Demultiplexing through a multimode fiber using chip-scale diffractive neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915653768708096 |
|---|---|
| author | Zhang, Qian Yu, Haoyi Zhang, Jie Zhang, Yuedi Meng, Chao Sun, Jiali Miao, Yu Zhang, Qiming Gu, Min Czarske, Juergen W |
| author_facet | Zhang, Qian Yu, Haoyi Zhang, Jie Zhang, Yuedi Meng, Chao Sun, Jiali Miao, Yu Zhang, Qiming Gu, Min Czarske, Juergen W |
| contents | In today's information age, advanced fiber optic transmission technology is of paramount importance. Multimode fibers (MMFs) using space-division multiplexing (SDM) are promising for improved transmission capacity, connection flexibility, and security of data. However, the complex transmission characteristics of MMFs significantly hinder precise mode demultiplexing. Conventional approaches, including holographic measurements, phase retrieval algorithms, photonic lanterns, and multiplane light conversion, are limited by system complexity, size, and flexibility. In this paper, we demonstrate for the first time a purely optical, chip-scale AI solution for high-mode isolation, speed-of-light demultiplexing of MMF modes using a three-dimensional diffractive neural network (DNN). The DNN is trained with synthetic modal data and fabricated using two-photon nanolithography. It features a compact size of $120μm \times 120μm \times 80μm$ and a diffractive structure size of $1μm^{2}$ for the neurons at the hidden layers of the network. Experimentally, the DNN demultiplexer achieves a relative demultiplexing accuracy of over 80%. The AI approach of DNN allows for flexible design and overcomes the size and performance limitations of digital-optical demultiplexers. This work paves the way for compact, low-latency optical processors for high-performance demultiplexers and enables scalable, chip-integrated solutions for next-generation fiber optic networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demultiplexing through a multimode fiber using chip-scale diffractive neural networks Zhang, Qian Yu, Haoyi Zhang, Jie Zhang, Yuedi Meng, Chao Sun, Jiali Miao, Yu Zhang, Qiming Gu, Min Czarske, Juergen W Optics In today's information age, advanced fiber optic transmission technology is of paramount importance. Multimode fibers (MMFs) using space-division multiplexing (SDM) are promising for improved transmission capacity, connection flexibility, and security of data. However, the complex transmission characteristics of MMFs significantly hinder precise mode demultiplexing. Conventional approaches, including holographic measurements, phase retrieval algorithms, photonic lanterns, and multiplane light conversion, are limited by system complexity, size, and flexibility. In this paper, we demonstrate for the first time a purely optical, chip-scale AI solution for high-mode isolation, speed-of-light demultiplexing of MMF modes using a three-dimensional diffractive neural network (DNN). The DNN is trained with synthetic modal data and fabricated using two-photon nanolithography. It features a compact size of $120μm \times 120μm \times 80μm$ and a diffractive structure size of $1μm^{2}$ for the neurons at the hidden layers of the network. Experimentally, the DNN demultiplexer achieves a relative demultiplexing accuracy of over 80%. The AI approach of DNN allows for flexible design and overcomes the size and performance limitations of digital-optical demultiplexers. This work paves the way for compact, low-latency optical processors for high-performance demultiplexers and enables scalable, chip-integrated solutions for next-generation fiber optic networks. |
| title | Demultiplexing through a multimode fiber using chip-scale diffractive neural networks |
| topic | Optics |
| url | https://arxiv.org/abs/2512.04767 |