Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5749
• Main Authors: Yu, Yaze, Cao, Yang, Wang, Gong, Pang, Yajun, Lang, Liying
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.06.2023; MDPI
• Subjects: 4f system; Classification; Computer simulation; Computer vision; convolutional neural network; Datasets; diffraction effect; Energy consumption; Fourier transforms; image classification; Image processing; Light; Neural networks; Neurons; Nonlinear optics; Optical communication; Optical materials; Optics; Simulation methods; image classification; diffraction effect; convolutional neural network; 4f system
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23125749

Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a hurdle. In this work, we propose an optical diffractive convolutional neural network (ODCNN) that is capable of performing image processing tasks in computer vision at the speed of light. We explore the application of the 4 system and the diffractive deep neural network (D2NN) in neural networks. ODCNN is then simulated by combining the 4 system as an optical convolutional layer and the diffractive networks. We also examine the potential impact of nonlinear optical materials on this network. Numerical simulation results show that the addition of convolutional layers and nonlinear functions improves the classification accuracy of the network. We believe that the proposed ODCNN model can be the basic architecture for building optical convolutional networks.