Visual servoing-based approach for efficient autofocusing in scanning electron microscope

Abstract

Abstract-Fast and reliable autofocusing methods are essential for performing automatic nano-objects positioning tasks using a scanning electron microscope (SEM). So far in the literature, various autofocusing algorithms have been proposed utilizing a sharpness measure to compute the best focus. Most of them are based on iterative search approaches; applying the sharpness function over the total range of focus to find an image in-focus. In this paper, a new, fast and direct method of autofocusing has been presented based on the idea of traditional visual servoing to control the focus step using an adaptive gain. The visual control law is validated using a normalized variance sharpness function. The obtained experimental results demonstrate the performance of the proposed autofocusing method in terms of accuracy, speed and robustness.

Scanning electron microscope image signal‐to‐noise ratio monitoring for micro‐nanomanipulation

“…For focus experiment, the DISS5 image acquisition system used for this work provides a simple control for the focus by linking the objective lens focal length with a series of focus steps (i.e., each focus step modifies the focal length to get a focused image). The focus steps are varied automatically and more details can be found in (Marturi et al 2013a). The images are acquired with a scan time of 360 ns for both magnification and focus experiments.…”

“…The maximum of the sharpness curve provides the best focus position as pointed in the figure. For this test the image sharpness S has been computed using normalized variance sharpness function given by Equation (8) (Marturi et al, 2013a).…”

Depth and Shape estimation from focus in scanning electron microscope for micromanipulation

“…In order to overcome this problem, in this work, we first segment the image into different regions of interest (ROI). For each ROI, the best focus position is computed using the visual servoing-based autofocusing technique explained in [11]. Unlike the search-based methods, visual servoing-based method maximises the sharpness using an adaptive gain and reaches the best focus in few iterations.…”

“…The developed inter-object depth estimation method uses a visual servoing-based autofocusing algorithm explained in [11] for depth computation. The main advantage associated with the developed method is that it does not require any additional hardware modifications to the existing system.…”

Closed-Loop Autofocus Scheme for Scanning Electron Microscope

“…An alternative method has been proposed in [9], based on fitting the sharpness function to a quadratic polynomial approximatively using some initial measurements. In [10], the autofocus has been achieved by computing the derivative of sharpness function numerically. Finally, statistical learning-based autofocus methods were studied for SEM [11], but were never implemented in real-time.…”