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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.20106 |
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| _version_ | 1866915467572019200 |
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| author | Zhang, Ye Zhu, Qiao Zhou, Rongkuan Lysak, Tatiana Wang, Chao |
| author_facet | Zhang, Ye Zhu, Qiao Zhou, Rongkuan Lysak, Tatiana Wang, Chao |
| contents | Five-dimensional (5D) optical data storage has emerged as a promising technology for ultra-high-density, long-term data archiving. However, its practical realization is hindered by noise and interference during data readout. In this work, we develop a high-precision mathematical model for multi-layer 5D optical storage, grounded in the Jones matrix framework, to accurately capture polarization transformations induced by stacked birefringent nanostructures. Building on this model, we propose a 20-frame FiLM-conditioned U-Net algorithm to reconstruct birefringence parameters--specifically, slow-axis orientation and retardance magnitude-directly from measured intensity patterns. Trained on both ideal and noisy datasets, the network demonstrates robust reconstruction performance under challenging measurement conditions. Compared with conventional frame-based retrieval approaches, our method achieves over an order-of-magnitude improvement in reconstruction accuracy. The proposed model and algorithm can be readily integrated into existing 5D optical readout systems, offering both a solid theoretical foundation and practical tools for precise data recovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-layer 5D Optical Data Storage: Mathematical Modeling and Deep Learning-Based Reconstruction of Birefringent Parameters Zhang, Ye Zhu, Qiao Zhou, Rongkuan Lysak, Tatiana Wang, Chao Optics Five-dimensional (5D) optical data storage has emerged as a promising technology for ultra-high-density, long-term data archiving. However, its practical realization is hindered by noise and interference during data readout. In this work, we develop a high-precision mathematical model for multi-layer 5D optical storage, grounded in the Jones matrix framework, to accurately capture polarization transformations induced by stacked birefringent nanostructures. Building on this model, we propose a 20-frame FiLM-conditioned U-Net algorithm to reconstruct birefringence parameters--specifically, slow-axis orientation and retardance magnitude-directly from measured intensity patterns. Trained on both ideal and noisy datasets, the network demonstrates robust reconstruction performance under challenging measurement conditions. Compared with conventional frame-based retrieval approaches, our method achieves over an order-of-magnitude improvement in reconstruction accuracy. The proposed model and algorithm can be readily integrated into existing 5D optical readout systems, offering both a solid theoretical foundation and practical tools for precise data recovery. |
| title | Multi-layer 5D Optical Data Storage: Mathematical Modeling and Deep Learning-Based Reconstruction of Birefringent Parameters |
| topic | Optics |
| url | https://arxiv.org/abs/2508.20106 |