Chip Quality Inspection System based on Deep Learning

The emergence of deep learning-based chip quality inspection systems marks a significant leap in modern technology. Chips, crucial to high-tech industries, greatly impact product performance, reliability, and safety. Traditional manual inspection methods, while diligent, are labor-intensive, time-consuming, and often inaccurate. Current defect detection research often focuses solely on advanced algorithms, neglecting real-world factors, resulting in algorithms that excel in theory but falter in practice. Deep learning-based systems, on the other hand, use image recognition algorithms for automatic defect detection. Their holistic approach, balancing algorithm performance with adaptability to diverse scenarios, makes them effective in various industrial applications. This study introduces the use of YOLOv7, a renowned object detection algorithm, for chip defect detection. Trained on a diverse dataset, YOLOv7 offers accurate and efficient defect detection. This system boasts low error rates, diverse chip type detection, comprehensive defect recognition, and fast detection speeds. This deep learning-based inspection system rapidly and precisely identifies scratches and missing pins, enhancing chip quality and driving industrial progress.