Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization Article Swipe
Marius Singler
,
Akshay Patil
,
Linda Ney
,
Andreas Lorenz
,
Sebastian Tepner
,
Florian Clement
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2024.100404
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2024.100404
The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.egyai.2024.100404
- OA Status
- gold
- Cited By
- 1
- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401001448Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.egyai.2024.100404Digital Object Identifier
- Title
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Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-25Full publication date if available
- Authors
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Marius Singler, Akshay Patil, Linda Ney, Andreas Lorenz, Sebastian Tepner, Florian ClementList of authors in order
- Landing page
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https://doi.org/10.1016/j.egyai.2024.100404Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.egyai.2024.100404Direct OA link when available
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Ground truth, Workflow, Deep learning, Robustness (evolution), Artificial intelligence, Computer science, Computer vision, Image quality, Quality (philosophy), Image (mathematics), Gene, Chemistry, Database, Biochemistry, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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