Image resolution in reflection scanning near-field optical microscopy using shear-force feedback: characterization with a spline and Fourier spectrum

Localized surface plasmon resonance in arrays of nano-gold cylinders: inverse problem and propagation of uncertainties

The plasmonic nanostructures are widely used to design sensors with improved capabilities. The position of the localized surface plasmon resonance (LSPR) is part of their characteristics and deserves to be specifically studied, according to its importance in sensor tuning, especially for spectroscopic applications. In the visible and near infra-red domain, the LSPR of an array of nano-gold-cylinders is considered as a function of the diameter, height of cylinders and the thickness of chromium adhesion layer and roughness. A numerical experience plan is used to calculate heuristic laws governing the inverse problem and the propagation of uncertainties. Simple linear formulae are deduced from fitting of discrete dipole approximation (DDA) calculations of spectra and a good agreement with various experimental results is found. The size of cylinders can be deduced from a target position of the LSPR and conversely, the approximate position of the LSPR can be simply deduced from the height and diameter of cylinders. The sensitivity coefficients and the propagation of uncertainties on these parameters are evaluated from the fitting of 15500 computations of the DDA model. The case of a grating of nanodisks and of homothetic cylinders is presented and expected trends in the improvement of the fabrication process are proposed.

Scanning near-field optical microscopy signal processing and resolution

To increase the signal-to-noise ratio and to remove the spatially slow varying signals, a lock-in amplifier is often used in scanning probe microscopy. The signal reconstructed from the lock-in data contains the contributions of the evanescent and homogeneous waves that are mixed in the near-field zone (i.e., at a very short distance). The resolution is determined and a method is given to suppress the useless background information. Experimental images of nanoparticles are processed.

Time-frequency analysis: a tool to discriminate artefacts from near-field optical data

Near-field optical data are non-stationary, which means that their spectral content varies with the position of the tip, due to both the scanning-probe recording process and the variations of the optical signal. Therefore time-frequency representations are potentially powerful tools for local characterization as they distribute the energy of the analysed signal over the time and frequency variables, and faithfully depict the signal local behaviour. In this paper, the time-frequency distributions are shown to be appropriate tools to analyse near-field optical data by using it first on simulated data, and second on experimental near-field optical images. Within this context, we observe that time-frequency analysis allows a possible separation of relevant optical signals from artefacts, especially in the usual case where the near-field optical signal is lower band than the feedback data.

Optical near-field data analysis through time-frequency distributions: application to the characterization and separation of the image spectral content by reassignment

The near-field optical images have been traditionally analyzed by Fourier analysis and, recently, by wavelet analysis. Those data are nonstationary, which means that their spectral content varies with time, owing to the scanning-probe recording process; therefore time-frequency representations are, potentially, powerful tools for local characteristics extraction or shape separation, since they distribute the energy of the analyzed signal over the time and frequency variables and faithfully depict the signal local behavior. In this study we show that Cohen's class time-frequency distributions and their modified version by the reassignment method are appropriate tools for the analysis of near-field optical data. We demonstrate this by using these tools first on simulated data and second on experimental near-field optical images. Within this context we observe that time-frequency analysis allows one to easily characterize local frequencies, which involves a possible separation of relevant optical signal from artifacts.