Automated visual inspection in the semiconductor industry: A survey
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Novelty detection: a review—part 1: statistical approaches
Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.018 · Cited by 1,435 · see citing works
Novelty detection: a review—part 2:
Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.019 · Cited by 813 · see citing works
A survey on industrial vision systems, applications and tools
Malamas, E. N., Petrakis, E. G. M., Zervakis, M. E., Petit, L., & Legat, J.-D. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(02)00152-x · Cited by 798 · see citing works
Computer-Vision-Based Fabric Defect Detection: A Survey
Kumar, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.1930.896476 · Cited by 725 · see citing works
A Survey of Automated Visual Inspection
Newman, T. S., & Jain, A. K. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1017 · Cited by 619 · see citing works
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques
Xie, X. (2008) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.268 · Cited by 500 · see citing works
Automatic PCB Inspection Algorithms: A Survey
Moganti, M., Erçal, F., Dağli, C. H., & Tsunekawa, S. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0020 · Cited by 404 · see citing works
Automated Visual Inspection: A Survey
Chin, R. T., & Harlow, C. A. (1982) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1982.4767309 · Cited by 400 · see citing works
A neural-network approach to recognize defect spatial pattern in semiconductor fabrication
Chen, F., & Liu, S.-F. (2000) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/66.857947 · Cited by 230 · see citing works
Defect detection on semiconductor wafer surfaces
Shankar, N. G., & Zhong, Z. W. (2004) · Microelectronic Engineering · doi:10.1016/j.mee.2004.12.003 · Cited by 188 · see citing works
Automated visual inspection: 1981 to 1987
Chin, R. T. (1988) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(88)90108-9 · Cited by 174 · see citing works
Automatic surface inspection using wavelet reconstruction
Tsai, D., & Hsiao, B. (2001) · Pattern Recognition · doi:10.1016/s0031-3203(00)00071-6 · Cited by 147 · see citing works
Automatic defect inspection for LCDs using singular value decomposition
Lu, C.-J., & Tsai, D. (2004) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-003-1832-6 · Cited by 146 · see citing works
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Li, W.-C., & Tsai, D. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.025 · Cited by 127 · see citing works
A neural-network approach for semiconductor wafer post-sawing inspection
Su, C., Yang, T., & Ke, C.-M. (2002) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/66.999602 · Cited by 113 · see citing works
Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces
Tsai, D., & Luo, J.-Y. (2010) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2010.2092783 · Cited by 112 · see citing works
Automatic band selection for wavelet reconstruction in the application of defect detection
Tsai, D., & Chiang, C.-H. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00003-9 · Cited by 111 · see citing works
An adaptive level-selecting wavelet transform for texture defect detection
Han, Y., & Shi, P. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2006.07.028 · Cited by 101 · see citing works
Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion
Tsai, D., Chang, C.-C., & Chao, S.-M. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.08.001 · Cited by 94 · see citing works
*, B. C. J., Wang, C.-C., & Liu, H.-C. (2005) · International Journal of Production Research · doi:10.1080/00207540412331285832 · Cited by 88 · see citing works
Automatic optical inspection for detecting defects on printed circuit board inner layers
Rau, H., & Wu, C. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2299-9 · Cited by 84 · see citing works
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Wang, C.-H. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.02.014 · Cited by 83 · see citing works
A Shift-Tolerant Dissimilarity Measure for Surface Defect Detection
Tsai, D., Chiang, I.-Y., & Tsai, Y.-H. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2166797 · Cited by 83 · see citing works
Li, W., & Tsai, D. (2009) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2009.2034844 · Cited by 82 · see citing works
Real-time Industrial Visual Inspection: A Review
