Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers
Stacking faults in epitaxial silicon wafers are structural defects that can reduce the recombination lifetime of the final solar cells significantly. They are known to originate mostly at the interface between substrate and deposited layer, at contamination particles and atomic steps. This work pre...
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| Format: | Article |
| Language: | English |
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TIB Open Publishing
2025-02-01
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| Series: | SiliconPV Conference Proceedings |
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| Online Access: | https://www.tib-op.org/ojs/index.php/siliconpv/article/view/1265 |
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| author | Theresa Trötschler Saed Al-Hajjawi Siddharth Raghavendran Jonas Haunschild Matthias Demant Stefan Rein |
| author_facet | Theresa Trötschler Saed Al-Hajjawi Siddharth Raghavendran Jonas Haunschild Matthias Demant Stefan Rein |
| author_sort | Theresa Trötschler |
| collection | DOAJ |
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Stacking faults in epitaxial silicon wafers are structural defects that can reduce the recombination lifetime of the final solar cells significantly. They are known to originate mostly at the interface between substrate and deposited layer, at contamination particles and atomic steps. This work presents a non-destructive and automated characterization method on full-size wafers to locate stacking faults and determine their layer of origin to identify process-based root causes. A deep learning model and a quantification via geometric defect properties is realized on dark field microscope images, with the potential to be transferred to inline images measured in dark field mode with high-resolution cameras. We achieve detection rates up to 92% for regular wafer surfaces. The depth analysis combines geometric properties of the stacking faults and measured wafer thickness and is applied on full-scale epitaxial wafers. Most stacking faults are confirmed to originate at the interface layer and their number is higher by 1-2 orders of magnitude when deposition occurs on a reorganized porous layer. However, our results also indicate that a non-negligible part of stacking faults has its origin within the epitaxial layer.
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| format | Article |
| id | doaj-art-08df78aacd744420836d2ab95aab32f4 |
| institution | Kabale University |
| issn | 2940-2123 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | TIB Open Publishing |
| record_format | Article |
| series | SiliconPV Conference Proceedings |
| spelling | doaj-art-08df78aacd744420836d2ab95aab32f42025-02-04T09:54:12ZengTIB Open PublishingSiliconPV Conference Proceedings2940-21232025-02-01210.52825/siliconpv.v2i.1265Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon WafersTheresa Trötschler0https://orcid.org/0009-0001-9778-3376Saed Al-Hajjawi1Siddharth Raghavendran2Jonas Haunschild3Matthias Demant4Stefan Rein5Fraunhofer Institute for Solar Energy SystemsFraunhofer Institute for Solar Energy Systems Fraunhofer Institute for Solar Energy Systems Fraunhofer Institute for Solar Energy Systems Fraunhofer Institute for Solar Energy Systems Fraunhofer Institute for Solar Energy Systems Stacking faults in epitaxial silicon wafers are structural defects that can reduce the recombination lifetime of the final solar cells significantly. They are known to originate mostly at the interface between substrate and deposited layer, at contamination particles and atomic steps. This work presents a non-destructive and automated characterization method on full-size wafers to locate stacking faults and determine their layer of origin to identify process-based root causes. A deep learning model and a quantification via geometric defect properties is realized on dark field microscope images, with the potential to be transferred to inline images measured in dark field mode with high-resolution cameras. We achieve detection rates up to 92% for regular wafer surfaces. The depth analysis combines geometric properties of the stacking faults and measured wafer thickness and is applied on full-scale epitaxial wafers. Most stacking faults are confirmed to originate at the interface layer and their number is higher by 1-2 orders of magnitude when deposition occurs on a reorganized porous layer. However, our results also indicate that a non-negligible part of stacking faults has its origin within the epitaxial layer. https://www.tib-op.org/ojs/index.php/siliconpv/article/view/1265Kerfless Silicon GrowthStacking FaultsImage SegmentationQuality Inspection |
| spellingShingle | Theresa Trötschler Saed Al-Hajjawi Siddharth Raghavendran Jonas Haunschild Matthias Demant Stefan Rein Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers SiliconPV Conference Proceedings Kerfless Silicon Growth Stacking Faults Image Segmentation Quality Inspection |
| title | Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers |
| title_full | Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers |
| title_fullStr | Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers |
| title_full_unstemmed | Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers |
| title_short | Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers |
| title_sort | deep learning based depth tracking of stacking faults in epitaxially grown silicon wafers |
| topic | Kerfless Silicon Growth Stacking Faults Image Segmentation Quality Inspection |
| url | https://www.tib-op.org/ojs/index.php/siliconpv/article/view/1265 |
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