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.
|Titolo:||Optimal Thicknesses Determination in a Multilayer Structure to Improve the SPP Efficiency for Photovoltaic Devices by an Hybrid FEM – Cascade Neural Network Based Approach|
|Data di pubblicazione:||2014|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|