{"title":"Accuracy of Dielectric Material Characterization at Millimeter-wave","authors":"Xiaoyou Lin, Boon-Chong Seet, Frances Joseph","doi":"10.1109/APCAP.2018.8538153","DOIUrl":null,"url":null,"abstract":"This paper analyzes the accuracy of characterized dielectric constant by existing two-microstrip-line (L-L) method and a recently proposed hybrid-microstrip-line (hybrid-ML) method at millimeter-wave frequency. The analysis is made on two types of dielectric materials: Rogers RT/d5880 standard high-frequency substrate and plain-woven polyester fabric substrate. The result shows that the hybrid-ML can achieve a good agreement between characterzed and vendor-specified values with less error as compared to the L-L method. Besides, due to the introduced error boxes, the hybrid-ML method also shows less variance in characterized values for both materials over the frequency of interest.","PeriodicalId":198124,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAP.2018.8538153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper analyzes the accuracy of characterized dielectric constant by existing two-microstrip-line (L-L) method and a recently proposed hybrid-microstrip-line (hybrid-ML) method at millimeter-wave frequency. The analysis is made on two types of dielectric materials: Rogers RT/d5880 standard high-frequency substrate and plain-woven polyester fabric substrate. The result shows that the hybrid-ML can achieve a good agreement between characterzed and vendor-specified values with less error as compared to the L-L method. Besides, due to the introduced error boxes, the hybrid-ML method also shows less variance in characterized values for both materials over the frequency of interest.