A knowledge-inherited learning for intelligent metasurface design and assembly

• Published in: Light, science & applications Vol. 12; no. 1; pp. 82 - 11
• Main Authors: Jia, Yuetian, Qian, Chao, Fan, Zhixiang, Cai, Tong, Li, Er-Ping, Chen, Hongsheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.03.2023; Springer Nature B.V; Nature Publishing Group
• Subjects: 639/624/399/1015; 639/624/400/1103; Adaptability; Automation; Deep learning; Lasers; Microwaves; Neural networks; Optical and Electronic Materials; Optical Devices; Optics; Photonics; Physics; Physics and Astronomy; RF and Optical Engineering; Satellite communications
• ISSN: 2047-7538; 2095-5545; 2047-7538
• DOI: 10.1038/s41377-023-01131-4

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the “parent” metasurface and then is freely assembled to construct the “offspring” metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.