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Proceedings Paper

Improved metrology of implant lines on static images of textured silicon wafers using line integral method
Author(s): Kuldeep Shah; Eli Saber; Kevin Verrier
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Paper Abstract

In solar wafer manufacturing processes, the measurement of implant mask wearing over time is important to maintain the quality of wafers and the overall yield. Mask wearing can be estimated by measuring the width of lines implanted by it on the substrate. Previous methods, which propose image analysis methods to detect and measure these lines, have been shown to perform well on polished wafers. Although it is easier to capture images of textured wafers, the contrast between the foreground and background is extremely low. In this paper, an improved technique to detect and measure implant line widths on textured solar wafers is proposed. As a pre-processing step, a fast non-local means method is used to denoise the image due to the presence of repeated patterns of textured lines in the image. Following image enhancement, the previously proposed line integral method is used to extract the position of each line in the image. Full- Width One-Third maximum approximation is then used to estimate the line widths in pixel units. The conversion of these widths into real-world metric units is done using a photogrammetric approach involving the Sampling Distance. The proposed technique is evaluated using real images of textured wafers and compared with the state-of-the-art using identical synthetic images, to which varying amounts of noise was added. Precision, recall and F-measure values are calculated to benchmark the proposed technique. The proposed method is found to be more robust to noise, with critical SNR value reduced by 10dB in comparison to the existing method.

Paper Details

Date Published: 13 March 2015
PDF: 10 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050F (13 March 2015); doi: 10.1117/12.2084525
Show Author Affiliations
Kuldeep Shah, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
Kevin Verrier, Varian Semiconductor Equipment Associates, Inc. (United States)


Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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