Abstract
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.
Highlights
Scattering results from the interaction between light and an object or set of objects
Accurate predictions of an isolated object’s scattered field distribution can be generated using numerical simulations for a given excitation field and is of great importance to a variety of areas related to nanophotonics, including linear and nonlinear responses of nano-particles [1] and nano-antennas [2], [3], metasurfaces [4], [5], biophotonics [6], [7], etc
To further validate the generalizability of the network, an open-source dataset is evaluated and the results show excellent agreement with those obtained from COMSOL Multiphysics [35] simulations
Summary
Scattering results from the interaction between light and an object or set of objects (e.g., nanoparticles). Whose shapes may be described by multiple geometrical parameters generally require sophisticated numerical tools which solve complex matrix systems discretized from differential or integral forms of Maxwell’s equations. Fast simulation of optical responses at the nano scale is highly desired in scenarios that require real-time application such as biosensing [15], [16] and iterative inverse designs of complex optical devices [17], [18], analysis of optical chirality [19]. As a result, creating a surrogate EM solver that is capable of real-time calculations for problems involving geometrical, material, and excitation variables is a challenging task
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