Improved-Efficacy EM-Based Antenna Miniaturization by Multi-Fidelity Simulations and Objective Function Adaptation

Abstract

The growing demand for the integration of surface mount design (SMD) antennas into miniaturized electronic devices has imposed increasing limitations on the structure dimensions. Examples include embedded antennas in applications such as on-board devices, picosatellites, 5G communications, or implantable and wearable devices. The demands for size reduction while ensuring a satisfactory level of electrical and field performance can be managed through constrained numerical optimization. The reliability of optimization-based size reduction requires utilization of full-wave electromagnetic (EM) analysis, which entails significant computational costs. This can be alleviated by incorporating surrogate modeling techniques, adjoint sensitivities, or the employment of sparse sensitivity updates. An alternative is the incorporation of multi-fidelity simulation models, normally limited to two levels, low and high resolution. This paper proposes a novel algorithm for accelerated antenna miniaturization, featuring a continuous adjustment of the simulation model fidelity in the course of the optimization process. The model resolution is determined by factors related to violation of the design constraints as well as the convergence status of the algorithm. The algorithm utilizes the lowest-fidelity model for the early stages of the optimization process; it is gradually refined towards the highest-fidelity model upon approaching convergence, and the constraint violations improve towards the preset tolerance threshold. At the same time, a penalty function approach with adaptively adjusted coefficients is applied to enable the precise control of constraints, and to increase the achievable miniaturization rates. The presented procedure has been validated using five microstrip antennas, including three broadband, and two circularly polarized structures. The obtained results corroborate the relevance of the implemented mechanisms from the point of view of improving the average computational efficiency of the optimization process by 43% as compared to the single-fidelity adaptive penalty function approach. Furthermore, the presented methodology demonstrates a performance that is equivalent or even superior to its single-fidelity counterpart in terms of an average constraint violation of 0.01 dB (compared to 0.03 dB for the reference), and an average size reduction of 25% as compared to 25.6%.

Optimization-Based High-Frequency Circuit Miniaturization through Implicit and Explicit Constraint Handling: Recent Advances

“…As for the other EM-driven design procedures, another practical issue is the high computational cost of the optimization process. In [46], an accelerated size reduction procedure has been proposed, which employs variable fidelity simulation models. It is briefly recalled here and illustrated using an example of a circularly polarized antenna.…”

“…The model management scheme of [46] adjusts the fidelity of the model, denoted as F, within the range F min ≤ F ≤ F max (cf. Section 6.1).…”

“…Regardless of the constraint handling approach (in particular, implicit or explicit), optimization-based miniaturization tends to remain a highly expensive task in terms of both computational time and resources. The employment of variable-fidelity EM models [46] has proved to be efficient in reducing the aforementioned costs. The operation of the scheme considered in [46] exploits meticulously defined performance indicators including the quality of the constraint satisfaction, as well as the convergence status of the algorithm.…”

“…The employment of variable-fidelity EM models [46] has proved to be efficient in reducing the aforementioned costs. The operation of the scheme considered in [46] exploits meticulously defined performance indicators including the quality of the constraint satisfaction, as well as the convergence status of the algorithm. This allows for the precise control of the constraints as well as obtaining satisfactory miniaturization rates at a reduced cost.…”

Globalized simulation-driven miniaturization of microwave circuits by means of dimensionality-reduced constrained surrogates

“…However, performance of the optimization process turns out to be contingent upon the appropriate choice of penalty coefficients 78 , which are normally selected by trial and error. This gave rise to adaptive penalty coefficient strategies 79 , 80 . Recently, explicit constraint handling methods have been proposed 81 , along with the techniques for customized treatment of equality constraints, based on correction procedures 82 .…”