Multilayer Plasmonic Nanostructures for Improved Sensing Activities Using a FEM and Neurocomputing-Based Approach

Abstract

In order to obtain optimized elementary devices (photovoltaic modules, power transistors for energy efficiency, high-efficiency sensors) it is necessary to increase the energy conversion efficiency of these devices. A very effective approach to achieving this goal is to increase the absorption of incident radiation. A promising strategy to increase this absorption is to use very thin regions of active material and trap photons near these surfaces. The most effective and cost-effective method of achieving such optical entrapment is the Raman scattering from excited nanoparticles at the plasmonic resonance. The field of plasmonics is the study of the exploitation of appropriate layers of metal nanoparticles to increase the intensity of radiation in the semiconductor by means of near-field effects produced by nanoparticles. In this paper, we focus on the use of metal nanoparticles as plasmonic nanosensors with extremely high sensitivity, even reaching single-molecule detection. The study conducted in this paper was used to optimize the performance of a prototype of a plasmonic photovoltaic cell made at the Institute for Microelectronics and Microsystems IMM of Catania, Italy. This prototype was based on a multilayer structure composed of the following layers: glass, AZO, metal and dielectric. In order to obtain good results, it is necessary to use geometries that orthogonalize the absorption of light, allowing better transport of the photocarriers-and therefore greater efficiency-or the use of less pure materials. For this reason, this study is focused on optimizing the geometries of these multilayer plasmonic structures. More specifically, in this paper, by means of a neurocomputing procedure and an electromagnetic fields analysis performed by the finite elements method (FEM), we established the relationship between the thicknesses of Aluminum-doped Zinc oxide (AZO), metal, dielectric and their main properties, characterizing the plasmonic propagation phenomena as the optimal wavelengths values at the main interfaces AZO/METAL and METAL/DIELECTRIC.

Keywords: cascade forward neural network (CFNN); finite element analysis (FEM); solar cell; surface plasmon polaritons (SPPs).

Figure 1

Structure of the thin-film solar device. The AZO (conductive aluminium-doped zinc oxide) layer is placed between the glass and the metal layers.

Figure 2

(a) Cross-section of a solar cell based on the structure of Figure 1 stack from SEM analysis (b) Cross-section of the cell stack from TEM analysis.

Figure 3

The real and imaginary part of amorphous silicon and doped relative dielectric constant as a function of photon energy.

Figure 4

Architecture of the selected CFNN.

Figure 5

The plasmon’s wavelength, ${\lambda }_{SPP}$ for different thicknesses values of metal and AZO at the interface AZO/METAL (up) and METAL/DIELECTRIC (down) obtained by means of COMSOL simulations.

Figure 5

The plasmon’s wavelength, ${\lambda }_{SPP}$ for different thicknesses values of metal and AZO at the interface AZO/METAL (up) and METAL/DIELECTRIC (down) obtained by means of COMSOL simulations.

Figure 6

The plasmon’s wavelength, ${\lambda }_{SPP}$ for different thicknesses values of metal and AZO at the interface AZO/METAL (up) and METAL/DIELECTRIC (down) obtained by means the use of selected CFNN.