Multi‐wavelength diffractive optical neural network integrated with 2D photonic crystals for joint optical classification
Yuanyuan Zhang, Kuo Zhang, Pei Hu, Daxing Li, Shuai Feng · Nanophotonics · 2025
Optical neural networks (ONNs) have demonstrated unique advantages in overcoming the limitations of traditional electronic computing through their inherent physical properties, including high parallelism, ultra-wide bandwidth, and low power consumption. As a crucial implementation of ONNs, on-chip diffractive optical neural network (DONN) offers an effective solution for achieving highly integrated and energy-efficient machine learning tasks. Notably, wavelength, as a fundamental degree of freedom in optical field manipulation, exhibits multidimensional multiplexing capabilities that can significantly enhance computational parallelism. However, existing DONNs predominantly operate under single-wavelength mechanisms, limiting the computational throughput. Here, we propose a multi-wavelength visual classification architecture termed PhC-DONN, which integrates two-dimensional photonic crystal (PhC) components with diffractive computing units. The architecture comprises three functional modules: (1) a PhC convolutional layer that enables multi-wavelength feature extraction; (2) a three-stage diffraction layer performing parallel modulation of optical fields; and (3) a PhC nonlinear activation layer implementing wavelength nonlinear computation. The results demonstrate that the PhC-DONN achieves classification accuracies of 99.09 % on the MNIST dataset, 66.41 % on the CIFAR-10 dataset, and 92.25 % on KTH human action recognition. By introducing a wavelength-parallel classification mechanism, the architecture accomplishes multi-channel inference during a single light propagation pass, resulting in a 32-fold enhancement in computational throughput compared to conventional DONNs while improving classification accuracy. This work not only establishes a novel optical classification paradigm for multi-wavelength optical neural network, but also provides a viable pathway towards constructing large-scale photonic intelligence parallel processors.