Artificial intelligence solution to extract the dielectric properties of materials at sub-THz frequencies

被引:4
作者
Guneser, Muhammet Tahir [1 ]
机构
[1] Karabuk Univ, Elect Elect Engn Dept, Karabuk, Turkey
关键词
permittivity measurement; acoustic transducers; artificial intelligence; stress-strain relations; nondestructive testing; material characterisation; medical treatment; food industry; material processing; materials density; extraction techniques; material phase; temperature; FREE-SPACE MEASUREMENT; COMPLEX PERMITTIVITY; NEURAL-NETWORK; SILICON-WAFER; CONSTANT; PARAMETERS; PERMEABILITY; OPTIMIZATION; ALGORITHM; MODEL;
D O I
10.1049/iet-smt.2018.5356
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Material characterisation plays a crucial role in many applications such as security, military, communication, bioengineering, medical treatment, food industry and material processing. Since it is useful to identify other properties of materials often tied to other useful parameters, such as stress-strain relation, bio content, moisture content, materials density and so on, the dielectric properties of materials should be achieved with high accuracy using appropriate measurement techniques and extraction techniques. There are many measurement methods to determine the dielectric properties of materials, which depend on parameters such as frequency range, material phase and temperature. In this study, the measurement methods and extraction techniques have been discussed, and alternative ways have been presented with experimental and simulation results. Furthermore, a new numerical extraction technique has been performed to achieve the dielectric properties of materials.
引用
收藏
页码:523 / 528
页数:6
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