Inverse design of multilayer nanoparticles using artificial neural networks and genetic algorithm

TL;DR

This paper introduces a hybrid approach combining neural networks and genetic algorithms for the inverse design of multilayer nanoparticles, enabling efficient and accurate optimization of their optical properties.

Contribution

It presents a novel method that integrates neural networks with genetic algorithms for inverse design, improving efficiency over traditional trial-and-error methods.

Findings

The method effectively predicts optimal multilayer nanoparticle structures.

It demonstrates the extension of the approach to other optical design problems.

The combined approach outperforms traditional methods in accuracy and speed.

Abstract

The light scattering of multilayer nanoparticles can be solved by Maxwell equations. However, it is difficult to solve the inverse design of multilayer nanoparticles by using the traditional trial-and-error method. Here, we present a method for forward simulation and inverse design of multilayer nanoparticles. We combine the global search ability of genetic algorithm with the local search ability of neural network. First, the genetic algorithm is used to find a suitable solution, and then the neural network is used to fine-tune it. Due to the non-unique relationship between physical structures and optical responses, we first train a forward neural network, and then it is applied to the inverse design of multilayer nanoparticles. Not only here, this method can easily be extended to predict and find the best design parameters for other optical structures.