Automatic inverse design of second-order differential metasurfaces based on reinforcement learning
Year: 2025
Type: article
Abstract: Second-order differential metasurfaces, as two-dimensional artificial nanostructures, hold great potential for various applications in optical computing, image processing, microscopy imaging, etc. They have the capability to perform second-order mathematical operations on incident optical fields. In general, the design of second-order differential optical processing metasurfaces needs to consider various constraints, including incident optical fields, material properties, and application requirements. This task is challenging and time-consuming, typically requiring manual intervention. Therefore, a reinforcement learning-based algorithm is proposed for the automatic design of second-order differential metasurfaces. It can rapidly find the optimal result by leveraging the powerful self-learning ability of reinforcement learning. This paper first introduces the basic principles of designing second-order differential optical processing metasurfaces. Then, it presents a new automatic design method for second-order differential metasurfaces based on reinforcement learning. Finally, a design of a second-order differential metasurface is presented. Simulation and experimental results validate the effectiveness of the proposed method. The proposed method effectively leverages the self-learning capability of reinforcement learning, circumventing challenges related to manual intervention, computational complexity, and time-consuming tasks in conventional methods.
Authors: Zhaohui Li, Shuo Yang, Delian Liu
Institutions: Xi'an University of Science and Technology, Xidian University
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
Awards: G3906044189, G4859598079
OpenAlex ID: W4409653849