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Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning.

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Title: Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning.
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.)
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  Data: Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning.
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  Data: <searchLink fieldCode="AR" term="%22Abbasi%2C+Muhammad+Inam%22">Abbasi, Muhammad Inam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> inamabbasi@utem.edu.my</i><br /><searchLink fieldCode="AR" term="%22Francis%2C+Moses%22">Francis, Moses</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khan%2C+Sher+Dali%22">Khan, Sher Dali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sulaiman%2C+Noor+Hafizah%22">Sulaiman, Noor Hafizah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dahri%2C+Muhammad+Hashim%22">Dahri, Muhammad Hashim</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ibrahim%2C+Imran+Mohd%22">Ibrahim, Imran Mohd</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shamsan%2C+Zaid+Ahmed%22">Shamsan, Zaid Ahmed</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Binwal%2C+Shikha%22">Binwal, Shikha</searchLink> (AUTHOR)<i> sbinwal@wiley.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 11/3/2025, Vol. 2025, p1-7. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Dielectric+properties%22">Dielectric properties</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Microwaves%22">Microwaves</searchLink><br /><searchLink fieldCode="DE" term="%22Substrates+%28Materials+science%29%22">Substrates (Materials science)</searchLink><br /><searchLink fieldCode="DE" term="%22Electromagnetic+measurements%22">Electromagnetic measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+electromagnetics%22">Computational electromagnetics</searchLink><br /><searchLink fieldCode="DE" term="%22Waveguides%22">Waveguides</searchLink><br /><searchLink fieldCode="DE" term="%22Dielectrics%22">Dielectrics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1155/je/9418810
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 1
    Subjects:
      – SubjectFull: Dielectric properties
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Microwaves
        Type: general
      – SubjectFull: Substrates (Materials science)
        Type: general
      – SubjectFull: Electromagnetic measurements
        Type: general
      – SubjectFull: Computational electromagnetics
        Type: general
      – SubjectFull: Waveguides
        Type: general
      – SubjectFull: Dielectrics
        Type: general
    Titles:
      – TitleFull: Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning.
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            NameFull: Abbasi, Muhammad Inam
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            NameFull: Francis, Moses
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            NameFull: Khan, Sher Dali
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            NameFull: Sulaiman, Noor Hafizah
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            – D: 03
              M: 11
              Text: 11/3/2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 23144904
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              Value: 2025
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            – TitleFull: Journal of Engineering (2314-4912)
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