计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220200092-6.doi: 10.11896/jsjkx.220200092
齐选龙1, 陈弘扬2, 赵文兵1, 赵地3,4, 高敬阳2
QI Xuanlong1, CHEN Hongyang2, ZHAO Wenbing1, ZHAO Di3,4, GAO Jingyang2
摘要: 内焊球空洞是BGA封装芯片的主要缺陷,可能会导致电气故障。目前,常用的检测方法是人工对照芯片X光影像检查,此类方法检测准确率低且时间、人力资源消耗大。因此,基于深度学习的自动化芯片缺陷检测方法越来越受到关注。芯片空洞检测与语义分割任务对应,但受限于数据缺乏高质量标注,模型准确率通常偏低,主动学习框架是潜在的解决方案。文中基于主动学习和U-Net++构建了芯片空洞率检测模型,通过等距划分将数据集分为多个子集,每个子集采用训练-预测-标注-扩展的框架循环优化U-Net++模型。在BGA封装芯片数据集上进行实验,模型分割平均Dice系数达到了80.99%,总体准确率达到了94.89%,达到了预定目标。首次将主动学习引入芯片检测领域,经实验验证可以有效提升芯片数据的标注水平,使得模型的分割准确率有所提高。
中图分类号:
[1]YUNUS M,SRIHARI K,PITARRESI J M,et al.Effect ofvoids on the reliability of BGA/CSP solder joints[J].Microelectronics Reliability,2003,43(12):2077-2086. [2]CHIU T C,ZENG K,STIERMAN R,et al.Effect of thermal aging on board level drop reliability for Pb-free BGA packages[C]//54th Electronic Components and Technology Conference.2004. [3]YANG T,ZHANG S S,JIANG F Z,et al.Brachial plexus ultra-sound image optimization based on deep learning and adaptive contrast enhancement[J].Computer Science,2019,46(11A):236-240. [4]PETERSON B,KWAN M,DUEWER F,et al.Optimizing x-ray inspection for advanaced packaging applications[C]//2020 International Wafer Level Packaging Conference.2020. [5]CHAO F,XIAOMINL,KOW J.3D package failure analysischallenge and solution[C]//2015 IEEE 17th Electronics Packaging and Technology Conference.2015. [6]VAGA R,BRYANT K.Recent advances in X-ray technology[C]//2016 Pan Pacific Microelectronics Symposium.2016. [7]GONG J,UME I C.Void inspection in lead-free solder bumps on ball grid array(BGA) packages using laser ultrasound technique[C]//ASME International Mechanical Engineering Congress and Exposition.2011. [8]LV H M,ZHAO D,CHI X B. Deep learning for early diagnosis ofalzheimer’s disease based on intensive AlexNet[J].Computer Science,2017,44(Z6):50-60. [9]CHEN S W,LIU Y J,LIU D,et al.AlexNet model and adaptivecontrast enhancement based ultrasound Image classification[J].Computer Science,2019,46(6A):146-152. [10]TABERNIK D,ELA S,SKVARC J,et al.Segmentation-based deep-learning approach for surface-defect detection[J].Journal of Intelligent Manufacturing,2020,31(3):759-776. [11]ZHANG Z Z,GAO J Y,LV G,et al.Pathological image classification of gastric cancer based on depth learning[J].Computer Science,2018,45(11A):263-268. [12]SAID A F,BENNETT B L,KARAM L J,et al.Automated void detection in solder balls in the presence of vias and other artifacts[J].IEEE Transactions on Components,Packaging and Manufacturing Technology,2012,2(11):1890-1901. [13]PENG S,NAM H D.Void defect detection in ball grid arrayX-ray images using a new blob filter[J].Journal of Zhejiang University Science C,2012,13(11):840-849. [14]WANG H,LIU D.A new threshold segmentation algorithm for segmenting micro-focus X-ray BGA solder joint image[C]//4th International Conference on Information Technology and Ma-nagement Innovation.2015. [15]VAN V M.Void detection in solder bumps with deep learning[J].Microelectronics Reliability,2018,88:315-320. [16]WANG F,WANG F.Rapidly void detection in TSVs with 2-D X-ray imaging and artificial neural networks[J].IEEE Transactions on Semiconductor Manufacturing,2014,27(2):246-251. [17]HE K,GKIOXARI G,DOLLAR P,et al.Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2017. [18]NEELURU V K,AHUJA V.Void region segmentation in ball grid array using u-net approach and synthetic data[J].arXiv:1907.04222,2019. [19]SCHIELE T,JANSCHE A,BERNTHALER T,et al.Comparison of deep learning-based image segmentation methods for the detection of voids in X-ray images of microelectronic components[C]//2021 IEEE 17th International Conference on Automation Science and Engineering.2021. [20]SETTLES B.Active learning literature survey[R].University of Wisconsin-Madison,2009. [21]ZHOU Z,RAHMAN S M M,TAJBAKHSH N,et al.UNet++:A nested u-net architecture for medical image segmentation[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2018. [22]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention.Cham:Springer,2015. [23]SU H Q,FU J C,GU H,et al.Parallel algorithm design for assisted diagnosis of prostate cancer[J].Computer Science,2019,46(11A):524-527. [24]GAO Q,GAO J Y,ZHAO D.GNNI U-net:precise segmentation neural network of left ventricular contours for MRI image based on group normalization and nearest interpolation[J].Computer Science,2020,47(8):213-220. [25]IGLOVIKOV V,SHVETS A.Ternausnet:U-net with vgg11encoder pre-trained on imagenet for image segmentation[J].arXiv:1801.05746,2018. [26]TAN M,LE Q.Efficientnet:Rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning.2019. [27]KAMBLE R,SAMANTA P,SINGHAL N.Optic disc,cup and fovea detection from retinal images using U-Net++with EfficientNet encoder[C]//International Workshop on Ophthalmic Medical Image Analysis.Cham:Springer,2020. [28]SONG Q,LI S,BAI Q,et al.Object detection method for gras-ping robot based on improved YOLOv5[J].Micromachines,2021,12(11):1273. [29]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020. [30]REZATOFIGHI H,TSOI N,GWAK J Y,et al.Generalized intersection over union:A metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019. [31]CHEN H Y,GAO J Y,ZHAO D,et al.LFSCA-UNet:liver fibrosis region segmentation network based on spatial and channel attention mechanisms[J].Journal of Image and Graphics,2021,26(9):2121-2134. [32]CHEN H Y,GAO J Y,ZHAO D,et al.Review of the research progress in deep learning and biomedical image analysis till 2020[J].Journal of Image of Graphics,2021,26(3):475-486. [33]REN P,XIAO Y,CHANG X,et al.A survey of deep activelearning[J].ACM Computing Surveys,2021,54(9):1-40. |
|