Broadband achromatic metalens design based on machine learning

The determination of the relation between the phase modulation and the geometric parameters of a single meta-atom, is the most important but also time-consuming part in a meta-surface design. Here, by developing a machine learning tool, the design process of a high performance achromatic metalens can be greatly simplified and accelerated. The backpropagation neural network is used to build a library of the phase modulation data with 15753 meta-atoms in less than 1 s. In the experiment, designed metalens has been demonstrated to show a high performance of achromatic focusing and imaging ability in the visible wavelengths from 420 to 640 nm without the polarization dependence.