Quantifying geometric tip-sample effects in AFM measurements using certainty graphs

Abstract

Abstract One of the challenges faced in accurately measuring nanoscale surface roughness using Atomic Force Microscopy (AFM) techniques are the geometric interactions of the surface with the AFM probe-tip. What is the significance of these effects, and to what extent do they reduce the effectiveness of an AFM as a nanospatial measurement tool of surfaces with roughness? The aim of this work is to develop a framework by which an AFM user would be able to automatically [1] quantify the magnitude these effects from geometric tip-sample interactions have on measurement accuracy. In turn, the AFM user would then be able to make an informed decision on whether additional methods need to be applied to correct these measurements. To accomplish this, a numerical model is developed that can generate sets of ‘true’ stochastic surfaces of various statistical roughness parameters as well as various probe-tip shapes. The model then simulates, using such probe-tips, the scanning procedure of an AFM on such sets of surfaces to produce corresponding measurement images. The difference, due to geometric tip-sample interactions, between the ‘true’ surfaces and the measured images is quantified, over various ranges of roughness parameters for different probe-tips, with a novel certainty metric to create certainty graphs. These graphs show for a given tip, the significance of the tip effects due to surface roughness and thus the extent of measurement inaccuracy.