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

Abstract

In this study, we present the Method of Spectral Redundancy Reduction (MSRR) for analyzing OES (optical emission spectroscopy) data of dry etching processes based on the principles of spectral clustering. To achieve this, the OES data are transformed into abstract graph matrices whose associated eigenvectors directly indicate anomalies in the data set. We developed an approach that allows for the reduction in temporally resolved optical emission spectra from plasma structuring processes in such a way that individual emission lines can be algorithmically detected, which exhibit a temporal behavior different from the collective behavior of the temporally resolved overall spectrum. The proportion of emission lines that behave consistently throughout the entire process duration is not considered. Our work may find applications in which OES is used as a process-monitoring technique, especially for low-pressure plasma processing. The major benefit of the developed method is that the scale of the original data is kept, making physical interpretations possible despite data reductions.

Keywords

• optical emission spectroscopy
• dry etching processes
• spectral clustering
• Gaussian similarity matrix