End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network Article Swipe
Yunxiang Wang
,
Ziyuan Yang
,
Pan Hu
,
Sushmit Hossain
,
Zerui Liu
,
Tse‐Hsien Ou
,
Jiacheng Ye
,
Wei Wu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/nano13182561
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/nano13182561
Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields.
Related Topics
Concepts
Path (computing)
Computer science
Probabilistic logic
Process (computing)
Invertible matrix
Inverse
Artificial neural network
Metric (unit)
Deep learning
Artificial intelligence
Computer engineering
Machine learning
Mathematics
Engineering
Operating system
Programming language
Operations management
Pure mathematics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/nano13182561
- https://www.mdpi.com/2079-4991/13/18/2561/pdf?version=1694768477
- OA Status
- gold
- Cited By
- 11
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386838122
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386838122Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/nano13182561Digital Object Identifier
- Title
-
End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-15Full publication date if available
- Authors
-
Yunxiang Wang, Ziyuan Yang, Pan Hu, Sushmit Hossain, Zerui Liu, Tse‐Hsien Ou, Jiacheng Ye, Wei WuList of authors in order
- Landing page
-
https://doi.org/10.3390/nano13182561Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-4991/13/18/2561/pdf?version=1694768477Direct link to full text PDF
- Open access
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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://www.mdpi.com/2079-4991/13/18/2561/pdf?version=1694768477Direct OA link when available
- Concepts
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Path (computing), Computer science, Probabilistic logic, Process (computing), Invertible matrix, Inverse, Artificial neural network, Metric (unit), Deep learning, Artificial intelligence, Computer engineering, Machine learning, Mathematics, Engineering, Operating system, Programming language, Operations management, Pure mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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