Diffractive neural networks for mode-sorting with flexible detection regions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912556608651264 |
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| author | Bearne, Kaden Duplinskiy, Alexander Filipovich, Matthew J. Lvovsky, A. I. |
| author_facet | Bearne, Kaden Duplinskiy, Alexander Filipovich, Matthew J. Lvovsky, A. I. |
| contents | Mode-sorting is a procedure that decomposes a light field into a basis of transverse modes, directing each mode into a separate spatial location, allowing the constituent mode intensities to be measured simultaneously. We demonstrate a mode-sorter based on a diffractive optical neural network and show that it is advantageous to include the output detection regions into the trainable set of parameters of that network. This approach outperforms traditional mode-sorting methods, achieving higher efficiency for the same crosstalk levels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffractive neural networks for mode-sorting with flexible detection regions Bearne, Kaden Duplinskiy, Alexander Filipovich, Matthew J. Lvovsky, A. I. Optics Mode-sorting is a procedure that decomposes a light field into a basis of transverse modes, directing each mode into a separate spatial location, allowing the constituent mode intensities to be measured simultaneously. We demonstrate a mode-sorter based on a diffractive optical neural network and show that it is advantageous to include the output detection regions into the trainable set of parameters of that network. This approach outperforms traditional mode-sorting methods, achieving higher efficiency for the same crosstalk levels. |
| title | Diffractive neural networks for mode-sorting with flexible detection regions |
| topic | Optics |
| url | https://arxiv.org/abs/2508.20058 |