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 T 2 statistic 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.
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Lin, HD., Chung, CY., Chiu, S.W. (2007). Computer-Aided Vision System for Surface Blemish Detection of LED Chips. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_59
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DOI: https://doi.org/10.1007/978-3-540-71629-7_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
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