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 to strongly reduce the training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.
A Cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in OpenMP / Bonanno, F; Capizzi, G; Lo Sciuto, G; Napoli, C; Pappalardo, G; Tramontana, E. - 8467:(2014), pp. 22-33. (Intervento presentato al convegno 13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014 tenutosi a Zakopane; Poland) [10.1007/978-3-319-07173-2_3].
A Cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in OpenMP
Napoli C
;
2014
Abstract
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 to strongly reduce the training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.File | Dimensione | Formato | |
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