Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning
Metasurfaces can provide unprecedented degree of freedom in manipulating electromagnetic waves and have been introduced to holography. Aiming to explore the full capability for information presentation, vectorial metasurface holography is sprung up, which exhibits mesmerizing capability in carrying...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127522008954 |
| _version_ | 1811249609044918272 |
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| author | Ruichao Zhu Jiafu Wang Chang Ding Yongfeng Li Zuntian Chu Xiaofeng Wang Tonghao Liu Yajuan Han Bo Feng Shaobo Qu |
| author_facet | Ruichao Zhu Jiafu Wang Chang Ding Yongfeng Li Zuntian Chu Xiaofeng Wang Tonghao Liu Yajuan Han Bo Feng Shaobo Qu |
| author_sort | Ruichao Zhu |
| collection | DOAJ |
| description | Metasurfaces can provide unprecedented degree of freedom in manipulating electromagnetic waves and have been introduced to holography. Aiming to explore the full capability for information presentation, vectorial metasurface holography is sprung up, which exhibits mesmerizing capability in carrying information compared with scalar counterpart. However, the more flexible modulation means the more complex design dimension, which hinders the development of vectorial metasurface holography. In this work, we propose an orthogonal I-shaped structure embedded with resistors to synthesize arbitrary polarization with less crosstalk. Benefiting from the independent control of phase and amplitude on orthogonal base, the orthogonality-simplified machine learning framework is employed to assist vectorial metasurface holography design. As a proof-of-concept, a vectorial metasurface holography carrying multi-polarization information was designed, simulated and measured. All the results exhibit a high degree of consistency, which fully demonstrates the effectiveness of our design. Encouragingly, our method paves a new route to simplify machine learning framework based on physical significance, which can be handily extended to more functional structures. |
| first_indexed | 2024-04-12T15:49:35Z |
| format | Article |
| id | doaj.art-4eaea75881c148a0b42406443c670883 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-04-12T15:49:35Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-4eaea75881c148a0b42406443c6708832022-12-22T03:26:33ZengElsevierMaterials & Design0264-12752022-11-01223111273Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learningRuichao Zhu0Jiafu Wang1Chang Ding2Yongfeng Li3Zuntian Chu4Xiaofeng Wang5Tonghao Liu6Yajuan Han7Bo Feng8Shaobo Qu9Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaCorresponding authors.; Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaCorresponding authors.; Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaShaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, Air Force Engineering University, Xi'an, Shaanxi 710051, ChinaMetasurfaces can provide unprecedented degree of freedom in manipulating electromagnetic waves and have been introduced to holography. Aiming to explore the full capability for information presentation, vectorial metasurface holography is sprung up, which exhibits mesmerizing capability in carrying information compared with scalar counterpart. However, the more flexible modulation means the more complex design dimension, which hinders the development of vectorial metasurface holography. In this work, we propose an orthogonal I-shaped structure embedded with resistors to synthesize arbitrary polarization with less crosstalk. Benefiting from the independent control of phase and amplitude on orthogonal base, the orthogonality-simplified machine learning framework is employed to assist vectorial metasurface holography design. As a proof-of-concept, a vectorial metasurface holography carrying multi-polarization information was designed, simulated and measured. All the results exhibit a high degree of consistency, which fully demonstrates the effectiveness of our design. Encouragingly, our method paves a new route to simplify machine learning framework based on physical significance, which can be handily extended to more functional structures.http://www.sciencedirect.com/science/article/pii/S0264127522008954MetasurfaceVectorial holographyMachine learningOrthogonalitySimplified design |
| spellingShingle | Ruichao Zhu Jiafu Wang Chang Ding Yongfeng Li Zuntian Chu Xiaofeng Wang Tonghao Liu Yajuan Han Bo Feng Shaobo Qu Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning Materials & Design Metasurface Vectorial holography Machine learning Orthogonality Simplified design |
| title | Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning |
| title_full | Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning |
| title_fullStr | Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning |
| title_full_unstemmed | Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning |
| title_short | Vectorial-Holography metasurface empowered by Orthogonality-Simplified Machine learning |
| title_sort | vectorial holography metasurface empowered by orthogonality simplified machine learning |
| topic | Metasurface Vectorial holography Machine learning Orthogonality Simplified design |
| url | http://www.sciencedirect.com/science/article/pii/S0264127522008954 |
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