While the atomic force microscope (AFM) was mainly developed to image the topography of a sample, it has been discovered as a powerful tool also for nanomanipulation applications within the last decade. A variety of different manipulation types exists, ranging from dip-pen and mechanical lithography to assembly of nano-objects such as carbon nanotubes (CNTs), deoxyribonucleic acid (DNA) strains, or nanospheres. The latter, the assembly of nano-objects, is a very promising technique for prototyping nanoelectronical devices that are composed of DNA-based nanowires, CNTs, etc. But, pushing nano-objects in the order of a few nanometers nowadays remains a very challenging, labor-intensive task that requires frequent human intervention. To increase throughput of AFM-based nanomanipulation, automation can be considered as a long-term goal. However, automation is impeded by spatial uncertainties existing in every AFM system. This article focuses on thermal drift, which is a crucial error source for automating AFM-based nanoassembly, since it implies a varying, spatial displacement between AFM probe and sample. A novel, versatile drift estimation method based on Monte Carlo localization is presented and experimental results obtained on different AFM systems illustrate that the developed algorithm is able to estimate thermal drift inside an AFM reliably even with highly unstructured samples and inside inhomogeneous environments.
Analysis of long-term temperature monitoring of multiple wind turbines