Enabling Intelligent Metasurfaces for Semi-known Input

• Published in: Electromagnetic waves (Cambridge, Mass.) Mass.), 2023-11, Vol.178, p.83-91
• Main Authors: Lin, Pujing, Qian, Chao, Zhang, Jie, Chen, Jieting, Zhu, Xiaoyue, Wang, Zhedong, Huangfu, Jiangtao, Chen, And Hongsheng
• Format: Article
• Language: English
• Subjects: Electromagnetism
• ISSN: 1559-8985; 1070-4698; 1559-8985
• DOI: 10.2528/PIER23090201

Compelling evidence suggests that the interaction between electromagnetic metasurfaces and deep learning gives rise to the proliferation of intelligent metasurfaces in the past decade. In general, deep learning offers a transformative force to reform the design and working style of metasurfaces. Most of the inverse-design literature announces that, given a user-defined input, pre-trained deep learning models can quickly output the metasurface candidates with high fidelity. However, they largely ignore an important fact, that is, the practical input is always semi-known. In this work, we introduce a generation-elimination network that is robust to semi-known input and information pollution. The network is composed of a generative network to generate a number of possible answers and then a discriminative network to eliminate suboptimal answers. We benchmark the feasibility via two scenes, the on-demand metasurface design of the reflection spectra and the far-field pattern. In the microwave experiment, we fabricated and measured the reconfigurable metasurfaces to automatically meet the semi-known beam steering requirement that widely exist in wireless communication. Our work for the first time answers the question of how to cope with semi-known input, which is ubiquitous in a panoply of real- world applications, such as imaging, sensing, and communication across noisy environment.