Mr. Shaoqing Han Profile

Shaoqing Han

at Shenzhen Institutes of Advanced Technology

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Proceedings Article | 28 June 2023 Paper

Full-size visual inspection system and method for semiconductor packaging lead-frame

Shaoqing Han, Feifei Gu

Proceedings Volume 12720, 127200L (2023) https://doi.org/10.1117/12.2675113

KEYWORDS: Cameras, Motion measurement, Inspection, Semiconductors, Visual inspection, Packaging, Calibration, Imaging systems, Light sources

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Semiconductor packaging lead-frame has the characteristics of high density, high precision, refinement, and miniaturization. However, traditional manual detection has a series of problems such as difficulty, low efficiency, and high miss rate. Aiming at this industry problem, this paper develops a full-scale detection system and corresponding detection method of semiconductor packaging lead frame based on machine vision. The developed system is composed of a motion control platform and system, hardware control systems such as light source camera, and image algorithm platform. Through the optimized visual detection algorithm and the accurate correction method of workpiece attitude under motion measurement conditions, it can realize the one-click adjustment of several key dimensions such as frame length, width, loading area thickness, pin thickness, bending height, and aperture Micron level measurement, high precision, and fast speed, effectively ensure the genuine product rate and control the scrap rate, to assist semiconductor packaging enterprises to save labor costs and improve production efficiency.

Proceedings Article | 21 June 2019 Paper

Nonwoven fabric crease defects detection based on Gabor filter

Shaoqing Han, Feifei Gu

Proceedings Volume 11061, 110610H (2019) https://doi.org/10.1117/12.2525959

KEYWORDS: Defect detection, Image filtering, Wavelet transforms, Wavelets, Inspection, Fourier transforms, Tunable filters, Spatial resolution, Analytical research, Human vision and color perception

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The defect detection of nonwoven fabrics is one of the most important steps of fabric quality assurance on production lines. For a long time, fabric defects detection has been carried out manually by human vision with an accuracy of about 60–70%, which not only affects the health of the inspectors, but also has high inspection cost. How to automatically detect crease defects of various forms at a high accuracy has been a challenging task in the field of machine vision. At present, Fourier transform and wavelet transform have been adopted to solve this problem. However, both of them can hardly detect stochastic textured in local region from different scales and directions. This paper adopted a 2D Gabor filter-based method to detect the crease defects, which has tunable angular and axial frequency bandwidth, tunable center frequencies, and could achieve optimal joint resolution in spatial and frequency domain. Firstly the fabric crease images are transformed from the spatial domain to the frequency domain. Secondly the frequency domain images are filtered by the Gabor filter with adjustable central frequency, bandwidth and azimuth, and the frequency domain images of the crease pattern are selected in the frequency domain image. Then they are reversed to the spatial domain. Finally the crease area of nonwoven fabric is obtained by the blob analysis. Experiments conducted on various forms of crease defects have shown that by adopting the proposed method, the nonwoven fabric’s crease defects can be detected effectively and accurately.

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