Thomas, A., Rodd, M. G., Holt, J. D., & Neill, C. J. (1995) · Real-Time Imaging · doi:10.1006/rtim.1995.1014 · Cited by 72 · see citing works
Serdaroglu, A., ERTÜZÜN, A. B., & Erçi̇l, A. (2006) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661806010196 · Cited by 69 · see citing works
Lin, H.-D. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2007.02.002 · Cited by 63 · see citing works
Independent component analysis-based defect detection in patterned liquid crystal display surfaces
Lu, C.-J., & Tsai, D. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2007.10.007 · Cited by 56 · see citing works
Defect detection in periodically patterned surfaces using independent component analysis
Tsai, D., & Lai, S.-C. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.02.011 · Cited by 51 · see citing works
Automatic inspection system of LED chip using two-stages back-propagation neural network
Kuo, C. J., Hsu, C.-T. M., Liu, Z., & Wu, H. (2013) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-012-0725-7 · Cited by 51 · see citing works
Tsai, D., & Hung, C. (2005) · International Journal of Production Research · doi:10.1080/00207540500140732 · Cited by 49 · see citing works
Separation of composite defect patterns on wafer bin map using support vector clustering
Wang, C.-H. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.057 · Cited by 49 · see citing works
A neural-network approach for an automatic LED inspection system
Chen, W., & Hsu, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.06.011 · Cited by 44 · see citing works
A novel defect detection and identification method in optical inspection
Xie, L., Huang, R., Gu, N., & Cao, Z. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1442-7 · Cited by 44 · see citing works
Clustered defect detection of high quality chips using self-supervised multilayer perceptron
Huang, C. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.07.011 · Cited by 43 · see citing works
Patterned wafer inspection by high resolution spectral estimation techniques
Khalaj, B. H., Aghajan, H. K., & Kailath, T. (1994) · Machine Vision and Applications · doi:10.1007/bf01211662 · Cited by 41 · see citing works
Automated SMD LED inspection using machine vision
Perng, D., Liu, H.-W., & Chang, C. (2011) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-011-3338-y · Cited by 38 · see citing works
Automatic detection of Mura defect in TFT-LCD based on regression diagnostics
Fan, S. S., & Chuang, Y.-C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.07.013 · Cited by 37 · see citing works
Detection of Spot-Type Defects on Liquid Crystal Display Modules
Kim, W. S., Kwak, D. M., Song, Y. C., Choi, D.-H., & Park, K. H. (2004) · Key engineering materials · doi:10.4028/www.scientific.net/kem.270-273.808 · Cited by 36 · see citing works
Automatic defect inspection system of colour filters using Taguchi-based neural network
Kuo, C. J., Hsu, C.-T. M., Fang, C.-H., Chao, S.-M., & Lin, Y.-D. (2012) · International Journal of Production Research · doi:10.1080/00207543.2012.695877 · Cited by 36 · see citing works
Wavelet analysis to fabric defects detection in weaving processes
Kim, S., Lee, M. H., & Woo, K.-B. (2003) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.1999.796918 · Cited by 35 · see citing works
A digital image-based measurement system for a LCD backlight module
Lin, C., Wu, W.-Z., Lay, Y., & Chang, M. (2001) · Optics & Laser Technology · doi:10.1016/s0030-3992(01)00069-x · Cited by 34 · see citing works
Directional textures auto-inspection using discrete cosine transform
Perng, D., & Chen, S. (2011) · International Journal of Production Research · doi:10.1080/00207543.2010.495087 · Cited by 33 · see citing works
Directional textures auto-inspection using principal component analysis
Chen, S., & Perng, D. (2011) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-010-3141-1 · Cited by 32 · see citing works
Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
Lin, H.-D., & Ho, D.-C. (2006) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-006-0614-3 · Cited by 31 · see citing works
Tsai, D., Chuang, S.-T., & Tseng, Y.-S. (2006) · International Journal of Production Research · doi:10.1080/00207540600622464 · Cited by 29 · see citing works
A Wavelet-Based Approach in Detecting Visual Defects on Semiconductor Wafer Dies
Yeh, C.-H., Wu, F.-C., Ji, W.-L., & Huang, C. (2010) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2010.2046108 · Cited by 29 · see citing works
