Pattern recognition imaging for AFM measurements

The focus of this paper is on an algorithm for distortion corrections for atomic force microscope (AFM) recorded images. AFM is a fundamental tool for the investigation of a wide range of mechanical properties due to the contact interaction between the AFM tip and the sample surface. When a sequence to AFM images correspondent to the same area are recorded, it is common to observe convolution of thermal drift with surface modifications due to the AFM tip stresses. The surface modifications are material properties and add knowledge to the response of the materials on nanoscale. As a consequence,a suitable de-convolution of the thermal drifts on the recorded images need to be developed. In this paper, we present a method for correcting thermal drifts where the original images are corrected using a low-order polynomial mapping function. The precision achieved and the fast computation time required make our method particularly useful for image analysis in a wide range of applications.