High-precision large-aperture single-frame interferometric surface profile measurement method based on deep learning

Large-aperture optical components are of paramount importance in domains such as integrated circuits, photolithography, aerospace, and inertial confinement fusion. However, measuring their surface profiles relies predominantly on the phase-shifting approach, which involves collecting multiple interferograms and imposes stringent demands on environmental stability. These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements. Therefore, this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement (LA-SFISPM) method based on deep learning and explores its capability to realize dynamic measurements with high accuracy. The interferogram is matched to the phase by training the data measured using the small aperture. The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment. Hence, high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter. The experimental results show that for the tested mirror with Φ = 820 mm, the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth, resulting in a maximum single-point error of 4.56 nm. Meanwhile, the peak-to-valley (PV) value is 0.075 8 λ, and the simple repeatability of root mean square (SR-RMS) value is 0.000 25 λ, which aligns well with the measured results obtained by ZYGO. In particular, a significant reduction in the measurement time (reduced by a factor of 48) is achieved compared with that of the traditional phase-shifting method. Our proposed method provides an efficient, rapid, and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach, which is highly desired in large-aperture interferometric measurement systems.