Diffractive Magic Cube Network with Super-high Capacity Enabled by Mechanical Reconfiguration
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908573982785536 |
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| author | Feng, Peijie Liu, Fubei Liu, Yuanfeng Chong, Mingzhe Zhang, Zongkun Zhao, Qian Sun, Jingbo Zhou, Ji Tan, Yunhua |
| author_facet | Feng, Peijie Liu, Fubei Liu, Yuanfeng Chong, Mingzhe Zhang, Zongkun Zhao, Qian Sun, Jingbo Zhou, Ji Tan, Yunhua |
| contents | Free-space wavefront manipulation devices have emerged as powerful platforms for advanced optical information systems. In response to the challenges posed by the exponential growth of optical information, optical multiplexing and dynamic reconfigurable devices are being actively explored to the enhance system capacity. Among them, coarse-grained mechanically reconfigurable mechanism offers a cost-effective and low-complexity approach for capacity enhancement. However, the channel numbers achieved in current studies are insufficient for practical applications because of inadequate mechanical transformations and suboptimal optimization models. In this article, a diffractive magic cube network (DMCN) is proposed to advance the multiplexing capacity of mechanically reconfigurable system. We utilized the diffractive deep neural network (D2NN) model to jointly optimize the subset of channels generated by the combination of three mechanical operations, permutation, translation, and rotation. The 144-channel holograms, 108-channel single/double focus, 60-channel single/multi-mode OAM beam generation were experimentally demonstrated using diffractive optical elements (DOEs). An equivalent connectivity law was formulated to improve model scalability. Our strategy not only provides a novel paradigm to improve system capacity to super-high level with low crosstalk, but also paves the way for new advancements in optical storage, computing, communication, and photolithography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20693 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffractive Magic Cube Network with Super-high Capacity Enabled by Mechanical Reconfiguration Feng, Peijie Liu, Fubei Liu, Yuanfeng Chong, Mingzhe Zhang, Zongkun Zhao, Qian Sun, Jingbo Zhou, Ji Tan, Yunhua Optics Applied Physics Data Analysis, Statistics and Probability Free-space wavefront manipulation devices have emerged as powerful platforms for advanced optical information systems. In response to the challenges posed by the exponential growth of optical information, optical multiplexing and dynamic reconfigurable devices are being actively explored to the enhance system capacity. Among them, coarse-grained mechanically reconfigurable mechanism offers a cost-effective and low-complexity approach for capacity enhancement. However, the channel numbers achieved in current studies are insufficient for practical applications because of inadequate mechanical transformations and suboptimal optimization models. In this article, a diffractive magic cube network (DMCN) is proposed to advance the multiplexing capacity of mechanically reconfigurable system. We utilized the diffractive deep neural network (D2NN) model to jointly optimize the subset of channels generated by the combination of three mechanical operations, permutation, translation, and rotation. The 144-channel holograms, 108-channel single/double focus, 60-channel single/multi-mode OAM beam generation were experimentally demonstrated using diffractive optical elements (DOEs). An equivalent connectivity law was formulated to improve model scalability. Our strategy not only provides a novel paradigm to improve system capacity to super-high level with low crosstalk, but also paves the way for new advancements in optical storage, computing, communication, and photolithography. |
| title | Diffractive Magic Cube Network with Super-high Capacity Enabled by Mechanical Reconfiguration |
| topic | Optics Applied Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2412.20693 |