Multi-wavelength diffractive optical neural network integrated with 2D photonic crystals for joint optical...

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.