Deep learning-based misalignment error correction method for spherical surface testing.

Authors

Yuanyuan Shang, Yuqin Wang, Fujiang Zeng, Rongzhu Zhang

Published in

Applied optics. Volume 65. Issue 32. Pages 11182-11194. Nov 10, 2026. Epub Oct 12, 2026.

Abstract

Accurate calibration of misalignment errors is essential for high-precision interferometric testing of large numerical aperture (NA) spherical surfaces, as misalignment-induced aberrations degrade wavefront measurement accuracy. To improve the accuracy and efficiency of misalignment calibration, this paper proposes a deep-learning-based wavefront misalignment correction method using interferometric fringe patterns as input. Simulated datasets covering single- and multiple-misalignment conditions were generated, and two ResNet-50-based networks were developed using a classification-guided decoupled regression strategy. Simulation results on the standard in-range test sets show that magnitude prediction errors remain within 6% for all evaluated cases, and the residual wavefront RMS is substantially reduced after network-only correction in the single-misalignment simulation test. Additional simulations with perturbations at NA=0.9 show that the proposed method retains effective correction capability within the investigated perturbation ranges. Physical experiments conducted at NA=0.42 using separately retrained networks provide preliminary physical validation of the proposed method under the available experimental conditions.

PMID:
43008144
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.

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