基于深度学习的自动光学检测在引线键合中的应用研究  

A Study on Application of Automatic Optical Inspection Based on Deep Learning in Wire Bonding

作　　者：孙伟[1] 张沅 胡永芳[1] 张伟[1] 王越飞 牛牧原 濮宬函 SUN Wei;ZHANG Yuan;HU Yongfang;ZHANG Wei;WANG Yuefei;NIU Muyuan;PU Chenghan(Nanjing Research Institute of Electronics Technology,Nanjing 210039,China;College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京电子技术研究所,江苏南京210039 [2]南京航空航天大学机电学院,江苏南京210016

出　　处：《电子机械工程》2025年第2期46-51,共6页Electro-Mechanical Engineering

摘　　要：在微波组件的微组装过程中,基于标准图像对比、灰阶二值化等传统算法的引线键合自动光学检测技术存在手动引线键合质量检测困难、复杂图像检测编程繁琐、需要大量人工复判等问题。文中基于深度学习算法构建面向机器视觉的引线键合质量智能检测模型,通过微小目标智能识别分类和基于注意力机制的旋转目标检测,实现焊点缺陷检测、不定形引线检测和更好的自适应引线键合缺陷识别。相比传统检测算法,在应用基于深度学习的自动光学检测模型后,误报率由4.97%下降至1.92%,人工复判工作量下降62%,同时,随着缺陷数据的积累和模型训练的迭代,基于深度学习的自动光学检测模型引线键合检测质量和检测效率将得到进一步提升。In the micro-assembly process of microwave modules,traditional algorithms such as standard image comparison and gray scale binaryzation used in automatic optical inspection(AOI)for wire bonding face challenges,including difficulties in quality inspection of manual wire bonding,cumbersome programming for complex image detection,and the need for extensive manual reevaluation.In this paper an intelligent inspection model for wire bonding quality in machine vision based on deep learning is proposed.By leveraging intelligent recognition and classification of micro-scale targets and rotating object detection with attention mechanisms,the model achieves solder joint defect detection,amorphous wire inspection,and adaptive defect recognition for wire bonding.Compared with traditional methods,the deep learning-based AOI model reduces the false alarm rate from 4.97%to 1.92%and decreases manual reevaluation workload by 62%.Furthermore,with the iterative accumulation of defect data and continuous model optimization,the proposed approach is expected to further enhance detection accuracy and operational efficiency for wire bonding quality control in industrial applications.

关 键 词：自动光学检测 深度学习 引线键合 

分 类 号：TN405.96[电子电信—微电子学与固体电子学] TH74[机械工程—光学工程]