Real-time Topography and Hamaker Constant Estimation in Atomic Force Microscopy Based on Adaptive Fading Extended Kalman Filter

In this study, a novel technique based on adaptive fading extended Kalman filter for atomic force microscopy is proposed to directly estimate the topography of a sample surface without needing any control system. While in conventional imaging techniques, the scanning speed or the bandwidth is limited due to a relatively large settling time, the method proposed in this research is able to address this issue and estimate the topography throughout transient oscillation of the microcantilever. With this aim, an estimation process using an adaptive fading extended Kalman filter (augmented with forgetting factor) as the system observer is designed and coupled with the system dynamics to obtain sample topography. Obtained results demonstrate that the sample height is estimated by this algorithm with high accuracy and a relatively high scanning speed. Moreover, the observer is able to identify the topography and Hamaker constant simultaneously. Therefore, the presented approach can compensate for the low scanning speed of the classical imaging method as well as eliminate the need for a closed-loop controller.