Application of Two Hopfield Neural Networks for Automatic Four-Element LED Inspection
Chang, C., Li, C.-H., Lin, S.-Y., & Jeng, M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2013817 · Cited by 29 · see citing works
TFT-LCD Mura Defect Detection Using Wavelet and Cosine Transforms
Chen, S.-L., & Chou, S.-T. (2008) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2.441 · Cited by 24 · see citing works
Li, T.-S. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.07.040 · Cited by 23 · see citing works
Usuki, S., Nishioka, H., Takahashi, S., & Takamasu, K. (2009) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-008-1901-y · Cited by 18 · see citing works
LOGIC product yield analysis by Wafer Bin Map pattern recognition supervised neural network
Chen, F. L., Lin, S.-C., Doong, K. Y. Y., & Young, K. K.-L. (2004) · IEEE International Conference on ASIC (ASICON) · doi:10.1109/issm.2003.1243336 · Cited by 15 · see citing works
Saitoh, F. (1999) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1999.822906 · Cited by 15 · see citing works
Defect inspection system for patterned wafers based on the spatial-frequency filtering
Ohshige, T., Tanaka, H., Miyazaki, Y., Kanda, T., Ichimura, H., Kosaka, N., & Tomoda, T. (2002) · IEEE/CPMT International Electronics Manufacturing Technology Symposium · doi:10.1109/iemt.1991.279775 · Cited by 15 · see citing works
Fuzzy recognition of the defect of TFT-LCD
Zhang, Y., & Zhang, J. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.572556 · Cited by 15 · see citing works
Graphic supervisory control of an automatic optical inspection for LED properties
Fung, R., Yang, C., & Lai, C.-T. (2011) · Measurement · doi:10.1016/j.measurement.2011.04.003 · Cited by 11 · see citing works
A hybrid defect detection for in-tray semiconductor chip
Chen, C., Huang, C.-L., & Yeh, C.-W. (2012) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-012-4149-5 · Cited by 10 · see citing works
A Novel Defect Inspection Method for Semiconductor Wafer Based on Magneto-Optic Imaging
Pan, Z., Chen, L., Li, W., Zhang, G., & Wu, P. (2012) · Journal of Low Temperature Physics · doi:10.1007/s10909-012-0671-y · Cited by 6 · see citing works
Development of Wafer Bond Integrity Inspection System Based on Laser Transmittance
Jang, D.-Y., Ahn, H.-S., Mehdi, S. S. M., Lim, Y.-H., & Hong, S.-K. (2010) · Journal of the Microelectronics and Packaging Society · Cited by 1 · see citing works
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Cited by
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State of the Art in Defect Detection Based on Machine Vision
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00343-6 · Cited by 851 · see citing works
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
A review of machine learning for the optimization of production processes
Weichert, D., Link, P., Stoll, A., Stefan Rüping, A., Ihlenfeldt, S., & Wrobel, S. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03988-5 · Cited by 364 · see citing works
Ebayyeh, A. A. R. M. A., & Mousavi, A. (2020) · IEEE Access · doi:10.1109/access.2020.3029127 · Cited by 314 · see citing works
Cheon, S., Lee, H., Kim, C. O., & Lee, S. H. (2019) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2019.2902657 · Cited by 309 · see citing works
Recent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287 · see citing works
Application of automation for in-line quality inspection, a zero-defect manufacturing approach
Azamfirei, V., Psarommatis, F., & Lagrosen, Y. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.12.010 · Cited by 246 · see citing works
Data Management in Industry 4.0: State of the Art and Open Challenges
Raptis, T. P., Passarella, A., & Conti, M. (2019) · IEEE Access · doi:10.1109/access.2019.2929296 · Cited by 246 · see citing works
Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks
Chen, J.-F., Chen, W., Huang, C., Huang, S.-H., & Chen, A. (2016) · doi:10.1109/ccbd.2016.027 · Cited by 166 · see citing works
Self-citationResearch Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Fang, X., Luo, Q., Zhou, B., Li, C., & Tian, L. (2020) · Sensors · doi:10.3390/s20185136 · Cited by 166 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
Anomaly Detection Using Deep Learning Based Image Completion
Haselmann, M., Gruber, D. P., & Tabatabai, P. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00201 · Cited by 142 · see citing works
Defect Detection in Electronic Surfaces Using Template-Based Fourier Image Reconstruction
