首页> 外文会议>International Symposium on Power Electronics, Electrical Drives, Automation and Motion >Optimal thicknesses determination in a multilayer structure to improve the SPP efficiency for photovoltaic devices by an hybrid FEM amp;#x2014; Cascade Neural Network based approach
【24h】

Optimal thicknesses determination in a multilayer structure to improve the SPP efficiency for photovoltaic devices by an hybrid FEM amp;#x2014; Cascade Neural Network based approach

机译:多层结构中的最佳厚度测定,通过混合FEM&#x2014提高光伏器件的SPP效率。基于级联神经网络的方法

获取原文

摘要

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.
机译:随着全球能源需要增长,通过光伏(PVS)器件或太阳能电池的发电增加了越来越兴趣。在文献中使用分析和数值方法来研究表面等离子体极性恒子(SPP)的传播,但是通过这些结构不能建立多层结构中的最佳厚度。本文采用了一种基于级联神经网络(NN)的新方法来预测涉及电磁场的多层等离子体结构的传播特性和耦合USP分析。训练的NNS能够以多层结构的厚度的不同值提供良好的精度,提供所需的SPP传播所需的最佳值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号