Advancements in Optical Diffraction Neural Networks

Optical diffraction neural networks (ODNNs) represent a promising advancement in computational optics, with significant potential for applications in image classification, image reconstruction, and biomedical imaging. By using the principles of light diffraction for neural network computations, ODNNs enable low-power, real-time data processing without the need for traditional electronic computing units. This review provides an overview of the foundational concepts behind ODNNs, starting with the principles of artificial neurons and progressing to the specific implementation of optical diffraction in neural network architectures. We examine recent advancements in key components of ODNNs, including optical signal processing, activation functions, and training algorithms. Additionally, we highlight the practical applications of ODNNs in areas such as signal analysis, optical imaging, image processing, and high-dimensional optical communications. This paper concludes with a discussion of the current challenges and future directions for ODNN research, emphasizing the potential for overcoming existing limitations and further expanding their capabilities.