Optimal thicknesses determination in a multilayer structure to improve the SPP efficiency for photovoltaic devices by an hybrid FEM — Cascade Neural Network based approach

F. Bonanno, G. Capizzi, S. Coco, C. Napoli, Antonino Laudani, G. L. Sciuto
{"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","authors":"F. Bonanno, G. Capizzi, S. Coco, C. Napoli, Antonino Laudani, G. L. Sciuto","doi":"10.1109/SPEEDAM.2014.6872103","DOIUrl":null,"url":null,"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.","PeriodicalId":344918,"journal":{"name":"2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPEEDAM.2014.6872103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34

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.
基于混合有限元-级联神经网络方法确定多层结构的最优厚度以提高光伏器件的SPP效率
随着全球能源需求的增长,人们对光伏(pv)设备或太阳能电池发电的兴趣越来越大。文献中采用解析和数值方法研究表面等离子激元(SPP)的传播,但这些方法无法确定多层结构中最优传播的最佳厚度。本文采用基于级联神经网络(NN)的新方法预测多层等离子体结构的传播特性,并对所涉及的电磁场进行耦合有限元分析。训练后的神经网络能够在多层结构中不同的厚度值下提供所需的SPP传播的最优值,并且具有良好的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信