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All-optical convolutional neural network based on phase change materials in silicon photonics platform.

Samaneh Amiri¹, Mehdi Miri²

¹Department of Communications and Electronics, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

Scientific Reports

|July 2, 2025

Summary

This study introduces an all-optical convolution neural network using silicon photonics for efficient AI. The novel design simplifies complexity and achieves high accuracy in data classification tasks.

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Area of Science:

Photonics
Artificial Intelligence
Computer Engineering

Background:

Current neural networks often rely on complex electronic components.
Integrated optical elements offer potential for reduced power consumption and increased speed.
Silicon photonics provides a mature platform for optical device fabrication.

Purpose of the Study:

To design and simulate an integrated all-optical convolution neural network (CNN).
To implement convolution, max-pooling, and fully connected layers using silicon photonics.
To reduce overall network complexity and reliance on electro-optical elements.

Main Methods:

Utilized GST-based active waveguides for network layers.
Mitigated ReLU requirement in convolution layers using positive kernel values.
Employed finite-difference time-domain (FDTD) method and coupled mode theory for simulations.
Validated network performance using Python programming.

Main Results:

Achieved 91.90% accuracy in MNIST data classification.
Demonstrated 80% accuracy in signal modulation identification (RML2016.10a dataset).
The all-optical CNN design shows comparable performance to electronic counterparts with reduced complexity.

Conclusions:

The proposed integrated all-optical CNN offers a simplified and efficient design.
Silicon photonics platform enables a fully optical implementation of complex neural network layers.
This approach paves the way for high-performance, low-complexity optical computing.