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We theoretically investigate the plasmonic properties of mid-infraredgraphene-based metamaterials and apply deep learning of a neural network forthe inverse design. These artificial structures have square periodic arrays ofgraphene plasmonic resonators deposited on dielectric thin films. Opticalspectra vary significantly with changes in structural parameters. Our numericalresults are in accordance with previous experiments. Then, the theoreticalapproach is employed to generate data for training and testing deep neuralnetworks. By merging the pre-trained neural network with the inverse network,we implement calculations for inverse design of the graphene-basedmetameterials. We also discuss the limitation of the data-driven approach.

Deep Learning for The Inverse Design of Mid-infrared Graphene Plasmons