High-Efficiency Metalens Antenna Design Through a ControlNet Diffusion Generation Model

IF 3.7 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Ruonan Chen;Cedric W. L. Lee;Peng Khiang Tan;Rajbala Solanki;Theng Huat Gan
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引用次数: 0

Abstract

In this letter, we propose a ControlNet stable diffusion-based method for the inverse design of a high efficiency metalens antenna. This marks the first application of a text and image fine-tuning generative deep-learning model in metalens design, offering high design freedom with more diversity and precise control. The design process involves dataset generation, electromagnetic simulation, feature encoding, training, unit cell generation, and metalens design. The generated unit cells demonstrate a phase range of 290$^{\circ }$ and high transmission magnitudes over 0.88, with 66% exceeding 0.95. A three-layer metalens antenna with an f/D ratio of 0.5, formed using the generated unit cells and a feeding horn, achieves a gain of 28.1 dBi and an efficiency of 51.4%, nearing the theoretical limit of 63.7%, and maintains minimal side-lobe levels of −22.3 dB and −22.7 dB in the $\phi =0^\circ$ and $\phi =90^\circ$ planes.
通过控制网络扩散生成模型设计高效金属天线
在本文中,我们提出了一种基于ControlNet稳定扩散的高效超构天线反设计方法。这标志着文本和图像微调生成深度学习模型在超构设计中的首次应用,提供了更高的设计自由度,具有更多的多样性和精确的控制。设计过程包括数据集生成、电磁仿真、特征编码、训练、单元生成和超构设计。所生成的单晶胞具有290$^{\circ}$的相位范围和超过0.88的高透射幅度,其中66%超过0.95。在$\phi =0^\circ$和$\phi =90^\circ$平面上,三层超构天线的f/D比为0.5,增益为28.1 dBi,效率为51.4%,接近63.7%的理论极限,并保持最小副瓣电平为- 22.3 dB和- 22.7 dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
9.50%
发文量
529
审稿时长
1.0 months
期刊介绍: IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.
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