Using orbital angular momentum (OAM) in the terahertz (THz) range provides a new degree of freedom for communication and imaging systems. This study presents a compact diffractive optical neural network (DONN) designed to recognize discrete and superposed OAM states at THz frequencies. The network consists of six diffractive layers trained to spatially separate nine OAM modes with topological charges from 1 to 9. Each mode is projected to a distinct position on the output plane, enabling direct recognition of its state. The structure was fabricated through low‐cost 3D printing techniques with high‐impact polystyrene, allowing for scalable and practical implementations. Experimental validation at 0.3 THz demonstrates good fidelity of mode discrimination and mapping. The proposed approach offers a robust and economical pathway for OAM decoding, offering new opportunities for beam manipulation through THz systems based on DONNs.
Jia et al. (Thu,) studied this question.