Im Rahmen des BMBF-Projektes SiM4diM entwickelt die PTB zusammen mit den Firmen JCMwave und Carl Zeiss IMT sowie der Hochschule Aalen neue Verfahren zur vollständigen Modellierung mikroskopisch-bildgebender Messsysteme auf der Grundlage rigoroser Beugungsrechnungen. Ziel ist es, mit diesem „virtuellen Mikroskop“ die bildbasierte industrielle Messtechnik signifikant zu verbessern und die Unsicherheit dimensionaler Metrologie auch im industriellen Umfeld um mehr als eine Größenordnung zu verringern.

This contribution presents experimental and simulation results of a tiltable line scanning low coherence interferometer applied for form measurement of spherical and aspherical objects with a diameter of up to 300 mm.
The region of interest is sampled by multiple annular subapertures that are realigned employing stitching algorithms based on Cartesian- and Zernike polynomial fittings.
The paper addresses common challenges in the reduction and modeling of displacement errors associated with the motion of the interferometric sensor between subaperture measurements and compares the topography deviations of the experimental results with those simulated by a Monte Carlo based model.

Uncertainty quantification by ensemble learning is explored in terms of an application known from the field of computational optical form measurements. The application requires solving a large-scale, nonlinear inverse problem. Ensemble learning is used to extend the scope of a recently developed deep learning approach for this problem in order to provide an uncertainty quantification of the solution to the inverse problem predicted by the deep learning method. By systematically inserting out-of-distribution errors as well as noisy data, the reliability of the developed uncertainty quantification is explored. Results are encouraging and the proposed application exemplifies the ability of ensemble methods to make trustworthy predictions on the basis of high-dimensional data in a real-world context.