Volume 13, Issue 3 (9-2021)                   itrc 2021, 13(3): 1-11 | Back to browse issues page

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Kazemi K, Moradi G. Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics. itrc 2021; 13 (3) :1-11
URL: http://journal.itrc.ac.ir/article-1-485-en.html
Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics
Kianoosh Kazemi1 , Gholamreza Moradi 2
1- Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
2- Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran , ghmoradi@aut.ac.ir
Abstract:   (2239 Views)
This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (4.54 GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better quality factor. Several techniques are used to enhance sensitivity, including a Photonic Band Gap (PBG), corner cut, and slow-wave vias. These techniques increase the interaction between the material under test and the electric field. By utilizing slow-wave vias, 35% size reduction is achieved. Achieving simultaneous miniaturization and sensitivity enhancement in this study introduces a new possibility and application for sensor design. The values of complex permittivities are extracted from scattering parameters obtained from simulation of the structure in CST Microwave Studio (MWS) using a machine learning approaches. Our sensor has 0.8% sensitivity, which is better than that of other sensors. Moreover, the maximum error rate in our method is lower than other existing methods. This ratio for the proposed method is 2.31% while for curve fitting and analytical solutions are 26% and 16%, respectively.
Keywords: Complex Permittivity, Machine Learning (ML), Photonic Band Gap, Slow-Wave, Substrate Integrated Waveguide (SIW).
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Type of Study: Research | Subject: Communication Technology
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