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Deep-Learning-Driven Ultra-Broadband X-Band Reflectarray Antenna via Physics-Guided Synthesis.

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Title: Deep-Learning-Driven Ultra-Broadband X-Band Reflectarray Antenna via Physics-Guided Synthesis.
Authors: Zakeri, Mohammadjavad1 mohammadjavad.zakeri@ucf.edu, Sadeghi, Sajjad2
Source: Progress in Electromagnetics Research C. 2025, Vol. 159, p273-280. 8p.
Subjects: Deep learning, Reflectarray antennas, Millimeter waves, Antenna design, Structural optimization, Ultra-wideband devices
Abstract: We present an eight-page in-depth study of a single-layer broadband reflectarray antenna operating over the 8 GHz-12 GHz X-band. At its core is a dual-ring hex-slit (DRHS) unit cell whose two hybridized slot modes yield a continuous ~ 530° monotonic phase traverse across 8-12 GHz with low dispersion and loss, enabling ultra-wideband operation without multilayers. The array employs a dual-ring hex-slit unit cell and a physics-informed deep learning (DL) surrogate model that reduces the geometry optimization time by x120 compared to brute force sweeps. The 30 cm x 30 cm prototype comprises 273 passive elements, delivers a 530° reflection-phase span, 27 dB peak gain, 56% aperture efficiency, and 34.6 dB cross-polar discrimination. A residual network trained in 5000 HFSS datapoints predicts reflection phase with 0.9° mean absolute error (MAE), whereas its inverse sister outputs the element radii in under 10 ms. Full-wave CST simulations and a preliminary measurement of the S parameter corroborate the synthesis accuracy to within 0.25 dB. Comprehensive parametric, angular stability, and computational analyses provide guidance for extending DL-assisted reflectarrays to higher frequencies and reconfigurable architectures. [ABSTRACT FROM AUTHOR]
Copyright of Progress in Electromagnetics Research C is the property of Electromagnetics Academy 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: Deep-Learning-Driven Ultra-Broadband X-Band Reflectarray Antenna via Physics-Guided Synthesis.
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  Data: <searchLink fieldCode="JN" term="%22Progress+in+Electromagnetics+Research+C%22">Progress in Electromagnetics Research C</searchLink>. 2025, Vol. 159, p273-280. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Reflectarray+antennas%22">Reflectarray antennas</searchLink><br /><searchLink fieldCode="DE" term="%22Millimeter+waves%22">Millimeter waves</searchLink><br /><searchLink fieldCode="DE" term="%22Antenna+design%22">Antenna design</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Ultra-wideband+devices%22">Ultra-wideband devices</searchLink>
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  Label: Abstract
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  Data: We present an eight-page in-depth study of a single-layer broadband reflectarray antenna operating over the 8 GHz-12 GHz X-band. At its core is a dual-ring hex-slit (DRHS) unit cell whose two hybridized slot modes yield a continuous ~ 530° monotonic phase traverse across 8-12 GHz with low dispersion and loss, enabling ultra-wideband operation without multilayers. The array employs a dual-ring hex-slit unit cell and a physics-informed deep learning (DL) surrogate model that reduces the geometry optimization time by x120 compared to brute force sweeps. The 30 cm x 30 cm prototype comprises 273 passive elements, delivers a 530° reflection-phase span, 27 dB peak gain, 56% aperture efficiency, and 34.6 dB cross-polar discrimination. A residual network trained in 5000 HFSS datapoints predicts reflection phase with 0.9° mean absolute error (MAE), whereas its inverse sister outputs the element radii in under 10 ms. Full-wave CST simulations and a preliminary measurement of the S parameter corroborate the synthesis accuracy to within 0.25 dB. Comprehensive parametric, angular stability, and computational analyses provide guidance for extending DL-assisted reflectarrays to higher frequencies and reconfigurable architectures. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Progress in Electromagnetics Research C is the property of Electromagnetics Academy 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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      – Type: doi
        Value: 10.2528/PIERC25071302
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 273
    Subjects:
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Reflectarray antennas
        Type: general
      – SubjectFull: Millimeter waves
        Type: general
      – SubjectFull: Antenna design
        Type: general
      – SubjectFull: Structural optimization
        Type: general
      – SubjectFull: Ultra-wideband devices
        Type: general
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      – TitleFull: Deep-Learning-Driven Ultra-Broadband X-Band Reflectarray Antenna via Physics-Guided Synthesis.
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            NameFull: Sadeghi, Sajjad
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            – D: 01
              M: 09
              Text: 2025
              Type: published
              Y: 2025
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              Value: 159
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            – TitleFull: Progress in Electromagnetics Research C
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