A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP

TL;DR

This paper introduces a new cascade neural network architecture and training method using OpenMP to efficiently model how metal thickness affects surface plasmon polariton propagation in ultracompact photonic devices.

Contribution

A novel cascade neural network architecture and an OpenMP-based training procedure for modeling SPP propagation dependence on metal thickness.

Findings

The proposed NN effectively models SPP propagation.

OpenMP training reduces computational time.

Experimental results confirm the model's accuracy.

Abstract

Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.