Sensitivity Enhancement of SiO2Plasma 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 SiO2 plasma etching using optical emission spectrum analysis. Optical emission spectroscopy (OES) signals were collected from the SiO2 plasma etching processes, and the modified GMM was applied to SiO2 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.