Image-Guided Nanopositioning Scheme for SEM

Positioning of micro-nanoobjects inside a scanning electron microscope (SEM) for manipulation is a key and challenging task to perform. Often it is performed by skilled operators via teleoperation, which is tedious and lacks repeatability. In this paper, rendering this task as an image-guided problem, we present a frequency domain scheme for automatic control of positioning platform movements. The designed controller uses the relative global image motion computed using the frequency spectral information of the images as visual signal and can provide control up to five degrees of freedom. The proposed approach is validated in simulations as well as experimentally using a high-resolution piezo-positioning platform mounted inside a SEM vacuum chamber. The obtained results quantify the performance of the proposed nanopositioning scheme.Note to Practitioners - The main motivation behind this paper comes from the very need for automatic positioning of objects inside a scanning electron microscope (SEM) to perform dynamic analysis and structural characterization. Mostly, the positioning tasks are exhibited by skilled operators via teleoperation. Nevertheless, it is still a difficult task to repeat, and hence automatic strategies are indispensable. This can be tackled up to an extent using microscopic vision information. However, the regular vision-guided strategies with integrated feature tracking are hard to use with SEM due to multiple instabilities associated with the imaging process. To address this issue, this paper presents an image frequency-based positioning stage controller that does not require any visual tracking and is capable of dealing with electronic images provided by SEM for automatic nanopositioning. The presented results illustrate the capability of the method to handle various perturbations and demonstrate its performance in terms of accuracy, robustness, and repeatability. Due to the existence of orthographic projection, the proposed method is limited to control depth displacements. This can be resolved by combining it with visual servoing-based autofocus methods.