Ruonan Chen;Cedric W. L. Lee;Peng Khiang Tan;Rajbala Solanki;Theng Huat Gan
{"title":"High-Efficiency Metalens Antenna Design Through a ControlNet Diffusion Generation Model","authors":"Ruonan Chen;Cedric W. L. Lee;Peng Khiang Tan;Rajbala Solanki;Theng Huat Gan","doi":"10.1109/LAWP.2024.3522289","DOIUrl":null,"url":null,"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<inline-formula><tex-math>$^{\\circ }$</tex-math></inline-formula> 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 <inline-formula><tex-math>$\\phi =0^\\circ$</tex-math></inline-formula> and <inline-formula><tex-math>$\\phi =90^\\circ$</tex-math></inline-formula> planes.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"24 4","pages":"938-942"},"PeriodicalIF":3.7000,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10815981/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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.