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This study aims to enhance the accuracy of defective pixel detection and yield analysis in micro-LEDs by implementing a combined inspection technique utilizing Automated Optical Inspection (AOI) and Photoluminescence (PL), along with the application of a machine learning algorithm. During the image preprocessing stage, template matching and histogram normalization were performed, followed by the extraction of features based on the Gray-Level Co-occurrence Matrix (GLCM). A Random Forest model was employed to classify normal and defective pixels, with initial defect detection performed via outlier analysis using box plots. Based on these results, synthetic training data were generated by combining image segments, enabling the development of a model that achieved over 99% accuracy. To further categorize defect types, Principal Component Analysis (PCA) and K-means clustering were applied. In the PL inspection, defective pixels were identified through box plot analysis of the mean and standard deviation of brightness data. Finally, only devices classified as non-defective in both AOI and PL inspections were considered acceptable, thereby improving overall defect detection accuracy. Further validation with large-scale datasets and diverse defect types are required for industrial application.
