Leveraging multi-order diffraction for optical feature extraction

Optical computing for artificial neural network implementation emerges as an important perspective for AI-assisted vision perception. However, the suitable utilization of optical devices and efficient optical computing models remains a challenge in the establishment of the optical network. In this paper, we leverage the grating diffraction effect of spatial light modulators for multiplexing and achieve an optical feature extraction computational prototype in image perception. Simulations are conducted to verify the established physical model, and real-shot experiments on the MNIST dataset and the UC Merced dataset validate the feasibility of the established scheme. The proposed optical multiplexing method with a lightweight backend processor achieves an accuracy of 93% in 5-class MNIST classification and 82% in UC Merced remote sensing scene classification. The proposed method achieves a compression ratio of 6.27 via feature encoding. It shows promising potential in utilizing the inherent physical properties of light propagation and information multiplexing in optical neural networks.