In this paper, a novel approach of SEM calibration based on non-linear minimization process is presented. The SEM calibration for the intrinsic parameters are achieved by an iterative non-linear optimization algorithm which minimize the registration error between the current estimated position of the pattern and its observed position. The calibration can be achieved by one image and multiple images of calibration pattern. Perspective and parallel projection models are addressed in this approach. The experimental results show the efficiency and accuracy of the proposed method. I. INTRODUCTIONScanning electron microscope (SEM) is an electron microscope where a focused beam of electrons is used to scan the surface of a specimen. This is an essential instrument to display, measure and manipulate the micro and nanostructure with a micrometers or nanometers accuracy. When the task requires the computation of metric information from the acquired 2D images, the calibration of the SEM is an important issue to be considered.Since the structure of a scanning electron microscope is very different from the structure of an optical microscope, it became apparent that novel image analysis, geometrical projection models and calibration processes would be necessary in order to extract accurate information from the SEM images. In earlier studies, photogrammetric analysis of SEM has been considered by several authors [1],[6]. The projection model relates a three-dimensional (3D) point on a specimen in the observed space to its projection in the two-dimensional (2D) image. Previous studies consider that at low magnification, perspective projection model can be applied because the observed area and electron beam sweep angle are both large. At higher magnification, the center of projection is usually regarded at infinity so the parallel projection model is assumed. However, the practical limit between the choice of perspective projection and parallel projection model is not clear. Some experiments [4], [16] show that parallel projection is assumed at magnification of 1000× and higher. [9] have concluded that the use of parallel projection depends on the desired accuracy for the calculation of position of a point on the specimen. Another important issue in calibration is distortion, which contains spatial distortion (static distortion) and time-dependent drift (temporally-varying distortion). The drift is mainly due to the accumulation of electrons on the surface of the observed 1 Le Cui is with Université Rennes 1,

Visual tracking and estimation of the 3D posture of a micro/nano-object is a key issue in the development of automated manipulation tasks using the visual feedback. The 3D posture of the micro-object is estimated based on a template matching algorithm. Nevertheless, a key challenge for visual tracking in a scanning electron microscope (SEM) is the difficulty to observe the motion along the depth direction. In this paper, we propose a template-based hybrid visual tracking scheme that uses luminance information to estimate the object displacement on x-y plane and uses defocus information to estimate object depth. This approach is experimentally validated on 4-DoF motion of a sample in a SEM.

Structural similarity (SSIM) is one image quality assessment metric that focuses on the statistic information in the spatial domain. It cannot reflect the small details of the contrast and the changing of texture, which can be perceived by human visual system，because SSIM cannot detect the distortion image with aliasing and blur effectively. This paper proposes a new image quality assessment metric called structural similarity based on global phase coherence (GPC-SSIM), which considers both the structural information in the spatial domain and the phase characteristics in the frequency domain. Through experiments, as the level of blur and aliasing of an image gets more and more serious, the dynamic range of the results obtained through SSIM is 0.6~1, while the ones through the new assessment index GPC_SSIM is 0~1. Thus GPC-SSIM is more sensitive to the blur and aliasing of image and can give more accurate assessment results for various kinds of degraded images than SSIM.Keywords-image quality assessment; phase information; structural similarity (SSIM); global phase coherence (GPC)

In this paper, an approach for 6-DoF automatic micropositioning is presented. It involves a closed-loop visual servoing scheme in order to achieve eye-to-hand positioning task in micro-scale. Instead of using classical visual features in the servoing scheme, pure image photometric information from the vision sensor is employed to compute the control law for micropositioning. The approach is validated in simulation as well as experimentally on a parallel positioning stage and a digital microscope at low magnification. Experimental results show the accuracy and efficiency of this control scheme.