Nanophotonic particle simulation and inverse design using artificial neural networks

Area of Science:

• Nanophotonics
• Computational electromagnetics
• Artificial intelligence in physics

Background:

• Accurate simulation of light scattering by multilayer nanoparticles is crucial for nanophotonic device design.
• Conventional simulation methods can be computationally intensive and time-consuming.

Purpose of the Study:

• To develop a novel method using artificial neural networks (ANNs) to approximate light scattering by multilayer nanoparticles.
• To demonstrate the efficiency and speed of ANNs for optical simulations.
• To explore the application of trained ANNs in solving nanophotonic inverse design problems.

Main Methods:

• Training artificial neural networks on a limited dataset of light scattering simulations.
• Utilizing the trained neural network for rapid simulation of optical processes.
• Employing backpropagation with analytical gradients for inverse design.

Main Results:

• The ANN accurately approximates light scattering simulations with high precision, even with sparse training data.
• Simulations using the trained ANN are orders of magnitude faster than conventional methods.
• The ANN effectively solves nanophotonic inverse design problems.

Conclusions:

• ANNs offer a highly efficient and accurate approach for simulating light scattering in multilayer nanoparticles.
• This method significantly accelerates optical process simulations and enables advanced inverse design capabilities in nanophotonics.