High‐Speed Design of Multiplexed Meta‐Optics Enabled by Physics‐Driven Self‐Supervised Network Article Swipe
Yuqing He
,
Sheng Ye
,
Yue Han
,
Mingna Xun
,
Qiang Li
,
Ruiqi Wang
,
Qihuang Gong
,
Yan Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202509242
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202509242
The artificial intelligence (AI) can accelerate the meta‐optics design by rapidly predicting the transmission coefficients of individual meta‐atoms. However, extensive optimization iterations are usually required to complete the desired metasurface consisting of massive meta‐atoms. For designing meta‐holography, any change to the target image forces the whole process to repeat, resulting in lengthy computation time. Here, a physics‐driven self‐supervised network (PDSS‐Net) built upon AI‐assisted optimization frameworks are proposed to further expedite the design process. The encoder‐decoder module introduced into the PDSS‐Net can establish a mapping between the input holographic images and the output structural parameters of all meta‐atoms. After self‐supervised training, the network learns this mapping and enables iteration‐free inference for inputs beyond the training dataset. The design of 2K‐resolution, three‐wavelength‐multiplexed meta‐holograms is completed within one second, achieving a computational speedup exceeding 1000‐fold over conventional optimization‐based approaches. By retraining, more complex tasks are achieved as demonstrated in the design of both the wavelength‐polarization‐depth multiplexed scalar and vectorial meta‐holograms. This iteration‐free computational paradigm with adaptability in typical multiplexed meta‐optics can be applied to the intelligent design of multifunctional metasurfaces, facilitating large‐scale applications of meta‐devices.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202509242
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509242
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412773692
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412773692Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/advs.202509242Digital Object Identifier
- Title
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High‐Speed Design of Multiplexed Meta‐Optics Enabled by Physics‐Driven Self‐Supervised NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-30Full publication date if available
- Authors
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Yuqing He, Sheng Ye, Yue Han, Mingna Xun, Qiang Li, Ruiqi Wang, Qihuang Gong, Yan LiList of authors in order
- Landing page
-
https://doi.org/10.1002/advs.202509242Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509242Direct link to full text PDF
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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://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509242Direct OA link when available
- Concepts
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Computer science, Holography, Multiplexing, Speedup, Inference, Computation, Artificial intelligence, Optics, Algorithm, Physics, Operating system, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.both | 150 |
| abstract_inverted_index.into | 78 |
| abstract_inverted_index.more | 139 |
| abstract_inverted_index.over | 133 |
| abstract_inverted_index.this | 104 |
| abstract_inverted_index.upon | 62 |
| abstract_inverted_index.with | 162 |
| abstract_inverted_index.After | 98 |
| abstract_inverted_index.Here, | 55 |
| abstract_inverted_index.built | 61 |
| abstract_inverted_index.image | 43 |
| abstract_inverted_index.input | 87 |
| abstract_inverted_index.tasks | 141 |
| abstract_inverted_index.time. | 54 |
| abstract_inverted_index.whole | 46 |
| abstract_inverted_index.beyond | 112 |
| abstract_inverted_index.change | 39 |
| abstract_inverted_index.design | 9, 72, 117, 148, 174 |
| abstract_inverted_index.forces | 44 |
| abstract_inverted_index.images | 89 |
| abstract_inverted_index.inputs | 111 |
| abstract_inverted_index.learns | 103 |
| abstract_inverted_index.module | 76 |
| abstract_inverted_index.output | 92 |
| abstract_inverted_index.scalar | 154 |
| abstract_inverted_index.target | 42 |
