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
Link To Document :
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