Coaxial-to-Waveguide Adapter Using Machine Learning Approach

Coaxial-to-waveguide transitions are essential elements in microwave systems, allowing efficient signal transfer between coaxial cables and rectangular waveguides. Conventional design methodologies significantly depend on full-wave simulations, often making them time-intensive and demanding on computational resources. This paper introduces a machine learning (ML) approach for designing wideband right-angle coaxial-to-waveguide transitions. A dataset created through CST simulations is utilized to train a regression model that predicts the ideal geometry parameters for minimizing return loss. This method is validated with a manufactured WR284 adapter, which demonstrates a strong correlation with simulation outcomes and highlights the capability of ML to enhance the design process of passive microwave components.