Machine learning prediction of electron density and temperature from optical emission spectroscopy in nitrogen plasma

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.