On Unsupervised Artificial Intelligence-Assisted Design of Antennas for High-Performance Planar Devices

Abstract

Designing modern antenna structures is a challenging endeavor. It is laborious and heavily reliant on engineering insight and experience, especially at the initial stages oriented towards the development of a suitable antenna architecture. Due to its interactive nature and hands-on procedures (mainly parametric studies) for validating the suitability of particular geometric setups, typical antenna development requires many weeks and significant involvement of a human expert. The same reasons only allow the designer to try out a very limited number of options in terms of antenna geometry arrangements. Automated topology development and dimension sizing is therefore of high interest, especially from an industry perspective where time-to-market and expert-related expenses are of paramount importance. This paper discusses a novel approach to unsupervised specification-driven design of planar antennas. The presented methodology capitalizes on a flexible and scalable antenna parameterization, which enables the realization of complex geometries while maintaining reasonably small parameter space dimensionality. A customized nature-inspired algorithm is employed to carry out space exploration and identification of a quasi-optimum antenna topology in a global sense. A fast gradient-based procedure is then incorporated to fine-tune antenna dimensions. The design framework works entirely in a black-box fashion with the only input being design specifications, and optional constraints, e.g., concerning the structure size. Numerous illustration case studies demonstrate the capability of the presented technique to generate unconventional antenna topologies of satisfactory performance using reasonable computational budgets, and with no human expert interaction necessary whatsoever.