DocumentCode
1776802
Title
Optimal thicknesses determination in a multilayer structure to improve the SPP efficiency for photovoltaic devices by an hybrid FEM — Cascade Neural Network based approach
Author
Bonanno, F. ; Capizzi, G. ; Coco, S. ; Napoli, Christian ; Laudani, Antonino ; Sciuto, G. Lo
Author_Institution
DIEEI, Univ. of Catania, Catania, Italy
fYear
2014
fDate
18-20 June 2014
Firstpage
355
Lastpage
362
Abstract
As the global energy needs to grow, there is increasing interest in the electricity generation by photovoltaics (PVs) devices or solar cells. Analytical and numerical methods are used in literature to study the propagation of surface plasmon polaritons (SPP) but the optimal thicknesses in a multilayer structure can´t be established for an optimal propagation by these. In this paper a new method based on cascade Neural Network (NN) is used to predict the propagation characteristics of a multilayer plasmonic structure and coupling FEM analysis of the involved electromagnetic field. The trained NNs are able to provide the required optimal values of the SPP propagation with good accuracy at different value of thicknesses in the multilayer structure.
Keywords
finite element analysis; multilayers; neural nets; photovoltaic power systems; polaritons; power engineering computing; surface plasmons; PV devices; SPP propagation; cascade neural network; coupling FEM analysis; electricity generation; electromagnetic field; global energy; multilayer plasmonic structure; optimal thickness determination; photovoltaic devices; propagation characteristics; solar cells; surface plasmon polaritons; Artificial neural networks; Metals; Nonhomogeneous media; Optical surface waves; Photovoltaic systems; Plasmons; Photovoltaics; Surface plasmon polaritons; cascade neural network; finite element analysis (FEM); propagation;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on
Conference_Location
Ischia
Type
conf
DOI
10.1109/SPEEDAM.2014.6872103
Filename
6872103
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