High computational density nanophotonic media for machine learning inference

Zhenyu Zhao, Yichen Pan, Jinlong Xiang, Yujia Zhang, An He, Yaotian Zhao, Youlve Chen, Yu He, Xinyuan Fang (), Yikai Su, Min Gu and Xuhan Guo ()
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Zhenyu Zhao: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Yichen Pan: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Jinlong Xiang: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Yujia Zhang: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
An He: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Yaotian Zhao: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Youlve Chen: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Yu He: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Xinyuan Fang: University of Shanghai for Science and Technology, School of Artificial Intelligence Science and Technology
Yikai Su: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering
Min Gu: University of Shanghai for Science and Technology, School of Artificial Intelligence Science and Technology
Xuhan Guo: Shanghai Jiao Tong University, State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Efficient machine learning inference is essential for the rapid adoption of artificial intelligence (AI) across various domains. On-chip optical computing has emerged as a transformative solution due to its ultra-low power consumption, yet improving computational density remains challenging because of the difficulty of miniaturizing interference-based components. Here, we demonstrate fabrication-constrained scattering optical computing within nanophotonic media, enabled by fabrication-aware inverse design. This yields an ultra-compact optical neural architecture occupying 64 µm²—a three-order reduction compared to conventional optical neural networks. Our prototype achieves 86.7% accuracy on the Iris dataset, closely matching simulations. To further validate scalability, we train a larger 64-input design for optical character recognition using 8×8 handwritten digits, reaching 92.8% test accuracy. These results highlight the potential of nanophotonic media to perform large-scale tasks in ultra-small footprints, paving the way for dense, energy-efficient optical processors for next-generation AI.

Date: 2025
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DOI: 10.1038/s41467-025-65213-0

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