Abstract

The growing demands for integration of surface mount design (SMD) antennas into miniatur-ized electronic devices have been continuously imposing limitations on the structure dimen-sions. Examples include embedded antennas in applications such as on-board devices, picosatel-lites, 5G communications, or implantable and wearable devices. The demands for size reduction while ensuring a satisfactory level of the electrical and field performance figures can be man-aged through constrained numerical optimization. The reliability of optimization-based size reduction requires utilization of full-wave electromagnetic (EM) analysis, which entails signifi-cant computational costs. This can be alleviated by incorporating surrogate modeling tech-niques, 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 the accelerated antenna miniaturiza-tion, featuring a continuous adjustment of the simulation model fidelity in the course of the op-timization process. The model resolution is determined by factors related to violation of the de-sign constraints as well as and 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 a 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 pro-cess by 43% as compared to the single-fidelity adaptive penalty function approach. Fur-thermore, the presented methodology demonstrates a performance quality equivalent or even superior to the single-fidelity counterpart in terms of the average constraint violation of 0.01 dB (compared to 0.03 dB for the reference), and the average size reduction of 25% as compared to 25.6%.

Citations

Authors (2)