Antennas on-chip are a particular type of radiating elements valued for their small footprint. They are most commonly integrated in circuit boards to electromagnetically interface free space, which is necessary for wireless communications. Antennas on-chip radiate and receive electromagnetic (EM) energy as any conventional antennas, but what distinguishes them is their miniaturized size. This means they can be integrated inside electronic devices. Although on-chip antennas have a limited range, they are suitable for cell phones, tablet computers, headsets, global positioning system (GPS) devices, and WiFi and WLAN routers. Typically, on-chip antennas are handicapped by narrow bandwidth (less than 10%) and low radiation efficiency. This survey provides an overview of recent techniques and technologies investigated in the literature, to implement high performance on-chip antennas for millimeter-waves (mmWave) and terahertz (THz) integrated-circuit (IC) applications. The technologies discussed here include metamaterial (MTM), metasurface (MTS), and substrate integrated waveguides (SIW). The antenna designs described here are implemented on various substrate layers such as Silicon, Graphene, Polyimide, and GaAs to facilitate integration on ICs. Some of the antennas described here employ innovative excitation mechanisms, for example comprising open-circuited microstrip-line that is electromagnetically coupled to radiating elements through narrow dielectric slots. This excitation mechanism is shown to suppress surface wave propagation and reduce substrate loss. Other techniques described like SIW are shown to significantly attenuate surface waves and minimise loss. Radiation elements based on the MTM and MTS inspired technologies are shown to extend the effective aperture of the antenna without compromising the antenna’s form factor. Moreover, the on-chip antennas designed using the above technologies exhibit significantly improved impedance match, bandwidth, gain and radiation efficiency compared to previously used technologies. These features make such antennas a prime candidate for mmWave and THz on-chip integration. This review provides a thorough reference source for specialist antenna designers.

Utilization of electromagnetic (EM) simulation tools is mandatory in the design of contemporary antenna structures. At the same time, conducting design procedures that require multiple evaluations of the antenna at hand, such as parametric optimization or yield-driven design, is hindered due to the high cost of accurate EM analysis. To a certain extent, this issue can be addressed using fast replacement models (also referred to as surrogates). Unfortunately, due to curse of dimensionality, traditional data-driven surrogate modeling methods are limited to antenna structures described by a few parameters with relatively narrow parameter ranges. This is by no means sufficient given the complexity of modern designs. In this paper, a novel technique for surrogate modeling of antenna structures is proposed. It involves a construction of two levels of surrogates, both realized as kriging interpolation models. The first model is based on a set of reference designs optimized for selected performance figures. It is used to establish a domain for the final (second level) surrogate. This formulation permits efficient modeling within wide ranges of antenna geometry parameters and wide ranges of performance figures (e.g., operating frequencies). At the same time, it allows uniform allocation of training data samples in a straightforward manner. Our approach is demonstrated using two microstrip antenna examples and is compared with conventional kriging and radial basis function modeling. Application examples for antenna optimization are also provided along with experimental validation.

Design of modern antennas relies—for reliability reasons—on full-wave electromagnetic simulation tools. In addition, increasingly stringent specifications pertaining to electrical and field performance, growing complexity of antenna topologies, along with the necessity for handling multiple objectives, make numerical optimization of antenna geometry parameters a highly recommended design procedure. Conventional algorithms, particularly global ones, entail often-unmanageable computational costs, so alternative approaches are needed. This work proposes a novel method for cost-efficient globalized design optimization of multi-band antennas incorporating the response feature technology into the trustregion framework. It allows for unequivocal allocation of the antenna resonances even for poor initial designs, where conventional local algorithms fail. Furthermore, the algorithm is accelerated by means of Jacobian variability tracking, which reduces the number of expensive finite-differentiation updates. Two real-world antenna design cases are used for demonstration purposes. The optimization cost is comparable to that of local routines while ensuring nearly global search capabilities.

Utilization of fast surrogate models has become a viable alternative to direct handling of fullwave electromagnetic (EM) simulations in EM-driven design. Their purpose is to alleviate the difficulties related to high computational cost of multiple simulations required by the common numerical procedures such as parametric optimization or uncertainty quantification. Yet, conventional data-driven (or approximation) modeling techniques are severely affected by the curse of dimensionality. This is a serious limitation when it comes to modeling of highly nonlinear antenna characteristics. In practice, general-purpose surrogates can be rendered for the structures described by a few parameters within limited ranges thereof, which is grossly insufficient from the utility point of view. This paper proposes a novel modeling approach involving variable-fidelity EM simulations incorporated into the recently reported nested kriging modeling framework. Combining the information contained in the densely sampled low- and sparsely sampled highfidelity models is realized using co-kriging. The resulting surrogate exhibits the predictive power comparable to the model constructed using exclusively high-fidelity data while offering significantly reduced setup cost. The advantages over conventional surrogates are pronounced even further. The presented modeling procedure is demonstrated using two antenna examples and further validated through the application case studies.

Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow- or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the antenna sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. While necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow- and multi-band antennas. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the antenna yield. Verification results obtained for a ring-slot antenna, a dual-band and a triple-band uniplanar dipoles indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations: 62, 74 and 132 EM simulations, respectively. Result reliability is validated through comparisons with EM-based Monte Carlo simulations.

Electromagnetic (EM) simulation tools are of primary importance in the design of contemporary antennas. The necessity of accurate performance evaluation of complex structures is a reason why the final tuning of antenna dimensions, aimed at improvement of electrical and field characteristics, needs to be based on EM analysis. Design automation is highly desirable and can be achieved by coupling EM solvers with numerical optimisation routines. Unfortunately, its computational overhead may be impractically high for conventional algorithms. This study proposes an efficient gradient search algorithm with numerical derivatives. The acceleration of the optimisation process is obtained by means of the two mechanisms developed to suppress some of finite‐differentiation‐based updates of the antenna response sensitivities that involve monitoring and quantifying the gradient changes as well as design relocation between the consecutive algorithm iterations. Both methods considerably reduce the need for finite differentiation, leading to significant computational savings. At the same time, excellent reliability and repeatability is maintained, which is demonstrated through statistics over multiple algorithm runs with random initial designs. The proposed approach is validated using a benchmark set of wideband antennas. The proposed algorithm is competitive to both the reference trust‐region algorithm as well as its recently reported accelerated versions.