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Joint Multi-Layer GAN-Based Design of Tensorial RF Metasurfaces

We present a deep learning approach to design tensorial metasurfaces for radio-frequency (RF) applications, such as communications and radar, that have strict radiation requirements. Such intelligent RF surfaces require significant manual tuning of several constituent meta-atom designs to realize the required phase shift or polarizability. In this context, meta-atoms that comprise multiple layers are considerably more challenging but have the advantage of implementing complex, tensorial electromagnetic (EM) responses. In this paper, to reduce the design time and optimization overhead, we employ a deep convolutional generative adversarial network (DC-GAN) to perform inverse design, i.e. generate single-and multi-layer meta-atom shapes based on the desired radiation pattern. Further, our DCGAN architecture includes a separate, simulator convolutional neural network that allows for quick validation of the response of the synthesized meta-atom shape. We generated spectral responses for training by feeding parametric variations of twelve published meta-atom designs to a full-wave EM simulator. Numerical experiments indicate that meta-atom designs generated by the DC-GAN differ from co-polarization and cross-polarization transmission responses obtained via EM simulators by less than 1 dB.