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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	19378718
DOI:	10.2528/PIERC25071302