This work presents a novel microscopic profilometry using diffraction image correlation, which is appropriate for in-situ automated optical inspection (AOI). In contrast to the traditional confocal microscopy, the developed technique replaces the detector pinhole by observing and matching the diffraction patterns. Thus accurate surface depth detection can be achieved by eliminating time-consuming vertical scanning operation. The development is the first attempt in optics to use image correlation between the pre-calibrated database of diffraction images and the measured one of the detecting object surface. The feasibility of the method has been theoretically verified by scalar diffraction theory, then verified by experimental testing. Also, a depth response curve with the physical meaning of similarity is introduced and interpreted. Meanwhile, multi-point lateral scanning in one field of view (FOV) is achieved by quickly switching micromirrors on the digital micromirror device (DMD), thus the quasi full-field 3-D reconstruction can be acquired by combining tens of captured images. Verified experimentally, a 3-D reconstruction with sub-micrometer vertical resolution can be realized, with a tunable lateral resolution.
A new full-field profilometry based on diffraction image correlation (DIC) was developed in which the technique bases on calibrated database of reference diffractive images (RDIs) to estimate surface depth information. This technique has the advantage of removing vertical scanning for achieving high measurement efficiency in microscopic surface profilometry. However, as the diffractive images not only depend on the tested height but also the local surface tilt. Thus, the pre-built image database may not match the measured diffractive image and lead to a systematic measured error incurred by the surface tilting condition. Thus, in the article, the influence of surface tilt to the diffraction images is investigated and analyzed to understand the relationship between the surface tilt and the image variation. A lateral drifting phenomenon depends on the tilt angle and tilt direction was also quantified to estimate the impact of the tested surface height. Meanwhile, a detection algorithm to determine the center of diffractive image was developed to position the shifting quantity of the image. Moreover, the information coupling problem between height and tilting parameters, such as the tilt angle and tilt direction was studied to decouple these parameters from the tested height, so the tested surface can be reconstructed accurately. To realize the proposed method, some data processing strategies were also proposed to decouple the depth information from multi-surface parameters such as surface tilt angles (pitch and yaw) and direction effectively.
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