基于卷积去噪自编码器的芯片表面弱缺陷检测方法

计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 118-125.doi: 10.11896/jsjkx.190100141

• 计算机图形学&多媒体 • 上一篇下一篇

基于卷积去噪自编码器的芯片表面弱缺陷检测方法

罗月,童卞,景帅,张蒙,饶永明,闫峰

(合肥工业大学计算机与信息学院合肥230601)

收稿日期:2019-01-17 出版日期:2020-02-15 发布日期:2020-03-18
通讯作者: 罗月童(ytluo@hfut.edu.cn)
基金资助:
国家重点研发计划(2017YFB1402200);安徽省科技强警计划项目(1604d0802009);浙江大学CAD&CG国家重点实验室开放课题(A1814);中央高校基本科研业务费专项资金(JZ2017HGBH0915);安徽省高等学校省级质量工程项目(2017jyxm0045)

Detection Method of Chip Surface Weak Defect Based on Convolution Denoising Auto-encoders

LUO Yue-tong,BIAN Jing-shuai,ZHANG Meng,RAO Yong-ming,YAN Feng

(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)

Received:2019-01-17 Online:2020-02-15 Published:2020-03-18
About author:LUO Yue-tong,born in 1978,Ph.D,professor,master supervisor,is member of China Computer Federation (CCF).His main research interests include visual analytic,computer vision and automated optical inspection.
Supported by:
This work was supported by the National Key Research and Development Plan of China (2017YFB1402200), Strengthen Police with Science and Technology Project of Anhui, China (1604d0802009), State Key Laboratory of CAD& CG, Zhejiang University (A1814), Fundamental Research Funds for the Central Universities of Ministry of Education of China (JZ2017HGBH0915) and Provincial Quality Engineering Project of the Higher Education Institutions of Anhui Province, China (2017jyxm0045).

摘要/Abstract

摘要： 芯片表面缺陷会影响芯片的外观和性能,因此表面缺陷检测是芯片生产过程中的重要环节。具有缺陷与背景对比度低、缺陷较小等特点的弱缺陷给传统检测方法带来了挑战。因为近年来深度学习在机器视觉领域展现出了强大的能力,所以文中采用基于深度学习的方法来研究芯片表面弱缺陷的检测问题。该方法将芯片表面缺陷看作噪音,首先应用卷积去噪自编码器(Convolutional Denoising Auto-encoders,CDAE)重构无缺陷图像,然后用重构的无缺陷图像减去输入图像,获得包含缺陷信息的残差图。因为残差图中已经消除了背景的影响,所以最后可以基于残差图较容易地进行缺陷检测。由于基于CDAE重构芯片背景的无缺陷图像时存在随机噪音,导致弱缺陷可能会湮没在重构噪音中,为此,文中提出了重叠分块策略抑制重构噪音,以便更好地检测弱缺陷。因为CDAE是无监督学习网络,所以训练时无需进行大量的人工数据标注,这进一步增强了该方法的可应用性。通过对真实芯片表面数据进行测试,验证了所提方法在芯片表面检测上的有效性。

关键词: 卷积去噪自编码器, 缺陷检测, 深度学习, 无监督学习, 芯片表面缺陷

Abstract: Chip surface defects can affect the appearance and performance of the chip.Therefore,surface defect detection is an important part of the chip production process.The automatic detection method based on machine vision attracts much attention because of its advantages of low cost and high efficiency.Weak defects such as low contrast between defects and background and small defects,bring challenges to traditional detection methods.Because deep learning has shown strong capabilities in the fields of machine vision in recent years,this paper studied the detection of weak defects on the chip surface by using the method based on deep learning.Chip surface defects were regarded as noise in this menthod.Firstly,convolutional denoising auto-encoders (CDAE) is applied to reconstruct the image without defect.Then,the reconstructed image without defect is used to subtract the input image,thus obtaining the residual image with defect information.Because the influence of background has been eliminated from the residual diagram,it is easier to detect defects based on the residual diagram.Because of the random noise in the process of reconstructing defect-free image from chip background image based on CDAE,the weak defect may be lost in the reconstructed noise.Therefore,this paper proposed an overlapping block strategy to suppress the reconstructed noise,so as to better detect the weak defect.Because CDAE is an unsupervised learning network,there is no need to perform a large amount of manual data annotation during training,which further enhances the applicability of the method.By using the real chip surface data provided by the paper partner,the effectiveness of the proposed method in chip surface detection is verified.

