The terahertz range possesses significant untapped potential for applications including high-volume wireless communications, noninvasive medical imaging, sensing, and safe security screening However, due to the unique characteristics and constraints of terahertz waves, the vast majority of these applications are entirely dependent upon the availability of beam control techniques Thus, the development of advanced terahertz-range beam control techniques yields a range of useful and unparalleled applications This article provides an overview and tutorial on terahertz beam control The underlying principles of wavefront engineering include array antenna theory and diffraction optics, which are drawn from the neighboring microwave and optical regimes, respectively As both principles are applicable across the electromagnetic spectrum, they are reconciled in this overview This provides a useful foundation for investigations into beam control in the terahertz range, which lies between microwaves and infrared

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Tutorial: Terahertz beamforming, from concepts to realizations

Investigations on conformal phased array pattern synthesis using a novel hybrid evolutionary algorithm are presented. First, in order to overcome the drawbacks of the standard genetic algorithm (GA) and the particle swarm optimization (PSO), an improved genetic algorithm (IGA) and an improved particle swarm optimization (IPSO) algorithm are proposed by introducing novel mechanisms. Then, inspired by the idea of grafting in botany, a hybrid algorithm called HIGAPSO is proposed, which combines IGA and IPSO to take advantages of both methods. After that, a spherical array antenna using wide-band stacked patch antenna elements is selected as a synthesis example to illustrate the power of HIGAPSO in solving realistic optimization problems. Finally, HIGAPSO is used to optimize the amplitude of the element current excitation of the spherical conformal array. Experimental results show that the hybrid algorithm is superior to GAs and PSOs when applied to both the classical test function and the practical problem of conformal antenna array synthesis.

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A Hybrid Optimization Algorithm and Its Application for Conformal Array Pattern Synthesis

In this paper an improved particle swarm optimization algorithm (IPSO) for electromagnetic applications is proposed. In order to overcome the drawbacks of standard PSO, some improved mechanisms for velocity updating, the exceeding boundary control, global best perturbation and the simplified quadratic interpolation (SQI) operator are adopted. To show the effectiveness of the proposed algorithm, a selected set of numerical examples, concerned with linear as well as planar array, is presented. Simulation results show that the refined pinpointing search ability and the global search ability of the proposed algorithm are significantly improved when compared to the particle swarm optimization (PSO) and Genetic Algorithm (GA).

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An Improved Particle Swarm Optimization Algorithm for Pattern Synthesis of Phased Arrays

In order to obtain a wide-angle scanning and low sidelobe level (SLL) microstrip phased array with a finite metal ground, a novel microstrip phased array based on microstrip magnetic dipole is presented in this communication. Microstrip magnetic dipoles are employed as the driven elements in the phased array. Meanwhile, coupling patches are embedded between the adjacent driven elements, and coupling energy is transferred between driven elements by the coupling patches. Strong coupling has been constructed between elements as the driven element spacing is only about ${0}.\text{35}\; {\lambda}$ . With the influence of adjacent elements, the 3-dB beamwidth (BW) of each active element can reach over $\pm \text{80}^\circ $ in the elevation plane. From simulation and measurement, the main lobe scanning ranges of an 8-element and a 16-element phased arrays can both extend over $\pm \text{80}^\circ $ in the elevation plane with a gain fluctuation less than 3 dB. Furthermore, in order to keep the SLL low in the scanning, especially at the low elevation angles, genetic algorithm (GA) has been used, and the SLL has been decreased to the value of $- \text{9}\;\text{dB}$ in the full scanning range of $\pm \text{77}^\circ $ for the eight-element array.

A Wide-Angle Scanning and Low Sidelobe Level Microstrip Phased Array Based on Genetic Algorithm Optimization

This paper covers our recent work on terahertz reflectarray antennas, providing a broad, critical perspective, and contrasting different approaches. The reflectarray antenna is a well-established device that offers significant control and freedom over the directionality and characteristics of its radiation pattern. Such a capability is critical to the successful development of commercially viable terahertz technologies. In this paper, the design, fabrication, and experimental characterization of four terahertz reflectarray devices is presented, based on two different classes of terahertz resonator. The first class is the metallic resonator, and three such reflectarray devices are presented, with each offering its own particular birefringent behavior. The second class is the dielectric resonator, which promises higher efficiency than the metallic resonator, and one such reflectarray device is presented. Devices such as these provide significant design freedom for defining particular beam-shaping operations for diverse application requirements. It is hoped that, with future advances in terahertz resonator technology, reflectarray antennas will prove instrumental in facilitating numerous promising applications of the terahertz range, including high-volume communications, noninvasive medical imaging, and security screening.

Terahertz Reflectarrays and Nonuniform Metasurfaces

A parallel genetic algorithm (GA) optimization tool has been developed for the synthesis of arbitrarily shaped beam coverage using planar 2D phased-array antennas. Typically, the synthesis of a contoured beam footprint using a planar 2D array is difficult because of the inherently large number of degrees of freedom involved (in general, the amplitude and phase of each element must be determined). We make use of a parallel GA tool in this study to compensate for this aspect of the design problem. The algorithm essentially compares a desired pattern envelope with that of trial arrays, and quantifies the effectiveness or desirability of each test case via a fitness function. The GA uses this information to rank and select subsequent arrays over a given number of generations via the conventional stochastic operators, i.e., selection, crossover, and mutation. Each fitness evaluation of a trial pattern is done on a node of the aerospace fellowship cluster supercomputer, which increases the speed of the algorithm linearly with the number of nodes. Because of the continuous nature of the parameters for this optimization problem, a real parameter encoding scheme is employed for the GA chromosome in order to avoid the quantization errors associated with a binary representation. A benchmark 10 times 10 (100) element array is employed, and various results of optimized coverage patterns are shown herein to illustrate the effectiveness and validity of the technique.

F.J. Villegas

Papers

Parallel Genetic-Algorithm Optimization of Shaped Beam Coverage Areas Using Planar 2-D Phased Arrays