Pattern recognition methods for thermal drift correction in Atomic Force Microscopy imaging

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Abstract
Atomic Force Microscopy (AFM) is a fundamental tool for the investigation of a wide range of mechanical properties on nanoscale due to the contact interaction between the AFM tip and the sample surface. The information recorded with AFM is stored and synthesized by imaging the sample properties to be studied. Distortions and unwanted effects in AFM images can be produced both due to instrumental sources or sample unknown bad responses. The focus of this paper is on an algorithm for distortion corrections for AFM recorded images due to the convolution of thermal drift and unknown polymer compliance. When a sequence of AFM images correspondent to the same polymeric area is acquired, it is common to observe the 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 needs to be developed. Because soft polymeric samples can present unwanted height alteration due to the stressing AFM tip contact, we present a method that combines a thermal drifts correcting tool (where the original images are modified using a suitable mapping function) with a height rescaling method. In turn, an image matching method based on a Tikhonov functional is developed between topography data and the surface elastic maps, respectively. The precision achieved and the fast computation time required make our methods particularly useful for image analysis on soft polymeric samples as well as in a wide range of other scanning probe microscopy applications.

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