Abstract
A metalens antenna with beamscanning performance is designed using a proposed prior-knowledge-guided machine-learning-enabled (PK-ML) synthesis method. The algorithm of conditional deep convolutional generative adversarial network (cDCGAN) is utilized in the proposed method to generate pixelated metacells with high degrees of freedom. Prior knowledge, including well-known fundamental electromagnetic theorems and experience in antenna design, are purposely applied in the proposed method to guide and speed up the synthesis. Triple-layer metacells are generated using the proposed method for the design of the metalens antenna with improved gain bandwidth. The proposed metalens antenna of area of 13.2 × 13.2 wavelengths realize the gain of 29.4 dBi at 12 GHz with 1-dB bandwidth of 13.5%, 52.2% higher than the reference metalens antenna.
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