Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films
Abstract We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhan...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-024-01723-8 |
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| _version_ | 1841544271321104384 |
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| author | Waqas W. Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu |
| author_facet | Waqas W. Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu |
| author_sort | Waqas W. Ahmed |
| collection | DOAJ |
| description | Abstract We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes, requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device, exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells, photodetectors, and optical filters. |
| format | Article |
| id | doaj-art-3719c18a511040d98067be53ce7978c8 |
| institution | Kabale University |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-3719c18a511040d98067be53ce7978c82025-01-12T12:40:22ZengNature Publishing GroupLight: Science & Applications2047-75382025-01-0114111110.1038/s41377-024-01723-8Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon filmsWaqas W. Ahmed0Haicheng Cao1Changqing Xu2Mohamed Farhat3Muhammad Amin4Xiaohang Li5Xiangliang Zhang6Ying Wu7Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)College of Engineering, Taibah UniversityDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)Abstract We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes, requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device, exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells, photodetectors, and optical filters.https://doi.org/10.1038/s41377-024-01723-8 |
| spellingShingle | Waqas W. Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films Light: Science & Applications |
| title | Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films |
| title_full | Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films |
| title_fullStr | Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films |
| title_full_unstemmed | Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films |
| title_short | Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films |
| title_sort | machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra thin silicon films |
| url | https://doi.org/10.1038/s41377-024-01723-8 |
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