| abstract_inverted_index.within | 124 |
| abstract_inverted_index.applied | 170 |
| abstract_inverted_index.between | 85 |
| abstract_inverted_index.complex | 140 |
| abstract_inverted_index.desired | 29 |
| abstract_inverted_index.enables | 107 |
| abstract_inverted_index.further | 69 |
| abstract_inverted_index.lengthy | 52 |
| abstract_inverted_index.mapping | 84, 105 |
| abstract_inverted_index.massive | 33 |
| abstract_inverted_index.network | 59, 102 |
| abstract_inverted_index.process | 47 |
| abstract_inverted_index.rapidly | 11 |
| abstract_inverted_index.repeat, | 49 |
| abstract_inverted_index.second, | 126 |
| abstract_inverted_index.speedup | 130 |
| abstract_inverted_index.typical | 165 |
| abstract_inverted_index.usually | 24 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.achieved | 143 |
| abstract_inverted_index.complete | 27 |
| abstract_inverted_index.dataset. | 115 |
| abstract_inverted_index.expedite | 70 |
| abstract_inverted_index.paradigm | 161 |
| abstract_inverted_index.process. | 73 |
| abstract_inverted_index.proposed | 67 |
| abstract_inverted_index.required | 25 |
| abstract_inverted_index.training | 114 |
| abstract_inverted_index.achieving | 127 |
| abstract_inverted_index.completed | 123 |
| abstract_inverted_index.designing | 36 |
| abstract_inverted_index.establish | 82 |
| abstract_inverted_index.exceeding | 131 |
| abstract_inverted_index.extensive | 20 |
| abstract_inverted_index.inference | 109 |
| abstract_inverted_index.resulting | 50 |
| abstract_inverted_index.training, | 100 |
| abstract_inverted_index.vectorial | 156 |
| abstract_inverted_index.PDSS‐Net | 80 |
| abstract_inverted_index.accelerate | 6 |
| abstract_inverted_index.artificial | 2 |
| abstract_inverted_index.consisting | 31 |
| abstract_inverted_index.frameworks | 65 |
| abstract_inverted_index.individual | 17 |
| abstract_inverted_index.introduced | 77 |
| abstract_inverted_index.iterations | 22 |
| abstract_inverted_index.parameters | 94 |
| abstract_inverted_index.predicting | 12 |
| abstract_inverted_index.structural | 93 |
| abstract_inverted_index.1000‐fold | 132 |
| abstract_inverted_index.approaches. | 136 |
| abstract_inverted_index.computation | 53 |
| abstract_inverted_index.holographic | 88 |
| abstract_inverted_index.intelligent | 173 |
| abstract_inverted_index.metasurface | 30 |
| abstract_inverted_index.multiplexed | 153, 166 |
| abstract_inverted_index.retraining, | 138 |
| abstract_inverted_index.(PDSS‐Net) | 60 |
| abstract_inverted_index.adaptability | 163 |
| abstract_inverted_index.applications | 180 |
| abstract_inverted_index.coefficients | 15 |
| abstract_inverted_index.conventional | 134 |
| abstract_inverted_index.demonstrated | 145 |
| abstract_inverted_index.facilitating | 178 |
| abstract_inverted_index.intelligence | 3 |
| abstract_inverted_index.optimization | 21, 64 |
| abstract_inverted_index.transmission | 14 |
| abstract_inverted_index.AI‐assisted | 63 |
| abstract_inverted_index.computational | 129, 160 |
| abstract_inverted_index.large‐scale | 179 |
| abstract_inverted_index.metasurfaces, | 177 |
| abstract_inverted_index.meta‐atoms. | 18, 34, 97 |
| abstract_inverted_index.meta‐optics | 8, 167 |
| abstract_inverted_index.meta‐devices. | 182 |
| abstract_inverted_index.multifunctional | 176 |
| abstract_inverted_index.2K‐resolution, | 119 |
| abstract_inverted_index.iteration‐free | 108, 159 |
| abstract_inverted_index.meta‐holograms | 121 |
| abstract_inverted_index.physics‐driven | 57 |
| abstract_inverted_index.encoder‐decoder | 75 |
| abstract_inverted_index.meta‐holograms. | 157 |
| abstract_inverted_index.self‐supervised | 58, 99 |
| abstract_inverted_index.meta‐holography, | 37 |
| abstract_inverted_index.optimization‐based | 135 |
| abstract_inverted_index.three‐wavelength‐multiplexed | 120 |
| abstract_inverted_index.wavelength‐polarization‐depth | 152 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100332367 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I181877577, https://openalex.org/I20231570, https://openalex.org/I39774598, https://openalex.org/I4210127494 |
| citation_normalized_percentile.value | 0.25518235 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |