Abstract
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.
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The data underlying the bar chart and curve figures in the manuscript are available via Figshare [https://doi.org/10.6084/m9.figshare.30745280] without accession code. All the data needed to evaluate the conclusions of this work are presented in the main text and Supplementary Materials.
Code availability
The deep learning models reported in this work used standard libraries and scripts that are publicly available in PyTorch. The code is available upon request from the corresponding author.
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Acknowledgements
The authors acknowledge the financial support by the National Natural Science Foundation of China (Grants No.61991423, Grants No. 52332006 and Grants No. 1257041442).
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P.F. conceived the research concept, designed the experimental framework, performed numerical simulations, and drafted the manuscript. F.L. carried out the experiments and contributed to manuscript preparation. Y.L. participated in the experimental work. M.C. and Z.Z. contributed to the numerical simulations. Q.Z. participated in discussions and provided constructive suggestions. J.L., R.P., and Z.W. assisted with experimental implementation. J.S., J.Z., and Y.T. supervised the project and served as corresponding authors. All authors discussed the results and reviewed the manuscript.
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Nature Communications thanks Zhongyang Li, who co-reviewed with Shuai Wan, and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.
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Feng, P., Liu, F., Liu, Y. et al. Diffractive magic cube network with super-high capacity enabled by mechanical reconfiguration. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68310-w
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DOI: https://doi.org/10.1038/s41467-026-68310-w
