In diesem Beitrag wird ein "Chirp"-Oberflächennormal vorgestellt. Es ist dafür vorgesehen, mit einer Serie von verschiedenen Ortsfrequenzen das Übertragungsverhalten von Oberflächenmessgeräten zu testen. Die Topografie des Normals enthält eine abgestufte Folge von Sinuswellen, deren Abstände, Steigungen und Krümmungen durch den Synthese-und Fertigungsprozess genau bekannt sind. Die Oberfläche des Normals wird durch einen Diamant-Drehprozess hergestellt. Bei der Erzeugung der NC-Steuerdaten wird mittels morphologischer Operationen der Einfluss der Werkzeuggeometrie berücksichtigt. Erste Ergebnisse von Messungen am Chirp-Normal mit Interferenzmikroskopen und Tastschnittgeräten werden gezeigt.In this article a chirp-calibration standard is presented. It is intended to test the function transfer of surface measuring instruments by a series of different space scaled frequencies. The standard contains a sequence of sinusoidal waves, the distance, slopes and curvatures of which are well known from the design and the manufacturing process. The surface of the specimen is manufactured by a single diamond turning process. The control data take into account the influence of the cutting tool by morphological operations. First results of measurements on the chirp standard by both a contact stylus instrument and an interference microscope are presented.

The abrasion (wear) of tips used in scanning force microscopy (SFM) directly influences SFM image quality and is therefore of great relevance to quantitative SFM measurements. The increasing implementation of automated SFM measurement schemes has become a strong driving force for increasing efforts towards the prediction of tip wear, as it needs to be ensured that the probe is exchanged before a level of tip wear is reached that adversely affects the measurement quality. In this paper, we describe the identification of tip abrasion in a system of SFM measurements. We attempt to model the tip-abrasion process as a concatenation of a mapping from the measured AFM data to a regression vector and a nonlinear mapping from the regressor space to the output space. The mapping is formed as a basis function expansion. Feedforward neural networks are used to approximate this mapping. The one-hidden layer network gave a good quality of fit for the training and test sets for the tip-abrasion system. We illustrate our method with AFM measurements of both fine periodic structures and randomly oriented sharp features and compare our neural network results with those obtained using other methods.

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In this work we study functions for maximum likelihood estimation in blind tip estimation. We will implement the expectation maximization (EM), the stochastic EM, and stochastic approximation EM algortithms to estimate the unknown tip geometry. To demonstrate the functionality of the algorithms we applied it to dilated artificial input signal.