Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations

In atomic force microscopy (AFM)-based nanomanipulation, the tip position uncertainties still exist due to the parameter inaccuracies in the open-loop compensation of the piezo scanner, the noise in the closed-loop control and thermal drift. These spatial uncertainties are very challenging to be directly estimated owing to the lack of real-time feedback, and its effects are more significant in performing an automatic nanomanipulation/assembly task than macro world manipulations. In this paper, we propose a stochastic framework for feature-based localization and planning in nanomanipulations to cope with these uncertainties. In the proposed framework, some features in the sample surface are identified to calculate their positions in statistics, and detected by using the AFM tip as the sensor itself through a local scan-based motion. In the localization, the Kalman filter is used through incorporating the tip motion model and the local scan-based observation model to estimate the on-line tip position in the task space. The simulation and experiments about tip positioning are carried out to illustrate the validity and feasibility of the proposed algorithm. Then, positioning tip for effective nanomanipulation is presented by using several experiments. Finally, a carbon nanotube is followed to show that the proposed method can provide a great potential for improving the position accuracy. Note to Practitioners-Atomic force microscopy (AFM)-based nanomanipulation has become a promising approach in developing devices and structures at nanoscale. One of the prerequisites for the effective and successful nanomanipulation is that the AFM tip position relative to the interest region can be controlled accurately. This paper proposes a stochastic approach for feature-based localization and planning method to solve these problems. The uncertainties of the tip position are decreased by using the Kalman filter method in the localization procedure. Fifty times of experiments are represented to illustrate the effectiveness and efficiency of the tip positioning method. Then, basic nanomanipulations in the vertical direction without and with SAFLP method are performed for representing significance of positioning the tip. Furthermore, assembling nanostructures and following the carbon nanotube are carried out by using SAFLP. These experiments indicate that accurate positioning tip in AFM-based observation and manipulation can provide valid nanomanipulation in N/MEMS assembly, and the following nano-objects such as nanotube in real-time observation, which will promote automation implementation in nanomanipulation.</p

Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations

In atomic force microscopy (AFM)-based nanomanipulation, the tip position uncertainties still exist due to the parameter inaccuracies in the open-loop compensation of the piezo scanner, the noise in the closed-loop control and thermal drift. These spatial uncertainties are very challenging to be directly estimated owing to the lack of real-time feedback, and its effects are more significant in performing an automatic nanomanipulation/assembly task than macro world manipulations. In this paper, we propose a stochastic framework for feature-based localization and planning in nanomanipulations to cope with these uncertainties. In the proposed framework, some features in the sample surface are identified to calculate their positions in statistics, and detected by using the AFM tip as the sensor itself through a local scan-based motion. In the localization, the Kalman filter is used through incorporating the tip motion model and the local scan-based observation model to estimate the on-line tip position in the task space. The simulation and experiments about tip positioning are carried out to illustrate the validity and feasibility of the proposed algorithm. Then, positioning tip for effective nanomanipulation is presented by using several experiments. Finally, a carbon nanotube is followed to show that the proposed method can provide a great potential for improving the position accuracy. Note to Practitioners-Atomic force microscopy (AFM)-based nanomanipulation has become a promising approach in developing devices and structures at nanoscale. One of the prerequisites for the effective and successful nanomanipulation is that the AFM tip position relative to the interest region can be controlled accurately. This paper proposes a stochastic approach for feature-based localization and planning method to solve these problems. The uncertainties of the tip position are decreased by using the Kalman filter method in the localization procedure. Fifty times of experiments are represented to illustrate the effectiveness and efficiency of the tip positioning method. Then, basic nanomanipulations in the vertical direction without and with SAFLP method are performed for representing significance of positioning the tip. Furthermore, assembling nanostructures and following the carbon nanotube are carried out by using SAFLP. These experiments indicate that accurate positioning tip in AFM-based observation and manipulation can provide valid nanomanipulation in N/MEMS assembly, and the following nano-objects such as nanotube in real-time observation, which will promote automation implementation in nanomanipulation.</p