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High computational density nanophotonic media for machine learning inference.

Zhenyu Zhao¹, Yichen Pan¹, Jinlong Xiang¹

¹State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

Nature Communications

|November 21, 2025

Summary

This summary is machine-generated.

Researchers developed ultra-compact optical neural networks using nanophotonic media for efficient machine learning inference. This breakthrough enables dense, low-power artificial intelligence (AI) processors for next-generation applications.

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Last Updated: Jan 10, 2026

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Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

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Area of Science:

Photonics
Artificial Intelligence
Materials Science

Background:

Efficient machine learning inference is crucial for AI adoption.
On-chip optical computing offers low power consumption but faces miniaturization challenges.
Conventional optical neural networks lack computational density.

Purpose of the Study:

To demonstrate fabrication-constrained scattering optical computing in nanophotonic media.
To achieve ultra-compact optical neural architectures.
To overcome miniaturization limitations in optical computing.

Main Methods:

Utilized fabrication-aware inverse design for nanophotonic media.
Developed scattering optical computing within these media.
Fabricated ultra-compact optical neural architectures (64 µm²).

Main Results:

Achieved 86.7% accuracy on the Iris dataset with the prototype.
Demonstrated scalability with an 8x8 handwritten digit optical character recognition design.
Reached 92.8% test accuracy for optical character recognition.

Conclusions:

Nanophotonic media enable large-scale AI tasks in ultra-small footprints.
The developed approach paves the way for dense, energy-efficient optical processors.
This research advances optical computing for next-generation AI.