Machine-Learning Designs of Anisotropic Digital Coding Metasurfaces

Digital coding representations of meta-atoms make it possible to realize intelligent designs of metasurfaces by means of machine learning algorithms. Here, a machine-learning method to design anisotropic digital coding metasurfaces is proposed, and meta-atoms may require any absolute phase values at different positions and under different polarizations. A deep-learning neural network to predict the vast and complex system is proposed, in which only 70 000 training coding patterns are used to train the network. Another 10 000 randomly chosen coding patterns are employed to validate the neural network, showing an accuracy of 90.05% of phase responses with 2 degrees error in the 360 degrees phase. Using the learned network, the correct coding pattern among 18 billion of billions of choices for the required phase can be readily found in a second, finishing automatic design of anisotropic meta-atoms. Three functional 1-bit anisotropic coding metasurfaces are intelligently achieved by the learned network. It is convenient to realize dual-beam scattering with left-handed circular polarization (LHCP) for one beam while right-handed circular polarization (RHCP) for the others, dual-beam scattering with circular polarization for one beam while linear polarization (LP) for the others, and triple-beam scattering with LHCP and RHCP for two beams while LP for the third one.