unDrift: A versatile software for fast offline SPM image drift correction

Abstract

Scanning probe microscopy (SPM) techniques are widely used to study the structure and properties of surfaces and interfaces across a variety of disciplines in chemistry and physics. One of the major artifacts in SPM is (thermal) drift, an unintended movement between sample and probe, which causes a distortion of the recorded SPM data. Literature holds a multitude of strategies to compensate for drift during the measurement (online drift correction) or afterwards (offline drift correction). With the currently available software tools, however, offline drift correction of SPM data is often a tedious and time-consuming task. This is particularly disadvantageous when analyzing long image series. Here, we present unDrift, an easy-to-use scientific software for fast and reliable drift correction of SPM images. unDrift provides three different algorithms to determine the drift velocity based on two consecutive SPM images. All algorithms can drift-correct the input data without any additional reference. The first semi-automatic drift correction algorithm analyzes the different distortion of periodic structures in two consecutive up and down (down and up) images, which enables unDrift to correct SPM images without stationary features or overlapping scan areas. The other two algorithms determine the drift velocity from the apparent movement of stationary features either by automatic evaluation of the cross-correlation image or based on positions identified manually by the user. We demonstrate the performance and reliability of unDrift using three challenging examples, namely images distorted by a very high drift velocity, only partly usable images, and images exhibiting an overall weak contrast. Moreover, we show that the semi-automatic analysis of periodic images can be applied to a long series containing hundreds of images measured at the calcite–water interface.