Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning.
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| Title: | Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning. |
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| Authors: | Abbasi, Muhammad Inam1 (AUTHOR) inamabbasi@utem.edu.my, Francis, Moses1 (AUTHOR), Khan, Sher Dali1 (AUTHOR), Sulaiman, Noor Hafizah2 (AUTHOR), Dahri, Muhammad Hashim3 (AUTHOR), Ibrahim, Imran Mohd1 (AUTHOR), Shamsan, Zaid Ahmed4 (AUTHOR), Binwal, Shikha (AUTHOR) sbinwal@wiley.com |
| Source: | Journal of Engineering (2314-4912). 11/3/2025, Vol. 2025, p1-7. 7p. |
| Subjects: | Dielectric properties, Machine learning, Microwaves, Substrates (Materials science), Electromagnetic measurements, Computational electromagnetics, Waveguides, Dielectrics |
| Abstract: | Dielectric properties of the substrates play an important role in the design and performance characterization of communication components such as antennas, filters, and sensors. Conventionally, dielectric probes are used to measure the properties of the substrate. However, the dielectric probes are very expensive and easily breakable instruments. In this work, a novel method of dielectric substrate prediction has been proposed using S12 measurements with two waveguides, along with the application of machine learning. Extensive data collection is done using multiple simulations of the proposed method in 3D electromagnetic software in the X‐band frequency range (8–12 GHz). The measurements are then conducted by using two waveguides, and the data is compared with the simulation data set, where the decision is made based on the comparison of dielectric properties. For verification of the proposed method, dielectric substrates of FR4 and Rogers 5880 have been used, which demonstrated very close agreement between the measured properties and properties from the data sheet. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Dielectric properties of the substrates play an important role in the design and performance characterization of communication components such as antennas, filters, and sensors. Conventionally, dielectric probes are used to measure the properties of the substrate. However, the dielectric probes are very expensive and easily breakable instruments. In this work, a novel method of dielectric substrate prediction has been proposed using S12 measurements with two waveguides, along with the application of machine learning. Extensive data collection is done using multiple simulations of the proposed method in 3D electromagnetic software in the X‐band frequency range (8–12 GHz). The measurements are then conducted by using two waveguides, and the data is compared with the simulation data set, where the decision is made based on the comparison of dielectric properties. For verification of the proposed method, dielectric substrates of FR4 and Rogers 5880 have been used, which demonstrated very close agreement between the measured properties and properties from the data sheet. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 23144904 |
| DOI: | 10.1155/je/9418810 |
