Spectral clustering algorithm for real‐time endpoint detection of silicon nitride plasma etching

Abstract

The spectral clustering algorithm (SCA) is developed for endpoint detection (EPD) of Si 3 N 4 plasma etching using optical emission spectroscopy (OES). OES signals are collected in real-time and filtered using discrete wavelet transform (DWT).The SCA using 3648 full-spectrum wavelengths with DWT filtering improves signal-to-noise ratio (SNR) by 2.78 times compared to single-wavelength related to N 2 molecule in 1.0% relative area etching. The wavelengths

About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction

“…In particular, from the perspective of the scientific community interested in reactive etching processes, an optimized endpoint signal without clear physical meaning is not satisfactory. Therefore, ongoing investigations focus on the time-resolved application of the MSRR to evaluate whether a "time-resolved reduced spectrum" similar to that presented in [3,4,17] can be used as a "real-time endpoint control" with increased sensitivity. In contrast to the literature listed here, the advantage would be that the calculated endpoint signal can be directly assigned to specific plasma species.…”