Metasurface-based all-optical diffractive convolutional neural networks

TL;DR

This paper introduces a metasurface-based all-optical convolutional neural network architecture that leverages diffractive optical components for high-speed, low-power image classification, demonstrating improved accuracy through numerical simulations.

Contribution

It presents the first co-designed metasurface-based optical CNN architecture combining convolutional layers with diffractive neural networks for enhanced optical pattern recognition.

Findings

Numerical simulations show improved classification accuracy with more diffractive layers.

The architecture enables layer-wise feature extraction directly in the optical domain.

Abstract

The escalating energy demands and parallel-processing bottlenecks of electronic neural networks underscore the need for alternative computing paradigms. Optical neural networks, capitalizing on the inherent parallelism and speed of light propagation, present a compelling solution. Nevertheless, physically realizing convolutional neural network (CNN) components all-optically remains a significant challenge. To this end, we propose a metasurface-based all-optical diffractive convolutional neural network (MAODCNN) for computer vision tasks. This architecture synergistically integrates metasurface-based optical convolutional layers, which perform parallel convolution on the optical field, with cascaded diffractive neural networks acting as all-optical decoders. This co-design facilitates layer-wise feature extraction and optimization directly within the optical domain. Numerical simulations confirm that the fusion of convolutional and diffractive layers markedly enhances classification accuracy, a performance that scales with the number of diffractive layers. The MAODCNN framework establishes a viable foundation for practical all-optical CNNs, paving the way for high-efficiency, low-power optical computing in advanced pattern recognition.