Imaging artefacts in scattering-type scanning near field optical microscopy arising from optical diffraction effects and contrast-active sub-surface features

TL;DR

This paper investigates optical diffraction artefacts and sub-surface feature effects in s-SNOM imaging, revealing how these artefacts influence image quality and proposing pathways for improved data acquisition and analysis.

Contribution

It identifies and characterizes diffraction-related artefacts and buried feature effects in s-SNOM, supported by experimental and simulation data, advancing understanding of artefact origins and mitigation.

Findings

Artefacts affect images at multiple demodulation frequencies.

Diffraction artefacts resemble laser intensity fluctuations.

Buried features can mimic surface signals without prior knowledge.

Abstract

The scattering-type Scanning Near-Field Optical Microscope (s-SNOM) is acknowledged as an excellent tool to investigate the optical properties of different materials and biological samples at the nanoscale. In this study we show that s-SNOM data are susceptible to being affected by specific artefacts related to the light diffraction phenomena and to stray contributions from shallow buried, contrast-active, structures. We focus on discussing the diffraction contributions from sample edges, next to those corresponding to one- or two-dimensional periodic structures, and undesired contributions from shallow buried periodic features. Each scenario was examined individually through both experimental methods and simulations. Our experimental findings reveal that such artefacts affect not only s-SNOM images demodulated at the direct-current (DC) component and the fundamental frequency, but also images demodulated at higher harmonic frequencies. We show that image artefacts caused by diffraction resemble the undesirable effects caused by illumination with a laser beam of unstable intensity, and that buried features can yield s-SNOM signals that cannot be distinguished from those originating from the sample surface, in absence of prior knowledge of the sample structure. Performed simulations confirm these experimental findings. This work enhances the understanding of s-SNOM data and paves the way for new data acquisition and postprocessing methods that can enable next-generation s-SNOM imaging and spectroscopy with significantly enhanced signal-to-noise ratio and resolution.