Performance and compatibility of different partially new methods in optical topography measurement are discussed. By using autofocus sensing, an established measurement procedure, height data of the surface are delivered directly in the scanning modus. Via novel ellipsotopometry, the local slope angles of the surface are detected. Here, surface topography follows from the gradient data by integration. Two slope detection procedures are conceivable: scanning ellipsotopometry using a very thin measurement beam by focusing on the surface to be measured, and imaging ellipsotopometry applying a CCD sensor detecting an extended measurement beam. Slope-based determination of topography is performed by optimal filtering algorithms based on the Gauss-Markoff estimation. This filtering takes advantage of inherent redundancy of the surface gradient data. For each of the two slope detecting procedures, we discuss the required algorithms to obtain optimal filtering. The performance of the data filtering is successfully demonstrated by measuring a sinusoidal surface standard. In comparison to nonredundant autofocus surface height measurement, ellipsotopometry applying optimal filtering can lead to significantly better results. The described filtering techniques can be transferred to other measurement procedures determining the surface gradients (e.g., Nomarsky interferometry, shearography), respectively.
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