{"ID":2873708,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05926","arxiv_id":"2509.05926","title":"Meta-training of diffractive meta-neural networks for super-resolution direction of arrival estimation","abstract":"Diffractive neural networks leverage the high-dimensional characteristics of electromagnetic (EM) fields for high-throughput computing. However, the existing architectures face challenges in integrating large-scale multidimensional metasurfaces with precise network training and haven't utilized multidimensional EM field coding scheme for super-resolution sensing. Here, we propose diffractive meta-neural networks (DMNNs) for accurate EM field modulation through metasurfaces, which enable multidimensional multiplexing and coding for multi-task learning and high-throughput super-resolution direction of arrival estimation. DMNN integrates pre-trained mini-metanets to characterize the amplitude and phase responses of meta-atoms across different polarizations and frequencies, with structure parameters inversely designed using the gradient-based meta-training. For wide-field super-resolution angle estimation, the system simultaneously resolves azimuthal and elevational angles through x and y-polarization channels, while the interleaving of frequency-multiplexed angular intervals generates spectral-encoded optical super-oscillations to achieve full-angle high-resolution estimation. Post-processing lightweight electronic neural networks further enhance the performance. Experimental results validate that a three-layer DMNN operating at 27 GHz, 29 GHz, and 31 GHz achieves $\\sim7\\times$ Rayleigh diffraction-limited angular resolution (0.5$^\\circ$), a mean absolute error of 0.048$^\\circ$ for two incoherent targets within a $\\pm 11.5^\\circ$ field of view, and an angular estimation throughput an order of magnitude higher (1917) than that of existing methods. The proposed architecture advances high-dimensional photonic computing systems by utilizing inherent high-parallelism and all-optical coding methods for ultra-high-resolution, high-throughput applications.","short_abstract":"Diffractive neural networks leverage the high-dimensional characteristics of electromagnetic (EM) fields for high-throughput computing. However, the existing architectures face challenges in integrating large-scale multidimensional metasurfaces with precise network training and haven't utilized multidimensional EM fiel...","url_abs":"https://arxiv.org/abs/2509.05926","url_pdf":"https://arxiv.org/pdf/2509.05926v1","authors":"[\"Songtao Yang\",\"Sheng Gao\",\"Chu Wu\",\"Zejia Zhao\",\"Haiou Zhang\",\"Xing Lin\"]","published":"2025-09-07T04:49:51Z","proceeding":"physics.optics","tasks":"[\"physics.optics\",\"cs.AI\",\"physics.app-ph\"]","methods":"[]","has_code":false}