In this thesis, a new model based on Artificial Neural Network (ANN) is used to predict the propagation characteristics of plasmonic nanostrip and coupled nanostrips transmission lines. The trained ANNs are capable of providing the required outputs with good accuracy. The nonlinear mapping performed by the trained ANN is written in the form of closed form expressions for the different characteristics of the lines under investigation. These characteristics include the effective refractive index and the characteristic impedance. The plasmonic coupled nanostrips transmission line is used as a new sensor that that senses variation in the refractive index with accuracy of 10Ã¯â‚¬Â6ÃŽÂ¼m (The accuracy is defined as the change in the coupling length divided by the change in the cladding material refractive index). In addition, an optimal new design for polarization rotation based on the coupled nanostrips is introduced and characterized.
MS in Physics
Committee Member 1
Committee Member 2
El Gamal, Mohamed
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(2014).Plasmonic transmission lines: neural networks modeling and applications [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Andrawis, Robert. Plasmonic transmission lines: neural networks modeling and applications. 2014. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.