A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP
Neural and Evolutionary Computing
2014-06-13 v1 Mesoscale and Nanoscale Physics
Materials Science
Distributed, Parallel, and Cluster Computing
Machine Learning
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.
Keywords
Cite
@article{arxiv.1406.3149,
title = {A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP},
author = {Francesco Bonanno and Giacomo Capizzi and Grazia Lo Sciuto and Christian Napoli and Giuseppe Pappalardo and Emiliano Tramontana},
journal= {arXiv preprint arXiv:1406.3149},
year = {2014}
}