Nanoscale measurement of the Power Spectral Density of surface roughness: a difficult experimental challenge and how to solve it

TL;DR

This paper addresses the challenge of accurately measuring surface roughness using SFM and PSD analysis, proposing a method to improve data quality by utilizing the error signal to obtain true topography.

Contribution

The authors introduce a technique that uses the error signal in SFM to correct topography data, enabling reliable PSD analysis independent of feedback parameters.

Findings

Error signal improves topography accuracy.

Corrected PSD curves are robust to feedback tuning.

Faster imaging without losing surface detail.

Abstract

In the present work we show that the correct determination of surface morphology using Scanning Force Microscopy (SFM) imaging and Power Spectral Density (PSD) analysis of the surface roughness is an extremely demanding task that is easily affected by experimental parameters such as scan speed and feedback parameters. We present examples were the measured topography data is significantly influenced by the feedback response of the SFM system and the PSD curves calculated from this experimental data do not correspond to that of the true topography. Instead, either features are "lost" due to low pass filtering or features are "created" due to oscillation of the feedback loop. In order to overcome these serious problems we show that the interaction signal (error signal) can be used not only to quantitatively control but also to significantly improve the quality of the topography raw data used for the PSD analysis. In particular, the calibrated error signal image can be used in combination with the topography image in order to obtain a correct representation of surface morphology ("true" topographic image). From this "true" topographic image a faithful determination of the PSD of surface morphology is possible. The corresponding PSD curve is not affected by the fine-tuning of feedback parameters, and allows for much faster image acquisition speeds without loss information in the PSD curve.