Deep Learning for Tilted-Wave Interferometry

The tilted-wave interferometer is an interferometrical measurement system for the accurate optical form measurement of optical aspheres and freeform surfaces. Its evaluation procedure comprises a high-dimensional inverse problem to reconstruct the form of the surface under test from measured data. Recent work has used a deep learning hybrid approach to solve the inverse problem successfully in a simulation environment. A quantification of the model uncertainty was incorporated using ensemble techniques. In this paper, we expand the application of the deep learning approach from simulations to measured data and show that it produces results similar to those of a state-of-the-art method in a real-world environment.

Authors:
Lara Hoffmann; 8.4 Data Analysis and Measurement Uncertainty & 4.2 Imaging and Wave Optics
Ines Fortmeier; 4.2 Imaging and Wave Optics
Clemens Elster; 8.4 Data Analysis and Measurement Uncertainty

For further information:
Hoffmann, Lara, Fortmeier, Ines and Elster, Clemens. "Deep learning for tilted-wave interferometry" tm - Technisches Mess, 2021. https://doi.org/10.1515/teme-2021-0103