Demonstrating completeness in optical neural computing

Krzysztof Tyszka

doi:10.1038/s41377-025-02123-2

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Demonstrating completeness in optical neural computing

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- Demonstrating completeness in optical neural computing
- Demonstrating completeness in optical neural computing
- Light: Science & Applications 2026年15卷第2期页码：360-362
- Affiliations：
  
  Institute of Experimental Physics, Faculty of Physics, University of Warsaw, ul. Pasteura 5, Warsaw PL-02-093, Poland
- Author bio：
  
  Krzysztof Tyszka (ktyszka@fuw.edu.pl)
- Funds：
- DOI：10.1038/s41377-025-02123-2
  中图分类号：
- 网络首发：2026-01-03，
  
  纸质出版：2026-02-28
- Accepted：
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Krzysztof Tyszka. Demonstrating completeness in optical neural computing[J]. Light: Science & Applications, 2026,15(2):360-362.

Tyszka K. Demonstrating completeness in optical neural computing. Light: Science & Applications, 15, 360-362 (2026).
Krzysztof Tyszka. Demonstrating completeness in optical neural computing[J]. Light: Science & Applications, 2026,15(2):360-362. DOI： 10.1038/s41377-025-02123-2.

Tyszka K. Demonstrating completeness in optical neural computing. Light: Science & Applications, 15, 360-362 (2026). DOI： 10.1038/s41377-025-02123-2.

摘要

Abstract

A silicon photonic deep optical neural network integrating convolutional and fully connected layers with on-chip optoelectronic nonlinear activations operates with partially coherent light to achieve high-speed

energy-efficient

end-to-end inference. This demonstration establishes a functional and scalable platform for evaluating complete optical neural processing

representing another step toward specialised

ultrafast photonic architectures beyond electronics.

关键词

Keywords

references

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