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A Cascade Neural Network Architecture Investigating Surface Plasmon Polaritons Propagation for Thin Metals in OpenMP

  • Francesco Bonanno
  • Giacomo Capizzi
  • Grazia Lo Sciuto
  • Christian Napoli
  • Giuseppe Pappalardo
  • Emiliano Tramontana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)

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 to strongly reduce the training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.

Keywords

Cascade neural network architectures Surface plasmon polaritons Plasmonics Plasmon structure 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francesco Bonanno
    • 1
  • Giacomo Capizzi
    • 1
  • Grazia Lo Sciuto
    • 2
  • Christian Napoli
    • 3
  • Giuseppe Pappalardo
    • 3
  • Emiliano Tramontana
    • 3
  1. 1.Dpt. of Electric, Electronic and Informatics Eng.University of CataniaItaly
  2. 2.Department of Industrial EngineeringUniversity of CataniaItaly
  3. 3.Department of Mathematics and InformaticsUniversity of CataniaItaly

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