| Summary: | 碩士 === 國立雲林科技大學 === 資訊工程研究所 === 95 === 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 LED wafer defects using the Learning Vector Quantization (LVQ) neural network is presented. In the wafer image, each die image can be taken and each of the regions of interest (ROI) can be handled separately. Then, by analyzing the properties of every ROI, we can extract specific geometric features and texture features. Using these features, the presented LVQ neural network is used to classify these dies as either acceptable or not. The experimental results confirm the effectiveness of the approach for LED wafer defect inspection.
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