[논문]D3PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

Park, Jin-Man; Yoo, Yong-Ho; Kim, Ue-Hwan; Lee, Dukyoung; Kim, Jong-Hwan

doi:10.1109/access.2020.3013291

[해외논문] D3PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology 원문보기

IEEE access : practical research, open solutions, v.8, 2020년, pp.140310 - 140322

Park, Jin-Man (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Yoo, Yong-Ho (Kohyoung Technology, Inc., Seoul, South Korea) , Kim, Ue-Hwan (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Lee, Dukyoung (Kohyoung Technology, Inc., Seoul, South Korea) , Kim, Jong-Hwan (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNN-based classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D3PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D3PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.

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DOI : 10.1109/ACCESS.2020.3013291 [무료]
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동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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