Inverse design of reflective metasurface antennas using deep learning from small‐scale statistically random pico‐cells

Abstract A small‐scale data‐selection method is proposed for deep‐learning‐based inverse design of reflective metasurface antennas (MAs). The overall design process involves discretization, coding, data selection, simulation, feature representation, training, prediction, and metasurface design. Unlike conventional method that selects the training data empirically after simulation from a large quantity of unit cells, the proposed statistically random data filtering (SRDF) method selects the training data set randomly before simulation by leveraging the geometric‐coding statistics of the unit cells. The advantages of the proposed data‐selection method include lower simulation cost (due to fewer simulations needed), improved generality, and higher design accuracy simultaneously. The design process starts from the discretization of a unit cell into a grid array of binaurally‐coded pico‐cells. Afterwards, the proposed SRDF method selects 0.01% of all the possible unit cells for simulation. With the simulated results, a deep‐convolutional‐variational‐autoencoder network is trained, leading to an accurate and bidirectional prediction of both the geometry and reflection phase of a unit cell in seconds. The proposed data‐selection method is validated by designing two reflective MAs with the predicted unit cells, each pointing a pencil beam off the boresight by 30° and 60°, respectively. Experiments are in line with full‐wave simulations.