Automated defect inspection of light-emitting diode chips using neural network and statistical approaches

This research explores the automated visual inspection of surface blemishes that fall across two different background textures in a light-emitting diode (LED) chip. Water-drop defects, commonly found on chip surface, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop defect is difficult because the blemish has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. The one-level Haar wavelet transform is used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based neural network (WNN) and wavelet-based multivariate statistical (WMS) approaches are proposed individually to integrate the multiple wavelet characteristics. Finally, the back-propagation algorithm of WNN and T2 test of WMS individually judge the existence of water-drop defects. Experimental results show that both of the proposed methods achieve above 95% and 92% detection rates and below 7.5% and 5.8% false alarm rates, respectively.