Extreme Beam-Forming With Impedance Metasurfaces Featuring Embedded Sources and Auxiliary Surface Wave Optimization

Xu, Gengyu; Ataloglou, Vasileios G.; Hum, Sean V.; Eleftheriades, George V.

doi:10.1109/access.2022.3157291

“…1, we first need to solve the analysis problem, i.e., to predict the scattering of the structure upon some kind of incident illumination. We base our analysis on a set of integral equations similar to [20], with the main difference being that there is no homogenized impedance layer in the present work, but only a ground plane C g and a dielectric region S v . We assume that the incident electromagnetic wave is transverse-electric (TE) polarized for simplicity (E inc = E inc x).…”

Section: B Analysis Through Integral Equationsmentioning

confidence: 99%

“…Then, the far-field electric field E f f (θ) and the radiation intensity U (θ) at different angles θ can be computed as a superposition of the radiation from all the induced currents and from the incident source. The analytic expressions for this step are omitted, but they can be found in [20].…”

Section: B Analysis Through Integral Equationsmentioning

confidence: 99%

“…Lately, a number of works have been presented where the macroscopic design relies on a set of integral equations that accurately predict the scattered fields from the metasurface structure [19][20][21]. By iteratively solving the integral equations through a Method of Moments (MoM) [22], it is possible to optimize one or more homogenized impedance layers to achieve a desired electromagnetic re- * vasilis.ataloglou@mail.utoronto.ca sponse from the metasurface.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of Modulated Dielectric Metasurfaces for Antenna Beamforming

Ataloglou

¹

,

Eleftheriades

²

2022

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)

2

0

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This paper presents an end-to-end design method for the synthesis of dielectric metasurfaces with modulated thickness that are able to realize desired radiation patterns in the far-field with high precision. The method relies on integral equations and the Method of Moments to calculate the induced surface and volumetric current densities throughout the metasurface upon a given incident illumination. By avoiding homogenization of the dielectric layer and calculating directly the scattered fields through the Method of Moments, the near-field and far-field response of the metasurface can be accurately predicted and optimized. In particular, a design example of a modulated dielectric metasurface, fed by a single embedded line source, is presented for the realization of a Chebyshev array pattern with −20 dB sidelobe level. Moreover, an externally-fed dielectric metasurface performing beam splitting of a normally-incident plane wave is designed, 3-D printed and measured, showing two reflected beams at ±30 • , high suppression of specular reflections (> 14 dB) and satisfactory agreement with the simulated and desired radiation patterns. The proposed design method for beamforming with dielectric metasurfaces can find application in higher frequencies where the use of copper may be problematic due to higher ohmic losses and fabrication challenges.

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“…In contrast to the two previous approaches, Ataloglou et al explicitly invoke auxiliary fields (AFs) to realize Taylor [7] and uniform [8] aperture distributions. More recently, this method was used to optimize a MoM-based meta-wire structure with an integrated feed for far-field beam forming and MIMO applications [9]. This approach optimizes the AFs by attempting to satisfy a fully specified desired aperture field distribution.…”

Section: Introductionmentioning

confidence: 99%

Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning

Pearson¹,

Naseri²,

Hum³

2022

Preprint

0

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Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-tomany mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.

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“…In contrast to the two previous approaches, Ataloglou et al explicitly invoke auxiliary fields (AFs) to realize Taylor [7] and uniform [8] aperture distributions. More recently, this method was used to optimize a MoM-based 7λ meta-wire structure at 10 GHz with an integrated feed for far-field beam forming with roughly 90% aperture efficiency and MIMO applications [9]. This approach optimizes the AFs by attempting to satisfy a fully specified desired aperture field distribution.…”

Section: Introductionmentioning

confidence: 99%

A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning

Pearson

¹

,

Naseri

²

,

Hum

³

2022

IEEE Open J. Antennas Propag.

Self Cite

5

0

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Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-tomany mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.

show abstract

Extreme Beam-Forming With Impedance Metasurfaces Featuring Embedded Sources and Auxiliary Surface Wave Optimization

Cited by 35 publications

References 70 publications

Design of Modulated Dielectric Metasurfaces for Antenna Beamforming

Design of Modulated Dielectric Metasurfaces for Antenna Beamforming

Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning

A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning

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