Automatic visual inspection of defects plays an important
role in industrial manufacturing with the benefits of low-cost and high
accuracy. In light-emitting diode (LED) manufacturing, each die on the LED wafer must
be inspected to determine whether it has defects or not. Therefore, detection
of defective regions is a significant issue to discuss. In this paper, a new
approach for inspection of LEDwafer defects using the learning vector
quantization (LVQ) neural network is presented. In the waferimage, each
die image and the region of interest (ROI) in them to handle can be acquired.
Then, by analyzing the properties of every ROI, we can extract specific
geometric features and texture features. Using these features, the LVQ neural
network is presented to classify these dies as either acceptable or not. The
experimental results confirm the usefulness of the approach for LED wafer defect
inspection.
Source:IEEE
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