About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction
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 eig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Plasma |
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| Online Access: | https://www.mdpi.com/2571-6182/7/1/15 |
| _version_ | 1797239611151351808 |
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| author | Micha Haase Mudassir Ali Sayyed Jan Langer Danny Reuter Harald Kuhn |
| author_facet | Micha Haase Mudassir Ali Sayyed Jan Langer Danny Reuter Harald Kuhn |
| author_sort | Micha Haase |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T17:54:17Z |
| format | Article |
| id | doaj.art-e7e7edcfb5ec4f37b5b449d766e06424 |
| institution | Directory Open Access Journal |
| issn | 2571-6182 |
| language | English |
| last_indexed | 2024-04-24T17:54:17Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plasma |
| spelling | doaj.art-e7e7edcfb5ec4f37b5b449d766e064242024-03-27T14:01:06ZengMDPI AGPlasma2571-61822024-03-017125828310.3390/plasma7010015About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy ReductionMicha Haase0Mudassir Ali Sayyed1Jan Langer2Danny Reuter3Harald Kuhn4Fraunhofer Institute for Electronic Nano Systems (ENAS), 09126 Chmenitz, GermanyFraunhofer Institute for Electronic Nano Systems (ENAS), 09126 Chmenitz, GermanyFraunhofer Institute for Electronic Nano Systems (ENAS), 09126 Chmenitz, GermanyFraunhofer Institute for Electronic Nano Systems (ENAS), 09126 Chmenitz, GermanyFraunhofer Institute for Electronic Nano Systems (ENAS), 09126 Chmenitz, GermanyIn 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.https://www.mdpi.com/2571-6182/7/1/15optical emission spectroscopydry etching processesspectral clusteringGaussian similarity matrix |
| spellingShingle | Micha Haase Mudassir Ali Sayyed Jan Langer Danny Reuter Harald Kuhn About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction Plasma optical emission spectroscopy dry etching processes spectral clustering Gaussian similarity matrix |
| title | About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction |
| title_full | About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction |
| title_fullStr | About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction |
| title_full_unstemmed | About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction |
| title_short | About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction |
| title_sort | about the data analysis of optical emission spectra of reactive ion etching processes the method of spectral redundancy reduction |
| topic | optical emission spectroscopy dry etching processes spectral clustering Gaussian similarity matrix |
| url | https://www.mdpi.com/2571-6182/7/1/15 |
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