Tsai, D., & Huang, C.-K. (2018) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2018.2873744 · Cited by 139 · see citing works
A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing
Imoto, K., Nakai, T., Ike, T., Haruki, K., & Satō, Y. (2019) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2019.2941752 · Cited by 135 · see citing works
An intelligent vision-based approach for helmet identification for work safety
Wu, H., & Zhao, J.-S. (2018) · Computers in industry (Print) · doi:10.1016/j.compind.2018.03.037 · Cited by 109 · see citing works
Yu, J., Zheng, X., & Liu, J. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.04.015 · Cited by 96 · see citing works
1.1 The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
Dean, J. (2020) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc19947.2020.9063049 · Cited by 98 · see citing works
Wen, G., Gao, Z., Cai, Q., Wang, Y., & Mei, S. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3007292 · Cited by 94 · see citing works
A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition
Batool, U., Shapiai, M. I., Tahir, M., Ismail, Z. H., Zakaria, N. J., & Elfakharany, A. (2021) · IEEE Access · doi:10.1109/access.2021.3106171 · Cited by 95 · see citing works
Botero, U. J., Wilson, R. S., Lu, H., Rahman, M. T., Sathiaseelan, M. A. M., Ganji, F., Asadizanjani, N., Tehranipoor, M. M., Woodard, D. L., & Forte, D. J. (2021) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3464959 · Cited by 118 · see citing works
How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection
Johnson, T., Fletcher, S., Baker, W., & Charles, R. L. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2018.07.016 · Cited by 84 · see citing works
Machine Vision-Based Positioning and Inspection Using Expectation–Maximization Technique
Tsai, D., & Hsieh, Y.-C. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2717284 · Cited by 76 · see citing works
Luo, C. L., Yu, L., Yan, J., Li, Z., Ren, P., Bai, X. P., Yang, E., & Liu, Y. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12686 · Cited by 76 · see citing works
Industry 4.0 adoption key factors: an empirical study on manufacturing industry
Narula, S., Prakash, S., Dwivedy, M., Talwar, V., & Tiwari, S. P. (2020) · Journal of Advances in Management Research · doi:10.1108/jamr-03-2020-0039 · Cited by 76 · see citing works
Schlosser, T., Friedrich, M., Beuth, F., & Kowerko, D. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01906-9 · Cited by 74 · see citing works
Yuk, E. H., Park, S. H., Park, C.-S., & Baek, J. (2018) · Applied Sciences · doi:10.3390/app8060932 · Cited by 72 · see citing works
Image Based Quality Inspection in Smart Manufacturing Systems: A Literature Review
Babic, M., Farahani, M. A., & Wuest, T. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.10.042 · Cited by 70 · see citing works
Machine Learning and Imaging Informatics in Oncology
Tseng, H.-H., Wei, L., Cui, S., Luo, Y., Ten Haken, R. K., & El Naqa, I. (2020) · Oncology · doi:10.1159/000493575 · Cited by 64 · see citing works
One-Shot Recognition of Manufacturing Defects in Steel Surfaces
Deshpande, A. M., Minai, A. A., & Kumar, M. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.05.146 · Cited by 69 · see citing works
de la Rosa, F. L., Sánchez-Reolid, R., Gómez-Sirvent, J. L., Morales, R., & Fernández‐Caballero, A. (2021) · Applied Sciences · doi:10.3390/app11209508 · Cited by 67 · see citing works
Ge, C., Wang, J., Wang, J., Qi, Q., Sun, H., & Liao, J. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103232 · Cited by 68 · see citing works
A data-driven method for enhancing the image-based automatic inspection of IC wire bonding defects
Chen, J., Zhang, Z., & Wu, F. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1821928 · Cited by 62 · see citing works
A Comprehensive Review of Quality Control and Reliability Research in Micro–Nano Technology
Sharmile, N., Chowdhury, R. R., & Desai, S. S. (2025) · Technologies · doi:10.3390/technologies13030094 · Cited by 66 · see citing works
Leveraging computer vision towards high-efficiency autonomous industrial facilities
Yousif, I., Burns, L., Kalach, F. E., & Harik, R. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02396-1 · Cited by 57 · see citing works
Multisensor Image Fusion for Automated Detection of Defects in Printed Circuit Boards
Li, M., Yao, N., Liu, S., Li, S., Zhao, Y., & Kong, S. G. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3106057 · Cited by 55 · see citing works
