LAUSR

Atomic force microscopy (AFM) is widely used in different fields, such as nanotechnology, semiconductor, microelectromechanical systems (MEMSs), bioscience, and so on. In the case of obtaining the 3-D topography of a large-scale MEMS sample, it is hard to localize the AFM tip position without other auxiliary microscopes in the unknown sample before scanning. Thus, in this article, the relative probe position on the MEMS layout map can be obtained, such that the probe can be moved and placed near the interesting region to start the inspection. However, the AFM scanned images on a MEMS sample typically involve only simple geometries with sparse features, which usually leads to localization difficulty. In this research, an AFM tip localization method was proposed by using the particle filter, referring to the macrorobot simultaneous localization and mapping (SLAM) technique. The AFM scanned images are treated as the unique sensor and the sample layout as the map. The sensor model of the particle filter is based on a feature extraction algorithm. After localization, the interesting area is scanned using a novel efficient scanning method combining online variable speed scan and learning-based feedforward control. In order to verify the effectiveness of the proposed methods, both tremendous simulations and experiments are conducted, and the results of the tip localization and efficient scanning on a MEMS sample are highly promising.