Machine learning based tandem network approach for antenna design

In this paper, we introduce novel machine learning based techniques to design multi-band microstrip antennas as per user specifications over a broad range of frequencies. The approach involves the design and training of a neural network for approximating the electromagnetic simulations of antennas, the so-called 'forward' problem. Here, the antenna is parameterized in terms of a checker-board pattern of metallic sub-patches. Additionally, a second 'tandem' neural network is also designed, which takes the user specification of a desired return-loss spectrum and returns an antenna structure. We explore the various machine learning innovations that are required in order for this approach to succeed. Our approach makes way for rapid designs of multi-band antennas, which is otherwise known to be a tedious task requiring vast domain knowledge.