Sensitivity Enhancement of SiO2 Plasma Etching Endpoint Detection Using Modified Gaussian Mixture Model

In this study, the Gaussian mixture model (GMM) was modified and implemented to determine the real-time endpoint of SiO
₂
plasma etching using optical emission spectrum analysis. Optical emission spectroscopy (OES) signals were collected from the SiO
₂
plasma etching processes, and the modified GMM was applied to SiO
₂
etching with relative areas of 8.0, 4.0, and 1.0 %. Consequently, the sensitivity of OES signals was improved by ~5.5 times, and the sensitivity factor of the modified GMM was increased by approximately two times, compared with those of the modified K-means cluster analysis (another clustering technique). In addition, 60 peaks related to the reactants were selected out of 6144 signals to improve the sensitivity of the modified GMM with full-spectrum wavelengths. The modified GMM analysis using the 60 reactant-related peaks exhibited a higher sensitivity (~1.4 times) than that with 6144 full-spectrum OES signals. Thus, the modified GMM can be a suitable and effective clustering technique for etching endpoint detection.