Deep Learning for Inverse Design of Radio-Frequency Circuits

This research introduces a novel, deep-learning-enabled approach for designing complex multi-port radio-frequency and sub-terahertz circuits and electromagnetic structures. The methodology overcomes limitations of traditional design methods by using deep learning models to achieve rapid synthesis of arbitrary-shaped structures with desired radiative and scattering properties. The key innovation lies in using a convolutional neural network (CNN) to predict the spectral response of electromagnetic structures, thereby eliminating the need for time-consuming electromagnetic simulations. Experimental results validate the approach through the design, fabrication, and measurement of various components, including antennas, filters, and a broadband mm-Wave amplifier. The developed method enables a new era of automated design for RF/sub-THz systems, promising enhanced performance and reduced design time.