利用多层感知器完成二维光子晶体的带隙预测

2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) Pub Date : 2017-08-01 DOI:10.1109/INTERCON.2017.8079685

Adriano da Silva Ferreira, H. Figueroa, Gilliard Nardel Malheiros Silveira

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引用次数: 4

摘要

本文建立了一个多层感知器人工神经网络模型，用于估计二维光子晶体的完全光子带隙。采用人工免疫网络算法设计了由两根硅圆棒组成的嵌入空气中的方形晶格光子晶体单元格，并将其几何形状与其C-PGBs一起存储在数据库中。使用未知单元格的测试评估并证明了模型多层感知器的C-PBG预测能力。

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Complete band-gap prediction of 2D photonic crystals by using multilayer perceptron

In this paper, a Multilayer Perceptron artificial neural network is modeled to estimate complete photonic band-gaps (C-PBGs) of bi-dimensional photonic crystals. Unit cells of square lattice photonic crystals, composed of two silicon round rods and embedded in air, have been designed by an artificial immune network algorithm, and their geometries have been stored in a database along with their C-PGBs. Tests using unknown unit cells assess and demonstrate the C-PBG predicting capability of the modeled Multilayer Perceptron.

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2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)

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