Scalability Analysis of Programmable Metasurfaces for Beam Steering

Abstract

Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces 5λ×5λ or larger with unit cells of λ/3 and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than θ > 60 o and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning.

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

“…Since the unit cells are not extremely small compared to the wavelength ( ) and phase reflection is the only parameter that is different for each unit cell, the coupling effect between the unit cells is negligible. The accuracy of this approximation is in excellent agreement with full-wave simulations [ 33 , 36 , 42 ] and has been used in different analyses [ 54 , 55 , 56 ].…”

“…Therefore, we just need to possess reflective features (reflection amplitude and phase) of the unit cell to estimate the far-field pattern. Analytical models exist for describing and predicting the reflected EM field in some well-defined cases, such as beam steering [ 33 ] and focusing [ 34 ] of planar impinging waves. Still, these models introduce simplifications which can result in limitations in realistic setups and, consequently, reduced precision of results compared to the direct solution from Maxwell’s equations [ 35 ].…”

Flexible 3D Printed Conductive Metamaterial Units for Electromagnetic Applications in Microwaves

“…Metamaterials and their two dimensional analogues, metasurfaces, are artificial materials with purposefully designed subwavelength, periodic elementary units, the meta-atoms, which provide controllable interactions with electromagnetic (EM) waves and enable exotic electromagnetic functions [ 1 , 2 ]. Metamaterials and complex media have been extensively experimentally evaluated and tested for real life applications and devices, such as antennas, sensors, splitters, isolators, modulators, electromagnetic shieldings and energy harvesters, in a large frequency range [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Their properties depend on both the meta-atoms’ architecture, and the constituent materials, which can be dielectrics, metals, semiconductors or 2D materials [ 15 , 16 , 17 , 18 ].…”

Toward Intelligent Metasurfaces: The Progress from Globally Tunable Metasurfaces to Software‐Defined Metasurfaces with an Embedded Network of Controllers

“…[125,126] Note that ultimately addressing the individual components inside tunable chips is usually performed with quantized voltage levels, which, however, can be selected at will depending on the application and can be in principle as many as required for a smooth quasi-continuous coverage of the impedance space. [127]…”