Surface roughness estimation by 3D stereo SEM reconstruction

Surface roughness is an important parameter to describe materials’ topography. This parameter has been widely studied and presents important tasks in many engineering applications. The development of non-contact-based roughness measurement techniques for engineering surfaces has received much attent...

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Autores:: Henao Londoño, Juan Camilo

Tipo de recurso:

Fecha de publicación:: 2015

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

Description
Summary:	Surface roughness is an important parameter to describe materials’ topography. This parameter has been widely studied and presents important tasks in many engineering applications. The development of non-contact-based roughness measurement techniques for engineering surfaces has received much attention. However, stylus-based equipments are still dominating this measurement task. Stylus techniques have great inherent limitations as they were originally intended to acquire 2D surface topography. Therefore, 3D surface roughness data can only be obtained from stylus equipment executing multiple scans of the surface. This task takes a lot of time to achieve a satisfactory result, may make micro-scratches on surfaces and can only evaluate a small area in a reasonable amount of time. In this work a new automated methodology for obtaining a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereo-vision is proposed. The 3D models can then be used to evaluate the surface roughness parameters. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. Since the brightness change of corresponding pixels is negligible for the small tilt involved in stereo SEM, and the cost function relies on dynamic programming, the matching algorithm uses a sum of absolute differences (SAD) over a variable pixel size window and an occlusion parameter which penalizes large depth discontinuities, that in practice, smooths the disparity map and the corresponding reconstructed surface. This step yields a disparity map, i.e. the differences between the horizontal coordinates of the matching points in the stereo images. The horizontal disparity map is finally converted into heights according to the SEM acquisition parameters: tilt angle, magnification and pixel size. A validation test was first performed using a microscopic grid with manufacturer specifications as reference. Finally, some surface roughness parameters were calculated within the model

Surface roughness estimation by 3D stereo SEM reconstruction

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