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.
This work was supported by the Miur project “Energetic” (PON02_00355_3391233 IT).
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Bonanno, F., Capizzi, G., Sciuto, G.L., Napoli, C., Pappalardo, G., Tramontana, E. (2014). A Cascade Neural Network Architecture Investigating Surface Plasmon Polaritons Propagation for Thin Metals in OpenMP. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_3
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DOI: https://doi.org/10.1007/978-3-319-07173-2_3
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