Metasurface-based all-optical diffractive convolutional neural networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915904832405504 |
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| author | Liang, Zhijiang Xiang, Chenxuan Xiao, Shuyuan Qiu, Jumin Li, Jie Liu, Qiegen Zou, Chengjun Liu, Tingting |
| author_facet | Liang, Zhijiang Xiang, Chenxuan Xiao, Shuyuan Qiu, Jumin Li, Jie Liu, Qiegen Zou, Chengjun Liu, Tingting |
| contents | The escalating energy demands and parallel-processing bottlenecks of electronic neural networks underscore the need for alternative computing paradigms. Optical neural networks, capitalizing on the inherent parallelism and speed of light propagation, present a compelling solution. Nevertheless, physically realizing convolutional neural network (CNN) components all-optically remains a significant challenge. To this end, we propose a metasurface-based all-optical diffractive convolutional neural network (MAODCNN) for computer vision tasks. This architecture synergistically integrates metasurface-based optical convolutional layers, which perform parallel convolution on the optical field, with cascaded diffractive neural networks acting as all-optical decoders. This co-design facilitates layer-wise feature extraction and optimization directly within the optical domain. Numerical simulations confirm that the fusion of convolutional and diffractive layers markedly enhances classification accuracy, a performance that scales with the number of diffractive layers. The MAODCNN framework establishes a viable foundation for practical all-optical CNNs, paving the way for high-efficiency, low-power optical computing in advanced pattern recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metasurface-based all-optical diffractive convolutional neural networks Liang, Zhijiang Xiang, Chenxuan Xiao, Shuyuan Qiu, Jumin Li, Jie Liu, Qiegen Zou, Chengjun Liu, Tingting Optics Applied Physics The escalating energy demands and parallel-processing bottlenecks of electronic neural networks underscore the need for alternative computing paradigms. Optical neural networks, capitalizing on the inherent parallelism and speed of light propagation, present a compelling solution. Nevertheless, physically realizing convolutional neural network (CNN) components all-optically remains a significant challenge. To this end, we propose a metasurface-based all-optical diffractive convolutional neural network (MAODCNN) for computer vision tasks. This architecture synergistically integrates metasurface-based optical convolutional layers, which perform parallel convolution on the optical field, with cascaded diffractive neural networks acting as all-optical decoders. This co-design facilitates layer-wise feature extraction and optimization directly within the optical domain. Numerical simulations confirm that the fusion of convolutional and diffractive layers markedly enhances classification accuracy, a performance that scales with the number of diffractive layers. The MAODCNN framework establishes a viable foundation for practical all-optical CNNs, paving the way for high-efficiency, low-power optical computing in advanced pattern recognition. |
| title | Metasurface-based all-optical diffractive convolutional neural networks |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2512.05558 |