基于深度学习的高精度晶圆缺陷检测方法研究

Research on high precision wafer defect detection based on deep learning

为了解决半导体制造领域缺陷检测中出现的检测效率低、错误率高、结果不稳定、成像精度低下导致无法精确地检测出不同种类的缺陷等问题,本文利用定制的CCD工业相机搭配高倍率的光学显微镜采集晶圆表面的扫描图像,结合改进的YOLOv4算法,实现了基于深度学习的高精度晶圆缺陷检测方法。实验表明,对于碳化硅晶圆缺陷,提出的方法模型可以识别各种复杂条件下的不同种类缺陷,具有良好的鲁棒性。对缺陷的平均识别精度达到99.24%,相较于YOLOv4-Tiny和原YOLOv4分别提升10.08%和1.92%。对缺陷的平均每图识别时间达到0.028 3 s,相较于基于Halcon软件方法和OpenCV模板匹配方法分别提升93.42%和90.52%,优于其他常规的晶圆缺陷检测方法,已实现在自主设计的验证系统和应用平台上稳定运行。

In order to solve the semiconductor manufacturing defect detection with low efficiency, the error rate is high, the result is not stable, imaging accuracy is low and cannot accurately detect the problem such as different kinds of defects. In this paper, by using a custom CCD industrial camera with a high ratio of optical microscope scan images on the surface of the wafer, combined with the improved YOLOv4 algorithm, a high precision wafer defect detection method based on deep learning is implemented. Experimental results show that the proposed model can identify different kinds of silicon carbide wafer defects under various complex conditions and has good robustness. The average accuracy of defect identification is 99.24%, which is about 10.08% and 1.92% higher than that of YOLOV4-Tiny and original YOLOv4, respectively. Compared with the Halcon-based method and OpenCV template matching method, the average recognition time of defects per graph reaches 0.028 3 s, which is about 93.42% and 90.52% higher than other conventional wafer defect detection methods and has realized stable operation in independently designed verification systems and application platform.