Abstract
Existing wafer pre-alignment methods suffer from high detection and maintenance costs, poor real-time performance, and the inability to simultaneously perform transparent wafer detection. In this paper, an improved visual detection method is proposed. Firstly, the method applies Gaussian filtering to remove noise and uses homomorphic filtering to enhance image contrast. Secondly, local OTSU threshold segmentation and morphological processing are employed for binarization, followed by perspective distortion correction. Finally, the sub-pixel edge rough fitting of the centroid is achieved using Zernike moments, and a geometric feature-based method is used to detect the corner positions of the wafer notches. After removing edge data, an iterative weighted least squares circle fitting algorithm is utilized for precise center fitting and center compensation to measure the wafer eccentricity. Experimental results demonstrate that compared to existing visual detection algorithms, this method can detect multiple types of wafers with low maintenance costs and achieve pre-alignment within 3.8 s, with positioning accuracy within 10 mm and a standard deviation of error of 0.0037 mm. It exhibits good real-time performance and robustness.
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QT (Corresponding Author) contributed to conceptualization, funding acquisition, resources, supervision, and writing—review & editing. XH (First Author) contributed to conceptualization, methodology, software, investigation, formal analysis, and writing—original draft. YM contributed to visualization and investigation. JH contributed to resources and supervision, Data Curation and Visualization.
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Tang, Q., Huang, X., Miao, Y. et al. A robust algorithm for rapid pre-alignment of multiple types and sizes of wafers. SIViP 18, 2559–2569 (2024). https://doi.org/10.1007/s11760-023-02930-3
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DOI: https://doi.org/10.1007/s11760-023-02930-3