Deep learning modeling approach for metasurfaces with high degrees of freedom
Sensong An, Bowen Zheng, Mikhail Y. Shalaginov, Hong Tang, Hang Li, L. P. Zhou, Jun Ding, Anu Agarwal, Clara Rivero‐Baleine, Myungkoo Kang, Kathleen Richardson, Tian Gu, Juejun Hu, Clayton Fowler, Hualiang Zhang
Abstract
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom's wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface.