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.
This paper presents a real time automatic image correction technique operating under real-time
constraints (25ms) to address red dot artifacts in low resolution display panels. The algorithm is
designed as a two stage process, starting with the identification of pixels which could cause such
artifacts followed by a color correction scheme to compensate for any perceived visual errors.
Artifact inducing pixels are identified by thresholding the vector color gradient of the image.
The color levels of the adjacent subpixels around the artifacts are estimated based on partitive
spatial color mixing. Red dot artifacts occur as singlet, couplets or triplets and consequently three
different correction schemes are explored. The algorithm also ensures that artifacts occurring
at junctions, corners and cross sections are corrected without affecting the underlying shape or
contextual sharpness. The performance of our algorithm was benchmarked using a series of 30
corrected and uncorrected images by human observers. This algorithm is designed as a general
purpose color correction technique for display panels with an offset in their subpixel patterns
and can be easily implemented as an isolated real time post processing technique for the output
display buffer without any higher order processing or image content information. All the above
mentioned benefits are realized through software and don’t require any upgrade or replacement
of existing hardware.