Predicting Scattering From Complex Nano-Structures via Deep Learning - Yongzhong Li; Yinpeng Wang; Shutong Qi; Qiang Ren; Lei Kang; Sawyer D. Campbell; Pingjuan L. Werner; Douglas H. Werner (IEEE Access)
Existing numerical electromagnetic (EM) solvers are usually computationally expensive, time consuming, and memory demanding. Recent advances in deep learning (DL) techniques have demonstrated superior efficiency and provide an alternative pathway for speeding up simulations by serving as effective computational tools. In this paper, we propose a DL framework for real-time predictions of the scattering from an isolated nano-structure in the near-field regime. We find that, to achieve precise approximation of the optical response obtained from numerical simulations, the proposed DL framework only requires a small training data set. The fully trained framework can be three orders of magnitude faster than a conventional EM solver based on the finite difference frequency domain method (FDFD). Furthermore, the proposed DL framework has demonstrated robustness to changes in design variables which govern the nano-structure geometry and material selection as well as properties of the incident wave, shedding light on universal scattering predictions at the nano scale via deep learning techniques. This framework increases the viability of the design and analysis of complex nanostructures, offering great potential for applications pertaining to complex light-matter interaction between electromagnetic fields and nanomaterials.