级联神经网络研究薄金属表面等离子体激元在OpenMP中的传播
A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP
摘要：
金属-电介质界面上的表面等离子体激元（SPPs）由于其强的场约束和增强效应，在超紧凑光子电路、光伏器件等领域引起了人们的极大兴趣。本文研究了一种新型的级联神经网络（NN）结构，以找到金属厚度对SPP传播的依赖性。此外，一个新的训练过程中提出的级联神经网络已开发使用基于OpenMP的框架，从而大大减少了训练时间。所进行的实验证实了所提出的神经网络架构的有效性，手头的问题。
英文摘要：
Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
