A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers
Epitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | EPJ Photovoltaics |
| Subjects: | |
| Online Access: | https://www.epj-pv.org/articles/epjpv/full_html/2023/01/pv220053/pv220053.html |
| _version_ | 1797938427411300352 |
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| author | Wörnhör Alexandra Demant Matthias Vahlman Henri Rein Stefan |
| author_facet | Wörnhör Alexandra Demant Matthias Vahlman Henri Rein Stefan |
| author_sort | Wörnhör Alexandra |
| collection | DOAJ |
| description | Epitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate. With high-throughput inline production processes, fast and reliable evaluation processes are crucial. The quality of the porous layers plays an important role regarding a successful detachment. Therefore, we present a fast and non-destructive investigation algorithm of thin, porous silicon layers. We predict the layer parameters directly from inline reflectance data by using a convolutional neural network (CNN), which is inspired by a comprehensive optical modelling approach from literature. There, a numerical fitting approach on reflection curves calculated with a physical model is performed. By adding the physical model to the CNN, we create a hybrid model, that not only predicts layer parameters, but also recalculates reflection curves. This allows a consistency check for a self-supervised network optimization. Evaluation on experimental data shows a high similarity with Scanning Electron Microscopy (SEM) measurements. Since parallel computation is possible with the CNN, 30.000 samples can be evaluated in roughly 100 ms. |
| first_indexed | 2024-04-10T19:00:40Z |
| format | Article |
| id | doaj.art-efdc2fd6e2bb4753a0e0b476adb106e1 |
| institution | Directory Open Access Journal |
| issn | 2105-0716 |
| language | English |
| last_indexed | 2024-04-10T19:00:40Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Photovoltaics |
| spelling | doaj.art-efdc2fd6e2bb4753a0e0b476adb106e12023-01-31T09:58:40ZengEDP SciencesEPJ Photovoltaics2105-07162023-01-0114410.1051/epjpv/2022035pv220053A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layersWörnhör Alexandra0Demant Matthias1Vahlman Henri2Rein Stefan3Fraunhofer-Institut für Solare Energiesysteme ISEFraunhofer-Institut für Solare Energiesysteme ISEFraunhofer-Institut für Solare Energiesysteme ISEFraunhofer-Institut für Solare Energiesysteme ISEEpitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate. With high-throughput inline production processes, fast and reliable evaluation processes are crucial. The quality of the porous layers plays an important role regarding a successful detachment. Therefore, we present a fast and non-destructive investigation algorithm of thin, porous silicon layers. We predict the layer parameters directly from inline reflectance data by using a convolutional neural network (CNN), which is inspired by a comprehensive optical modelling approach from literature. There, a numerical fitting approach on reflection curves calculated with a physical model is performed. By adding the physical model to the CNN, we create a hybrid model, that not only predicts layer parameters, but also recalculates reflection curves. This allows a consistency check for a self-supervised network optimization. Evaluation on experimental data shows a high similarity with Scanning Electron Microscopy (SEM) measurements. Since parallel computation is possible with the CNN, 30.000 samples can be evaluated in roughly 100 ms.https://www.epj-pv.org/articles/epjpv/full_html/2023/01/pv220053/pv220053.htmlhybrid modelself-consistentporous siliconthin filmsreflectometry |
| spellingShingle | Wörnhör Alexandra Demant Matthias Vahlman Henri Rein Stefan A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers EPJ Photovoltaics hybrid model self-consistent porous silicon thin films reflectometry |
| title | A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers |
| title_full | A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers |
| title_fullStr | A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers |
| title_full_unstemmed | A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers |
| title_short | A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers |
| title_sort | self consistent hybrid model connects empirical and optical models for fast non destructive inline characterization of thin porous silicon layers |
| topic | hybrid model self-consistent porous silicon thin films reflectometry |
| url | https://www.epj-pv.org/articles/epjpv/full_html/2023/01/pv220053/pv220053.html |
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