Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size

TL;DR

This paper introduces a deep learning-based surrogate model to efficiently simulate the optical response of large, aperiodic plasmonic metasurfaces, overcoming computational challenges of traditional full-field simulations.

Contribution

It presents a novel deep convolutional neural network approach to accurately model complex plasmonic nanostructure assemblies without relying on periodicity.

Findings

Achieves high accuracy with approximately 1% error

Enables modeling of metasurfaces with thousands of nanostructures

Capable of resolving spectral coupling effects

Abstract

Optical interactions have an important impact on the optical response of nanostructures in complex environments. Accounting for interactions in large ensembles of structures requires computationally demanding numerical calculations. In particular if no periodicity can be exploited, full field simulations can become prohibitively expensive. Here we propose a method for the numerical description of aperiodic assemblies of plasmonic nanostructures. Our approach is based on dressed polarizabilities, which are conventionally very expensive to calculate, a problem which we alleviate using a deep convolutional neural network as surrogate model. We demonstrate that the method offers high accuracy with errors in the order of a percent. In cases where the interactions are predominantly short-range, e.g. for out-of-plane illumination of planar metasurfaces, it can be used to describe aperiodic metasurfaces of basically unlimited size, containing many thousands of unordered plasmonic nanostructures. We furthermore show that the model is capable to spectrally resolve coupling effects. The approach is therefore of highest interest for the field of metasurfaces. It provides significant advantages in applications like homogenization of large aperiodic planar metastructures or the design of sophisticated wavefronts at the micrometer scale, where optical interactions play a crucial role.