AFM Tip Localization on Large Range Sample Using Particle Filter for MEMS Inspection

Abstract

Atomic force microscopy (AFM) is a powerful instrument that has the ability to characterize sample topography on nanoscale resolution. AFM is widely used in different fields, such as nanotechnology, semiconductor, Microelectromechanical Systems (MEMS), bioscience. In the case of obtaining 3D topography of a large range sample, we need to know the relative position of the AFM probe to the sample. The scanning range of an AFM generally is much smaller than the sample size. Therefore, it is hard to localize the AFM tip position without other auxiliary microscopes such as optical microscope. Moreover, the AFM scanned images on a MEMS sample typically involve only simple geometries with sparse features which usually leads to the difficulty of localization. Besides, the system uncertainties including piezoelectric scanner hysteresis, thermal drift, and coarse dual stage would affect positioning accuracy. In this paper, we propose an AFM tip localization method using particle filter referring to macro robot Simultaneous localization and mapping (SLAM). We take the AFM scanned image 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. To verify the efficacy of the proposed methods, both simulations and experiments are conducted, and the proposed tip localization method is highly promising.