Metasurface-Based Image Classification Using Diffractive Deep Neural Network Article Swipe
Kaiyang Cheng
,
Cong Deng
,
Fengyu Ye
,
Hongqiang Li
,
Fei Shen
,
Yuancheng Fan
,
Yubin Gong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/nano14221812
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/nano14221812
The computer-assisted inverse design of photonic computing, especially by leveraging artificial intelligence algorithms, offers great convenience to accelerate the speed of development and improve calculation accuracy. However, traditional thickness-based modulation methods are hindered by large volume and difficult fabrication process, making it hard to meet the data-driven requirements of flexible light modulation. Here, we propose a diffractive deep neural network (D2NN) framework based on a three-layer all-dielectric phased transmitarray as hidden layers, which can perform the classification of handwritten digits. By tailoring the radius of a silicon nanodisk of a meta-atom, the metasurface can realize the phase profile calculated by D2NN and maintain a relative high transmittance of 0.9 at a wavelength of 600 nm. The designed image classifier consists of three layers of phase-only metasurfaces, each of which contains 1024 units, mimicking a fully connected neural network through the diffraction of light fields. The classification task of handwriting digits from the ‘0’ to ‘5’ dataset is verified, with an accuracy of over 90% on the blind test dataset, as well as demonstrated by the full-wave simulation. Furthermore, the performance of the more complex animal image classification task is also validated by increasing the number of neurons to enhance the connectivity of the neural network. This study may provide a possible solution for practical applications such as biomedical detection, image processing, and machine vision based on all-optical computing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/nano14221812
- OA Status
- gold
- Cited By
- 2
- References
- 54
- Related Works
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- OpenAlex ID
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https://openalex.org/W4404295879Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/nano14221812Digital Object Identifier
- Title
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Metasurface-Based Image Classification Using Diffractive Deep Neural NetworkWork 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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2024-11-12Full publication date if available
- Authors
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Kaiyang Cheng, Cong Deng, Fengyu Ye, Hongqiang Li, Fei Shen, Yuancheng Fan, Yubin GongList of authors in order
- Landing page
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https://doi.org/10.3390/nano14221812Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/nano14221812Direct OA link when available
- Concepts
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Artificial neural network, Artificial intelligence, Image (mathematics), Deep learning, Computer science, Contextual image classification, Pattern recognition (psychology), Geology, Computer vision, Remote sensingTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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