Li, C., Zhang, X., Huang, Y., Tang, C., & Fatikow, S. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106530 · Cited by 54 · see citing works
Pitfalls and protocols of data science in manufacturing practice
Lee, C., & Chien, C.-F. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01711-w · Cited by 55 · see citing works
Automated detection of part quality during two-photon lithography via deep learning
Lee, X. Y., Saha, S. K., Sarkar, S., & Giera, B. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101444 · Cited by 53 · see citing works
Gigapixel imaging with a novel multi-camera array microscope
Thomson, E. E., Harfouche, M., Konda, P. C., Seitz, C. W., Kim, K., Cooke, C., Xu, S., Jacobs, W. S., Blazing, R. M., Chen, Y., et al. (2022) · eLife · doi:10.7554/elife.74988 · Cited by 54 · see citing works
Volkau, I., Mujeeb, A., Wenting, D., Erdt, M., & Sourin, A. (2019) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2019.00025 · Cited by 51 · see citing works
Real-Time H∞ Control of Networked Inverted Pendulum Visual Servo Systems
Du, D., Zhang, C., Song, Y., Zhou, H., Li, X., Fei, M., & Li, W. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2921821 · Cited by 47 · see citing works
Wang, J., Gao, P., Zhang, J., Lu, C., & Shen, B. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102513 · Cited by 46 · see citing works
Hu, Q., Hao, K., Wei, B., & Li, H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101608 · Cited by 46 · see citing works
Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks
Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3024603 · Cited by 45 · see citing works
Evaluation of the machine learning classifier in wafer defects classification
Jizat, J. A. M., Majeed, A. P. P. A., Nasir, A. F. A., Taha, Z., & Yuen, E. (2021) · ICT Express · doi:10.1016/j.icte.2021.04.007 · Cited by 44 · see citing works
A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach
Joshi, K. D., Chauhan, V., & Surgenor, B. W. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1438-3 · Cited by 43 · see citing works
A CNN-based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing
Imoto, K., Nakai, T., Ike, T., Haruki, K., & Satō, Y. (2018) · IEEE International Symposium on Semiconductor Manufacturing conference proceedings · doi:10.1109/issm.2018.8651174 · Cited by 43 · see citing works
The paradigm shift of mass customisation research
Kim, S., & Lee, K. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2081629 · Cited by 42 · see citing works
Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning
Pham, T. T. A., Thoi, D. K. T., Choi, H., & Park, S. (2023) · Sensors · doi:10.3390/s23063246 · Cited by 43 · see citing works
Computer vision defect detection on unseen backgrounds for manufacturing inspection
Mezher, A. M., & Marble, A. E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122749 · Cited by 47 · see citing works
Supervised contrastive learning for wafer map pattern classification
Bae, Y., & Kang, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107154 · Cited by 37 · see citing works
A Data-Flow Oriented Deep Ensemble Learning Method for Real-Time Surface Defect Inspection
Liu, Y., Gao, H., Guo, L., Qin, A., Cai, C., & You, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2957849 · Cited by 38 · see citing works
Nahar, L., Awrangjeb, M., & Islam, M. S. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104067 · Cited by 38 · see citing works
Chen, Y., Fan, C.-Y., & Chang, K.-H. (2016) · Computers & Industrial Engineering · doi:10.1016/j.cie.2016.05.009 · Cited by 36 · see citing works
Kang, S. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1447-2 · Cited by 36 · see citing works
Demystifying Defects: Federated Learning and Explainable AI for Semiconductor Fault Detection
Patel, T., Murugan, R., Yenduri, G., Jhaveri, R. H., Snoussi, H., & Gaber, T. (2024) · IEEE Access · doi:10.1109/access.2024.3425226 · Cited by 38 · see citing works
Madkhali, O. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad1e44 · Cited by 33 · see citing works
Quantifying and optimizing visualization: An evolutionary computing-based approach
Halim, Z., & Tufail, M. (2017) · Information Sciences · doi:10.1016/j.ins.2016.12.035 · Cited by 32 · see citing works
Automatic visual inspection: An approach with multi-instance learning
Mera, C., Orozco‐Alzate, M., Branch, J. W., & Mery, D. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.09.002 · Cited by 33 · see citing works
