Artificial Neural Network-Based Prediction of the Optical Properties of Spherical Core-Shell Plasmonic Metastructures

The substitution of time- and labor-intensive empirical research as well as slow finite difference time domain (FDTD) simulations with revolutionary techniques such as artificial neural network (ANN)-based predictive modeling is the next trend in the field of nanophotonics. In this work, we demonstrated that neural networks with proper architectures can rapidly predict the far-field optical response of core-shell plasmonic metastructures. The results obtained with artificial neural networks are comparable with FDTD simulations in accuracy but the speed of obtaining them is between 100-1000 times faster than FDTD simulations. Further, we have proven that ANNs does not have problems associated with FDTD simulations such as dependency of the speed of convergence on the size of the structure. The other trend in photonics is the inverse design problem, where the far-field optical response of a spherical core-shell metastructure can be linked to the design parameters such as type of the material(s), core radius, and shell thickness using a neural network. The findings of this paper provide evidence that machine learning (ML) techniques such as artificial neural networks can potentially replace time-consuming finite domain methods in the future.

Automated summary

This study demonstrates that artificial neural networks can rapidly predict the optical response of core-shell plasmonic metastructures, with speeds 100-1000 times faster than traditional FDTD simulations. Furthermore, neural networks address issues related to convergence speed dependent on structure size in FDTD simulations. Additionally, in the field of photonics, neural networks can establish connections between far-field optical responses and design parameters.