Computer-Aided Vision System for Surface Blemish Detection of LED Chips

Authors:
Hong-Dar Lin;Chung-Yu Chung;Singa Wang Chiu
Affiliations:
Department of Industrial Engineering and Management,;Department of Industrial Engineering and Management,;Department of Business Administration, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township, Taichung County 41349, Taiwan (R.O.C.),
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

This research explores the automated detection of surface blemishes in light-emitting diode (LED) chips. An LED is a semiconductor device that emits visible light when an electric current passes through the semiconductor chip. Water-drop blemishes, commonly appearing on the surfaces of chips, impair the appearance of LEDs as well as their functionality and security. Consequently, detecting water-drop blemishes becomes crucial for the quality control of LED products. We first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. Then, the T2statistic of multivariate statistical analysis is applied to integrate the multiple wavelet characteristics. Finally, the wavelet based multivariate statistical approach judges the existence of water-drop blemishes. Experimental results show that the proposed method achieves an above 95% detection rate and a below 1.5% false alarm rate in inspecting water-drop blemishes of LED chips.