A weak supervision machine vision detection method based on artificial defect simulation
Li, C., Huang, Y., Li, H., & Zhang, X. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106466 · Cited by 31 · see citing works
Schlosser, T., Beuth, F., Friedrich, M., & Kowerko, D. (2019) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2019.8869311 · Cited by 30 · see citing works
Botero, U. J., Wilson, R., Lu, H., Rahman, M. T., Sathiaseelan, M. A. M., Ganji, F., Asadizanjani, N., Tehranipoor, M. M., Woodard, D. L., & Forte, D. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04210 · Cited by 29 · see citing works
Zhang, X., Jiang, Z., Yang, H. Y., Mo, Y., Zhou, L., Zhang, Y., Li, J., & Wei, S. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104136 · Cited by 28 · see citing works
Guidelines of Automated Optical Inspection (AOI) System Development
Liao, H., Lim, Z.-Y., Hu, Y., & Tseng, H. (2018) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2018.8600456 · Cited by 28 · see citing works
Pixel-Wise Defect Detection by CNNs without Manually Labeled Training Data
Haselmann, M., & Gruber, D. P. (2019) · Applied Artificial Intelligence · doi:10.1080/08839514.2019.1583862 · Cited by 27 · see citing works
Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision
Tian, H., Wang, D., Lin, J.-G., Chen, Q., & Liu, Z. (2020) · Sensors · doi:10.3390/s20164531 · Cited by 28 · see citing works
Ceramic tableware surface defect detection based on deep learning
Sun, P., Hua, C., Ding, W., Hua, C., Liu, P., & Lei, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109723 · Cited by 26 · see citing works
Deep open-set recognition for silicon wafer production monitoring
Frittoli, L., Carrera, D., Rossi, B., Fragneto, P., & Boracchi, G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108488 · Cited by 26 · see citing works
Adaptive Curvature-Guided Image Filtering for Structure + Texture Image Decomposition
Belyaev, A. G., & Fayolle, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2852741 · Cited by 26 · see citing works
Park, J., Choi, Y., Kwon, S., Lee, Y., Kim, J.-W., Kim, J.-J., Lee, J., Ahn, J., Kwak, H., Yang, Y., et al. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01469-3 · Cited by 25 · see citing works
Akhtar, M. B. (2022) · HighTech and Innovation Journal · doi:10.28991/hij-2022-03-01-01 · Cited by 24 · see citing works
Korodi, A., Anitei, D., Boitor, A., & Silea, I. (2020) · Sensors · doi:10.3390/s20123520 · Cited by 24 · see citing works
The implication and evaluation of geometrical imperfections on manufactured surfaces
Mullany, B. A., Savio, E., Haitjema, H., & Leach, R. (2022) · CIRP Annals · doi:10.1016/j.cirp.2022.05.004 · Cited by 22 · see citing works
Choi, W., Shin, R., Lim, J., & Kang, S. (2016) · Scientific Reports · doi:10.1038/srep33278 · Cited by 22 · see citing works
Batool, U., Shapiai, M. I., Mostafa, S. A., & Ibrahim, M. Z. (2023) · IEEE Access · doi:10.1109/access.2023.3321025 · Cited by 21 · see citing works
Digitalization of composite manufacturing using nanomaterials based piezoresistive sensors
Mazumder, M. R. H., Govindaraj, P., Salim, N. V., Antiohos, D., Fuss, F. K., & Hameed, N. (2024) · Composites Part A Applied Science and Manufacturing · doi:10.1016/j.compositesa.2024.108578 · Cited by 22 · see citing works
Decision Support System for Dimensional Inspection of Extruded Rubber Profiles
Vukićević, A. M., Djapan, M., Todorovic, P., Erić, M., Stefanovic, M. Z., & Mačužić, I. (2019) · IEEE Access · doi:10.1109/access.2019.2934561 · Cited by 20 · see citing works
Rosati, G., Faccio, M., Barbazza, L., & Rossi, A. (2015) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-015-7243-7 · Cited by 20 · see citing works
Improving the Performance of Automated Optical Inspection (AOI) Using Machine Learning Classifiers
Reshadat, V., & Kapteijns, R. A. J. W. (2021) · International Conference on Data and Software Engineering · doi:10.1109/icodse53690.2021.9648445 · Cited by 19 · see citing works
Du, Y., Chen, J., Zhou, H., Yang, X., Wang, Z., Zhang, J., Shi, Y., Chen, X., & Zheng, X. (2023) · Optics Communications · doi:10.1016/j.optcom.2023.129736 · Cited by 20 · see citing works
Liu, J., Tang, Z., Gui, W., Liu, W., Xu, P., & Zhu, J. (2016) · Journal of Process Control · doi:10.1016/j.jprocont.2016.04.014 · Cited by 18 · see citing works
Dimensional deviation measurement of ceramic tiles according to ISO 10545-2 using the machine vision
Emam, S. M., & Sayyedbarzani, S. A. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-2781-4 · Cited by 18 · see citing works
