Scalable multilayer diffractive neural network with all-optical nonlinear activation
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866917989662588928 |
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| author | Dong, Yiying Zhang, Bohan Liang, Ruiqi Jia, Wenhe Chen, Kunpeng Zou, Junye Hu, Futai Liu, Sheng Li, Xiaokai Yang, Yuanmu |
| author_facet | Dong, Yiying Zhang, Bohan Liang, Ruiqi Jia, Wenhe Chen, Kunpeng Zou, Junye Hu, Futai Liu, Sheng Li, Xiaokai Yang, Yuanmu |
| contents | All-optical diffractive neural networks (DNNs) offer a promising alternative to electronics-based neural network processing due to their low latency, high throughput, and inherent spatial parallelism. However, the lack of reconfigurability and nonlinearity limits existing all-optical DNNs to handling only simple tasks. In this study, we present a folded optical system that enables a multilayer reconfigurable DNN using a single spatial light modulator. This platform not only enables dynamic weight reconfiguration for diverse classification challenges but crucially integrates a mirror-coated silicon substrate exhibiting instantaneous \c{hi}(3) nonlinearity. The incorporation of all-optical nonlinear activation yields substantial accuracy improvements across benchmark tasks, with performance gains becoming increasingly significant as both network depth and task complexity escalate. Our system represents a critical advancement toward realizing scalable all-optical neural networks with complex architectures, potentially achieving computational capabilities that rival their electronic counterparts while maintaining photonic advantages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable multilayer diffractive neural network with all-optical nonlinear activation Dong, Yiying Zhang, Bohan Liang, Ruiqi Jia, Wenhe Chen, Kunpeng Zou, Junye Hu, Futai Liu, Sheng Li, Xiaokai Yang, Yuanmu Optics All-optical diffractive neural networks (DNNs) offer a promising alternative to electronics-based neural network processing due to their low latency, high throughput, and inherent spatial parallelism. However, the lack of reconfigurability and nonlinearity limits existing all-optical DNNs to handling only simple tasks. In this study, we present a folded optical system that enables a multilayer reconfigurable DNN using a single spatial light modulator. This platform not only enables dynamic weight reconfiguration for diverse classification challenges but crucially integrates a mirror-coated silicon substrate exhibiting instantaneous \c{hi}(3) nonlinearity. The incorporation of all-optical nonlinear activation yields substantial accuracy improvements across benchmark tasks, with performance gains becoming increasingly significant as both network depth and task complexity escalate. Our system represents a critical advancement toward realizing scalable all-optical neural networks with complex architectures, potentially achieving computational capabilities that rival their electronic counterparts while maintaining photonic advantages. |
| title | Scalable multilayer diffractive neural network with all-optical nonlinear activation |
| topic | Optics |
| url | https://arxiv.org/abs/2504.13518 |