ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.

Renbin Huang, Daohua Zhan, Xiuding Yang, Bei Zhou, Linjun Tang, Nian Cai, Han Wang, Baojun Qiu

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In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Renbin HuangSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Daohua ZhanSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Xiuding YangSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Bei ZhouSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Linjun TangSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Nian CaiSchool of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0002-7826-5055

Han WangSchool of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Baojun QiuChina Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 511370, China.

Funding

Jihua Laboratory Foundation of the Guangdong Province Laboratory of China X190071UZ190National Natural Science Foundation of China 62171142Natural Science Foundation of Guangdong Province 2021A1515011908

6 · The paper itself

Abstract

In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after packaging. However, the current traditional defect detection algorithm cannot meet the demands of high accuracy, fast speed, and real-time chip defect detection in industrial production. Therefore, this paper proposes a new multi-scale feature fusion module (ATSPPF) based on convolutional neural networks, which can more fully extract semantic information at different scales. In addition, based on this module, we design a deep learning model (ATNet) for detecting lead defects in chips. The experimental results show that at 8.2 giga floating point operations (GFLOPs) and 146 frames per second (FPS), mAP

Indexed as

chipsdeep learningdefectsX-ray images

Identifiers

PMID37512688

DOI10.3390/mi14071375

PMCPMC10384794

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.