Abstract
Abstract 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.
Highlights
The need for accurate measurement techniques increases with ongoing technological advancements
The novelty of this paper is to show that the recently developed deep learning hybrid approach for computational optical form measurement [15] can be applied to measurement data it has never before seen, even if it was trained on purely simulated data
% of the reconstructed form from the state-of-the-art method is covered by the described model uncertainty of the deep learning prediction when no prior misalignment correction is carried out, while % of the topography is covered when the prior correction of the topography position is included
Summary
The need for accurate measurement techniques increases with ongoing technological advancements. The tilted-wave interferometer (TWI) [5, 6] is one of the state-of-the-art interferometrical measurement techniques for optical form measurements of optical aspheres and freeform surfaces. Recent work [14, 15] suggests the use of a deep learning hybrid approach to solve the inverse topography reconstruction problem. In this hybrid approach, the surface form is reconstructed by a deep learning model, while the prior calibration is performed by the conventional method using the simulation tool box SimOptDevice [16, 12, 17] developed at the Physikalisch-Technische Bundesanstalt (PTB). The recent approach incorporates a quantification of the model uncertainty and was shown to achieve accurate results and reliable uncertainty estimates in a simulationbased environment
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