Compensation of thermal drift effects in atomic force microscopy using probabilistic state estimation

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Since more than two decades, the atomic force microscope (AFM) represents an essential instrument in various disciplines covering life science, biology, material science, semiconductor industries, and micro- and nanotechnology in general. It has revolutionized surface analysis by providing high-resolution visualization of structures at micro-, nano-, and atomic scales. While AFM was first only used for imaging the topography of a sample, different operation modes evolved over time including electrical, magnetic, and chemical measurements. The need for novel lithographic methods providing higher resolution than conventional lithographic processes and enabling to process new materials (e.g. biomaterials) have motivated research in the domain of AFM-based lithography. Intense research has also been carried out on the manipulation of individual micro-/nanoentities with the AFM tip. Promising potential applications of such AFM-based assembly are e.g. prototyping of novel nanoelectronic devices and systems based on nanomaterials such as CNTs, DNA, single-graphene layers, nanowires, etc. A problem that is affecting almost every application area of AFM is the presence of thermal drift induced. Thermal drift originates from small changes in temperature and differences in the coefficient of thermal expansion of the different components of the AFM (or SPM in general). This results in an unknown, time-variant displacement of the probe relative to the sample in all three dimensions. This motion is generally slow, but it is causing distortions in images and lithographic processes, falsifying spectroscopy results, and compromising the success of nanomanipulations. In the proposed project our major objective is the development of a flexible drift compensation system applicable in the context of imaging, spectroscopy as well as nanomanipulation tasks. The developed methods should allow for an active compensation of thermal drift during image acquisition and manipulation in real-time without any prior knowledge of the sample properties or drift state. Moreover, it should compensate drift in all three dimensions thus not limiting the field of application. To reach these objectives a probabilistic algorithm will be developed incorporating non-raster scanning methods, generally valid models describing AFM topography data as well as a probabilistic model describing drift. The algorithm will be based on Bayesian filtering to incorporate the uncertainties introduced by inaccurate models. The system will be experimentally validated on different systems using a custom-made AFM control architecture in the context of AFM-based automated manipulation of different nanoobjects.