Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

TL;DR

This paper introduces a neural network-based method for spectrum prediction, inverse design, and optimization of plasmonic waveguide systems, demonstrating high accuracy and broad application potential.

Contribution

It presents a novel neural network approach combined with genetic algorithms for efficient spectrum prediction and inverse design of plasmonic waveguides, outperforming previous methods.

Findings

Neural networks accurately predict transmission spectra with small training data.

The method enables high-precision inverse design of plasmonic structures.

Performance metrics of waveguides are optimized effectively.

Abstract

In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyper-parameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by Monte Carlo method, the transmission spectrums predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS which are expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects and so on.