Key words: Chip surface defects, Convolution denoising auto-encoders, Deep learning, Defect detection, Unsupervised learning

中图分类号:

TP391

罗月,童卞,景帅,张蒙,饶永明,闫峰. 基于卷积去噪自编码器的芯片表面弱缺陷检测方法[J]. 计算机科学, 2020, 47(2): 118-125. https://doi.org/10.11896/jsjkx.190100141

LUO Yue-tong,BIAN Jing-shuai,ZHANG Meng,RAO Yong-ming,YAN Feng. Detection Method of Chip Surface Weak Defect Based on Convolution Denoising Auto-encoders[J]. Computer Science, 2020, 47(2): 118-125. https://doi.org/10.11896/jsjkx.190100141

参考文献

[1]CHEN K.Research on key techniques for integrated circuit chip surface defects vision detection[D].Nanjing:Southeast University,2016.
[2]RADOVAN S,PAPADOPOULOS G D,GEORGOUDAKIS M,et al.Vision system for finished fabric inspection[C]∥Machine Vision Applications in Industrial Inspection X.San Jose,California:International Society for Optics and Photonics,2002:97-104.
[3]SILVÉN O,NISKANEN M,KAUPPINEN H.Wood inspection with non-supervised clustering[J].Machine Vision and Applications,2003,13(5／6):275-285.
[4]SINGHKA D K H,NEOGI N,MOHANTA D.Surface defect classification of steel strip based on machine vision[C]∥2014 International Conference on Computer and Communications Technologies (ICCCT).Hyderabad,India:IEEE,2014:1-5.
[5]CHIOU Y C,LIN C S,CHIOU B C.The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas[J].Expert Systems with Applications,2008,35(4):1771-1779.
[6]DONG X F,HAN Z Y,LIAO S Y,et al.Study on Semiconductor Surface Defect Detection Based on Machine Vision[J].Metrology & Measurement Technology,2014,34(5):22-24,49.
[7]SU L,SHI T,DU L,et al.Genetic algorithms for defect detection of flip chips[J].Microelectronics Reliability,2015,55(1):213-220.
[8]FENG L,GONG Z H.An algorithm for chip surface defect detection based on sequential similarity and light source automatic adjustment[J].Modern Electronics Technique,2017(5):58-62.
[9]BENGIO Y,LECUN Y.Scaling learning algorithms towards AI[C]∥Large-Scale Kernel Machines.Hayward Street,Cambridge:MIT Press,2007:321-359.
[10]BENGIO Y,DELALLEAU O.On the expressive power of deep architectures[C]∥International Conference on Algorithmic Learning Theory.Germany:Springer-Verlag,2011:18-36.
[11]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[12]FENG C,LIU M Y,KAO C C,et al.Deep Active Learning for Civil Infrastructure Defect Detection and Classification[M]∥Computing in Civil Engineering 2017.Berlin:Springer,2017:298-306.
[13]ZHANG A,WANG K C P,LI B,et al.Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network[J].Computer-Aided Civil and Infrastructure Engineering,2017,32(10):805-819.
[14]REN R,HUNG T,TAN K C.A generic deep-learning-based approach for automated surface inspection[J].IEEE transactions on cybernetics,2018,48(3):929-940.
[15]WANG X B,LI J,YAO H M,et al.Solar Cells Surface Defects Detection Based on Deep Learning[J].Pattern Recognition and Artificial Intelligence,2014,27(6):517-523.
[16]LI Y,ZHAO W,PAN J.Deformable patterned fabric defect detection with Fisher criterion-based deep learning[J].IEEE Transactions on Automation Science and Engineering,2017,14(2):1256-1264.
[17]MEI S,YANG H,YIN Z.An unsupervised-learning-based approach for automated defect inspection on textured surfaces[J].IEEE Transactions on Instrumentation and Measurement,2018,67(6):1266-1277.
[18]VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising autoencoders[C]∥Proceedings of the 25th International Conference on Machine Learning.New York:ACM,2008:1096-1103.
[19]VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion[J].Journal of machine learning research,2010,11(12):3371-3408.
[20]PATHAK D,KRAHENBUHL P,DONAHUE J,et al.Context encoders:Feature learning by inpainting[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,USA:IEEE,2016:2536-2544.
[21]STRUB F,MARY J,GAUDEL R.Hybrid Collaborative filtering with autoencoders[J].arXiv:1603.00806,2016.
[22]OTSU N.A threshold selection method from gray-level histograms[J].IEEE Transactions on Systems,Man,and Cyberneti-cs,1979,9(1):62-66.

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