Visible Achromatic Metalens Design Based on Artificial Neural Network

Abstract Metasurfaces, known as ultra‐thin and planar structures, are widely used in optical components with their excellent ability to manipulate the wavefront of the light. The key function of the metasurfaces is the spatial phase modulation, originated from the meta‐atoms. Thus, to find the relation between the phase modulation and the parameters of an individual meta‐atom, including the sizes, shapes, and material's optical properties, is the most important but also time‐consuming part in the metasurface design. Here by developing a backpropagation neural network based machine learning tool, the design process of a high performance achromatic metalens can be greatly simplified and accelerated. A library of the phase modulation data from 15 753 meta‐atoms can be generated in less than 1 s by our backpropagation neural network. In the experiment, it is demonstrated that the designed metalens shows an excellent achromatic focusing and imaging ability in the visible wavelengths from 420 to 640 nm without the polarization dependence.