of Fusion Energy, Gunsan-si 54004, Korea;

关键词: optical emission spectroscopy;    plasma nitridation;    plasma parameters;    machine learning;    virtual metrology;   

DOI  :  10.3390/coatings11101221

来源: DOAJ

【 摘 要 】

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 $T_{\mathrm{e}}$ and $n_{\mathrm{e}}$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.

【 授权许可】

Unknown