Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method

Abstract

Plasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated. For the surface morphology studies, chemical composition analysis and nanograin structure, Scanning Electron Microscopy (SEM), X-Ray Photoelectron Spectroscopy (XPS), Energy Dispersive X-Ray Spectroscopy (EDS) and High-Resolution Transmission Electron Microscopy (HR TEM) measurements were performed. The position of the resonance band was successfully tuned in the 100 nm range. The EDS together with the XPS analysis confirmed the formation of an alloy with the aspect ratio of individual metals in a single nanoisland similar to the ratio of the thicknesses of the initially sputtered layers. The experimental research was complemented by the neural network model, which enables the calculation of the absorbance peak depending on the thickness of Au and Ag layers and the annealing time. The proposed model of machine learning makes it possible to fine-tune the desired position of the plasmon resonance.