Autofocus on moving object in scanning electron microscope

Abstract

The sharpness of the images coming from a Scanning Electron Microscope (SEM) is a very important property for many computer vision applications at micro- and nanoscale. It represents how much object details are distinctive in the images: the object may be perceived sharp or blurred. Image sharpness highly depends on the value of focal distance, or working distance in the case of the SEM. Autofocus is the technique allowing to automatically adjust the working distance to maximize the sharpness. Most of the existing algorithms allows working only with a static object which is enough for the tasks of visualization, manual microanalysis or microcharacterization. These applications work with a low frame rate, less than 1 Hz, that guarantees a low level of noise. However, static autofocus can not be used for samples performing continuous 3D motion, which is the case of robotic applications where it is required to carry out a continuous 3D position measurement, e.g., nano-assembly or nanomanipulation. Moreover, in addition to constantly keeping object in focus while it is moving, it is required to perform the operation at high frame rate. The approach offering both these possibilities is presented in this paper and is referred as dynamic autofocus. The presented solution is based on stochastic optimization techniques. It allows tracking the maximum of the sharpness of the images without sweep and without training. It works under uncertainty conditions: presence of noise in images, unknown maximal sharpness and unknown 3D motion of the specimen. The experiments, that were performed with noisy images at high frame rate (5 Hz), were conducted on a Carl Zeiss Auriga 60 FE-SEM. They prove the robustness of the algorithm with respect to the variation of optimization parameters, object speed and magnification. Moreover, it is invariant to the object structure and its variation in time.

Keywords: Autofocus; Gradient descent; SEM; Stochastic optimization.