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In this study, experiments were conducted to etch SiO₂ and Si₃N₄ by introducing N₂ at flow rates of 0, 2, 4, 6, and 8 sccm into a CF₄/O₂ plasma. OES (Optical Emission Spectroscopy) data were systematically collected and analyzed under each condition to understand the impact of N₂ addition on plasma chemistry. Machine learning techniques were applied to identify specific OES wavelengths that are critical to the etch rate and selectivity of both materials. Furthermore, the importance of the selected wavelengths was determined using XAI (Explainable Artificial Intelligence) methods.
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The SiO2 etching process using CF4/O2 plasma is a critical step in semiconductor manufacturing, where process efficiency and precise control are essential. In this study, optical emission spectroscopy (OES) data was utilized in real-time to analyze the correlation between plasma conditions and the etch rate (ER) during the process. Specifically, the source and bias power were divided into four different conditions to systematically evaluate the changes in plasma characteristics and the etching process. Based on this evaluation, a physical model was developed to predict the etch rate.
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