Improvement of Plasma Etching Endpoint Detection With Data-Driven Wavelength Selection and Gaussian Mixture Model

The signal-to-noise ratio of optical emission spectroscopy (OES) data has decreased as the plasma etching process has advanced. As a result, not only the advanced endpoint detection method was required, but also the selection of more informative wavelengths. This paper proposes an improved endpoint detection algorithm by combining data-driven wavelength selection and a Gaussian mixture model (GMM). The data-driven wavelength selection algorithm finds the correlation between training data and a sigmoid function of time. Then, using the fitted GMM of the training data in latent space, the endpoint of the test data is determined in real-time. The proposed algorithm’s performance was evaluated using real OES data, comprised of seven operations. The correlation-based wavelength selection algorithm significantly reduced detection error by 70.2% when compared to the conventional method, which s…