A Hybrid Deep Learning-Based Framework for Chip Packaging Fault Diagnostics in X-Ray Images
Wang, J., Li, G., Bai, H., Yuan, G., Li, X., Lin, B., Zhong, L., & Zhang, X. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3397360 · Cited by 16 · see citing works
Integrated Circuit Gold Wire Bonding Measurement Via 3-D Point Cloud Deep Learning
Xie, Q., Long, K., Lu, D., Li, D., Zhang, Y., & Wang, J. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3120473 · Cited by 16 · see citing works
Mirabian, S., Mohammadian, F., Ganji, Z., Zare, H., & Khalesi, E. H. (2025) · The Neuroradiology Journal · doi:10.1177/19714009251313511 · Cited by 15 · see citing works
Strengthening Small Object Detection in Adapted RT-DETR Through Robust Enhancements
Madan, M., & Reich, C. (2025) · Electronics · doi:10.3390/electronics14193830 · Cited by 15 · see citing works
Bilayer Markov Random Field Method for Detecting Defects in Patterned Fabric
Chang, X., Liu, W., Zhu, C. Z., Xiaohua, Z., & Gui, G. (2021) · Journal of Circuits Systems and Computers · doi:10.1142/s021812662250058x · Cited by 15 · see citing works
Rahman, N. N. S. A., Saad, N. M., Abdullah, A. R., & Ahmat, N. (2019) · Jurnal Teknologi · doi:10.11113/jt.v81.12505 · Cited by 15 · see citing works
Image Anomaly Detection by Aggregating Deep Pyramidal Representations
Mishra, P. K., Piciarelli, C., & Foresti, G. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68799-1_51 · Cited by 15 · see citing works
A Review: Absolute Linear Encoder Measurement Technology
Zhao, M., Yuan, Y., Luo, L., & Li, X. (2025) · Sensors · doi:10.3390/s25195997 · Cited by 14 · see citing works
Vison-Based 3D Shape Measurement System for Transparent Microdefect Characterization
Deng, Y., Pan, X., Wang, X., & Zhong, X. (2019) · IEEE Access · doi:10.1109/access.2019.2931194 · Cited by 14 · see citing works
Muresan, M. P., Cireap, D. G., & Giosan, I. (2020) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp51029.2020.9266249 · Cited by 14 · see citing works
Assessment of Aircraft Engine Blade Inspection Performance Using Attribute Agreement Analysis
Aust, J., & Pons, D. J. (2022) · Safety · doi:10.3390/safety8020023 · Cited by 16 · see citing works
Saif, Y. A. M., Rus, A. Z. M., Yusof, Y., Ahmed, M. L., Al-Alimi, S., Didane, D. H., Adam, A., Gu, Y. H., Al‐masni, M. A., & Abdulrab, H. Q. A. (2023) · Applied Sciences · doi:10.3390/app132011419 · Cited by 13 · see citing works
Wang, S., & Jiao, J. R. (2024) · Machines · doi:10.3390/machines12120873 · Cited by 14 · see citing works
Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets
Tung, H.-H., Cheng, C.-C., Chen, Y., Chen, Y.-F., Huang, S.-H., & Chen, A. (2016) · doi:10.1109/ccbd.2016.032 · Cited by 13 · see citing works
Self-citationBeuth, F., Schlosser, T., Friedrich, M., & Kowerko, D. (2020) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon43393.2020.9255234 · Cited by 13 · see citing works
Development of an Image Grating Sensor for Position Measurement
Fu, S., Cheng, F., Tjahjowidodo, T., & Liu, M. (2019) · Sensors · doi:10.3390/s19224986 · Cited by 13 · see citing works
Jang, J.-Y., & Yoon, S. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3003622 · Cited by 12 · see citing works
Next-generation Vision Inspection Systems: a pipeline from 3D model to ReCo file
Lupi, F., Freitas, N., Arvana, M., Rocha, A. D., Maffei, A., Barata, J., & Lanzetta, M. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02456-6 · Cited by 12 · see citing works
- Authors
- Huang, S.-H., & Pan, Y.-C.
- Affiliations
- National Tsing Hua University (TW)
- Corresponding author
- Huang, Szu-Hao
- ORCID
- Huang, Szu-Hao
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 66
- Pages
- 1-10
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W2092243497
- Cited by
- 307
- References
- 60
- Citations by year
- 2026: 20 · 2025: 33 · 2024: 35 · 2023: 33 · 2022: 30 · 2021: 38 · 2020: 42 · 2019: 23 · 2018: 26 · 2017: 8 · 2016: 15 · 2015: 4
- FWCI
- 16.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Industry (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- automated visual inspection, semiconductor manufacturing, optical inspection, image processing, defect detection, wafer inspection, thin-film transistor liquid crystal display, light-emitting diodes, projection method, learning-based approaches, hybrid algorithm, production line automation, quality control
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