Design and optimization of SIW patch antenna for Ku band applications using ANN algorithms

Abstract

Substrate Integrate Waveguides (SIW) present very compatible components to planar technologies since recent years that have has been widely implemented in microwave antennas as a class of effective integrated transmission lines to provide high quality factor capacities and incomparable self-consistent shielding. Artificial Neural Networks (ANN) present ones of the fundamental electromagnetic (EM) design automations through numerical optimizations which become nowadays ubiquitous in various modeling fields such as microwave engineering. Accordingly, this paper provides for the Ku microwave band (12–18 GHz), a new design of a patch antenna based on SIW technology using a tree-dimensional electromagnetic (EM) simulation based on structured supervised learning alternative to neural networks to provide accurate geometric dimensions for the target requirements. The SIW patch antenna is designed to operate in Ku frequency band and resonate at 16.10 GHz. The optimized antenna shows very low return losses of less than −10dB to −19dB for the selective band resulting in good performance. ANN algorithms implemented for the training process present than a reliable tool of estimating the antenna performance to provide precise geometrical dimensions with the specific requirements.