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Related Experiment Video

Updated: Jun 8, 2025

Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation

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Integrated convolutional kernel based on two-dimensional photonic crystals.

Daxing Li, Kuo Zhang, Xiaoyong Hu

|November 1, 2024

Summary

This summary is machine-generated.

Optical neural networks using photonic crystals enable faster AI processing. This study introduces a reconfigurable optical convolutional kernel for image edge detection, achieving high accuracy on benchmark datasets.

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Area of Science:

Optoelectronics
Artificial Intelligence
Photonics

Background:

Optical neural networks (ONNs) offer low latency and high bandwidth for AI acceleration.
Photonic crystals (PhCs) provide unique photonic bandgap properties for light control in integrated optoelectronics.

Purpose of the Study:

To propose and demonstrate an optical reconfigurable convolutional kernel based on PhCs.
To integrate this kernel into an ONN framework for image processing tasks.

Main Methods:

Construction of a PhC-based weight bank for optical convolutional operations.
Development of an optical convolutional neural network framework incorporating the PhC kernel.

Main Results:

The PhC convolutional kernel successfully performed image edge processing tasks.
Achieved 97.81% blind recognition accuracy on the MNIST dataset.
Achieved 80.31% blind recognition accuracy on the Fashion-MNIST dataset.

Conclusions:

Demonstrated the feasibility of building ONNs using PhCs.
Paved new avenues for optical computing and AI acceleration.