A Hybrid Deep Learning-Based Framework for Chip Packaging Fault Diagnostics in X-Ray Images

人工智能 稳健性(进化) 计算机科学 规范化(社会学) 炸薯条 计算机视觉 分割 模板匹配 模式识别(心理学) 故障检测与隔离 图像分割 深度学习 图像(数学) 人类学 社会学 基因 电信 生物化学 执行机构 化学
作者
Jie Wang,Gaomin Li,Haoyu Bai,Guixin Yuan,Xuan Li,Bin Lin,Lijun Zhong,Xiaohu Zhang
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (9): 11181-11191 被引量:1
标识
DOI:10.1109/tii.2024.3397360
摘要

In the testing of chips, defect diagnostics in X-ray images of packaging chips is mainly performed by humans, which is time-consuming and inefficient. To overcome the abovementioned problems, a novel intelligent defect diagnostics system based on hybrid deep learning for chip X-ray images was proposed. The system consists of four successive stages: image segmentation and normalization, image reconstruction and defect detection, contour matching, and qualification diagnosis. The first stage is used to localize the external contours of the target chip and remove extraneous backgrounds through the improved UNet. Then, considering the variety of defects and the complexity of labeling, an unsupervised learning model is designed to reconstruct defect-free images to detect defects, which requires only normal samples for training. Third, the multicomponent template matching based on structural prior is used to localize the internal contours of the chip. In the final stage, the qualification is diagnosed based on the previous results through the Floyd–Warshall algorithm. The effectiveness and robustness of the proposed methods are verified by experiments on real-world inspection lines. The experimental results demonstrate that the developed system can successfully perform fault diagnostics tasks, achieving a judgment accuracy of 92.5%.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
小袁发布了新的文献求助10
刚刚
刚刚
1秒前
1秒前
在水一方应助wjx采纳,获得10
1秒前
李奥发布了新的文献求助10
1秒前
Vera发布了新的文献求助10
2秒前
怕黑的纸鹤完成签到 ,获得积分10
4秒前
4秒前
4秒前
UUU完成签到 ,获得积分10
4秒前
5秒前
小二郎应助冷傲迎梦采纳,获得10
6秒前
6秒前
huihongzeng发布了新的文献求助10
7秒前
非常可爱发布了新的文献求助10
8秒前
Eastonlyzhang发布了新的文献求助10
8秒前
自由大叔发布了新的文献求助10
9秒前
阿龙发布了新的文献求助10
9秒前
10秒前
善学以致用应助Z可采纳,获得10
11秒前
11秒前
lily发布了新的文献求助50
11秒前
SUN完成签到,获得积分0
11秒前
12秒前
幽默的绣连完成签到,获得积分10
12秒前
12秒前
13秒前
重要的如天完成签到,获得积分20
14秒前
15秒前
Jasper应助雪白的山雁采纳,获得10
15秒前
15秒前
彭于晏应助酷酷巧蟹采纳,获得10
15秒前
15秒前
16秒前
16秒前
大模型应助tingting采纳,获得10
16秒前
16秒前
memedaaaah发布了新的文献求助10
16秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974882
求助须知:如何正确求助?哪些是违规求助? 3519431
关于积分的说明 11198315
捐赠科研通 3255698
什么是DOI,文献DOI怎么找? 1797904
邀请新用户注册赠送积分活动 877237
科研通“疑难数据库(出版商)”最低求助积分说明 806219