Carmine Gianfagna, M. Swaminathan, P. Raj, R. Tummala, Giulio Antonini
{"title":"Enabling antenna design with nano-magnetic materials using machine learning","authors":"Carmine Gianfagna, M. Swaminathan, P. Raj, R. Tummala, Giulio Antonini","doi":"10.1109/NMDC.2015.7439256","DOIUrl":null,"url":null,"abstract":"A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.","PeriodicalId":181412,"journal":{"name":"2015 IEEE Nanotechnology Materials and Devices Conference (NMDC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Nanotechnology Materials and Devices Conference (NMDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NMDC.2015.7439256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.
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