Coatings | |
Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma | |
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
【 授权许可】
Unknown