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Micromachines 2016, 7(7), 110; doi:10.3390/mi7070110

A Multithread Nested Neural Network Architecture to Model Surface Plasmon Polaritons Propagation

1
Department of Electrical, Electronics and Informatics Engineering, University of Catania, 95125 Catania, Italy
2
Department of Engineering, University of Roma Tre, 00146 Rome, Italy
3
Department of Mathematics and Informatics, University of Catania, 95125 Catania, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Pei-Cheng Ku and Jaeyoun (Jay) Kim
Received: 29 April 2016 / Revised: 13 June 2016 / Accepted: 22 June 2016 / Published: 30 June 2016
(This article belongs to the Special Issue Micro/Nano Photonic Devices and Systems)
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Abstract

Surface Plasmon Polaritons are collective oscillations of electrons occurring at the interface between a metal and a dielectric. The propagation phenomena in plasmonic nanostructures is not fully understood and the interdependence between propagation and metal thickness requires further investigation. We propose an ad-hoc neural network topology assisting the study of the said propagation when several parameters, such as wavelengths, propagation length and metal thickness are considered. This approach is novel and can be considered a first attempt at fully automating such a numerical computation. For the proposed neural network topology, an advanced training procedure has been devised in order to shun the possibility of accumulating errors. The provided results can be useful, e.g., to improve the efficiency of photocells, for photon harvesting, and for improving the accuracy of models for solid state devices. View Full-Text
Keywords: nanotechnologies; photonics; nanoplasmonics; neural networks; high performance computing nanotechnologies; photonics; nanoplasmonics; neural networks; high performance computing
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Capizzi, G.; Lo Sciuto, G.; Napoli, C.; Tramontana, E. A Multithread Nested Neural Network Architecture to Model Surface Plasmon Polaritons Propagation. Micromachines 2016, 7, 110.

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