1. bookVolumen 22 (2022): Heft 5 (October 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1335-8871
Erstveröffentlichung
07 Mar 2008
Erscheinungsweise
6 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Automatic Detection of Chip Pin Defect in Semiconductor Assembly Using Vision Measurement

Online veröffentlicht: 05 Aug 2022
Volumen & Heft: Volumen 22 (2022) - Heft 5 (October 2022)
Seitenbereich: 231 - 240
Eingereicht: 25 Nov 2021
Akzeptiert: 22 May 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1335-8871
Erstveröffentlichung
07 Mar 2008
Erscheinungsweise
6 Hefte pro Jahr
Sprachen
Englisch

[1] Zhang, Z.F., Liu, Y., Wu, X.S., Kan, S.L. (2014). Integrated color defect detection method for polysilicon wafers using machine vision. Advances in Manufacturing, 2, 318-326. https://doi.org/10.1007/s40436-014-0095-9 Search in Google Scholar

[2] Tout, K., Meguenani, A., Urban, J.P., Cudel, C. (2021). Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks. The International Journal of Advanced Manufacturing Technology, 112, 3307-3326. https://doi.org/10.1007/s00170-020-06467-4 Search in Google Scholar

[3] Zhou, P., Wang, Z.G., Yan, Y., Huang, N., Kang, R.K., Guo, D.M. (2020). Sensitivity analysis of the surface integrity of monocrystalline silicon to grinding speed with same grain depth-of-cut. Advances in Manufacturing, 8, 97-106. https://doi.org/10.1007/s40436-020-00291-5 Search in Google Scholar

[4] Song, J.D., Kim, Y.G., Park, T.H. (2019). SMT defect classification by feature extraction region optimization and machine learning. The International Journal of Advanced Manufacturing Technology, 101, 1303-1313. https://doi.org/10.1007/s00170-018-3022-6 Search in Google Scholar

[5] Acciani, G., Brunetti, G., Fornarelli, G. (2006). Application of neural networks in optical inspection and classification of solder joints in surface mount technology. IEEE Transactions on Industrial Informatics, 2 (3), 200-209. https://doi.org/10.1109/TII.2006.877265 Search in Google Scholar

[6] Liu, C., Chang, L. (2019). Characterization of surface micro-roughness by off-specular measurements of polarized optical scattering. Measurement Science Review, 19 (6), 257-263. https://doi.org/10.2478/msr-2019-0033 Search in Google Scholar

[7] Gao, H., Jin, W., Yang, X., Kaynak, O. (2017). A line-based-clustering approach for ball grid array component inspection in surface-mount technology. IEEE Transactions on Industrial Electronics, 64 (4), 3030-3038. https://doi.org/10.1109/TIE.2016.2643600 Search in Google Scholar

[8] Liu, G., Tong, H., Li, Y., Zhong, H., Tan, Q. (2021). A profile shaping and surface finishing process of micro electrochemical machining for microstructures on microfluidic chip molds. The International Journal of Advanced Manufacturing Technology, 115, 1621-1636. https://doi.org/10.1007/s00170-021-07264-3 Search in Google Scholar

[9] Acciani, G., Fornarelli, G., Giaquinto, A. (2011). A fuzzy method for global quality index evaluation of solder joints in surface mount technology. IEEE Transactions on Industrial Informatics, 7 (1), 115-124. https://doi.org/10.1109/TII.2010.2076292 Search in Google Scholar

[10] Liu, S., Ume, I., Achari, A. (2004). Defects pattern recognition for flip-chip solder joint quality inspection with laser ultrasound and Interferometer. IEEE Transactions on Electronics Packaging Manufacturing, 27 (1), 59-66. https://doi.org/10.1109/TEPM.2004.830515 Search in Google Scholar

[11] Yang, J., Ume, I.C. (2010). Laser ultrasonic technique for evaluating solder bump defects in flip chip packages using modal and signal analysis methods. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 57 (4), 920-932. https://doi.org/10.1109/TUFFC.2010.149620378454 Search in Google Scholar

[12] Liu, S., Erdahl, D., Ume, I., Achari, A., Gamalski, J. (2001). A novel approach for flip chip solder joint quality inspection: Laser ultrasound and interferometric system. IEEE Transactions on Components and Packaging Technologies, 24 (4), 616-624. https://doi.org/10.1109/6144.974950 Search in Google Scholar

[13] Han, B., Yi, M. (2018). A template matching based method for surface-mount rectangular -pin-chip positioning and defect detection. In 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 1009-1014. https://doi.org/10.1109/IMCCC.2018.00212 Search in Google Scholar

[14] Schmidt, C. (2018). 3-D X-Ray imaging with nanometer resolution for advanced semiconductor packaging FA. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8 (5), 745-749. https://doi.org/10.1109/TCPMT.2018.2827058 Search in Google Scholar

[15] Lee, J., Han, C., Ko, K., Lee, S. (2016). Development of vision system for defect inspection of electric parts in the tape and reel package. In 2016 16th International Conference on Control, Automation and Systems (ICCAS). IEEE, 437-439. https://doi.org/10.1109/ICCAS.2016.7832357 Search in Google Scholar

[16] Balter, M., Chen, A., Maguire, T., Yarmush, M. (2017). Adaptive kinematic control of a robotic venipuncture device based on stereo vision, ultrasound, and force guidance. IEEE Transactions on Industrial Electronics, 64 (2), 1626-1635. https://doi.org/10.1109/TIE.2016.2557306524093728111492 Search in Google Scholar

[17] Tao, X., Zhang, Z., Zhang, F., Xu, D. (2015). A novel and effective surface flaw inspection instrument for large-aperture optical elements. IEEE Transactions on Instrumentation and Measurement, 64 (9), 2530-2540. https://doi.org/10.1109/TIM.2015.2415092 Search in Google Scholar

[18] Xu, C., Yang, X., He, Z., Qiu, J., Gao, H. (2021). Precise positioning of circular mark points and transistor components in surface mounting technology applications. IEEE Transactions on Industrial Informatics, 17 (4), 2534-2544. https://doi.org/10.1109/TII.2020.2999023 Search in Google Scholar

[19] Kurylo, P., Pivarčiová, E., Cyganiuk, J., Frankovský, P. (2019). Machine vision system measuring the trajectory of upper limb motion applying the Matlab software. Measurement Science Review, 19 (1), 1-8. https://doi.org/10.2478/msr-2019-0001 Search in Google Scholar

[20] Che, J., Sun, Y., Jin, X., Chen, Y. (2021). 3D measurement of discontinuous objects with optimized dual-frequency grating profilometry. Measurement Science Review, 21 (6), 197-204. https://doi.org/10.2478/msr-2021-0027 Search in Google Scholar

[21] Di Leo, G., Liguori, C., Pietrosanto, A., Sommella, P. (2017). A vision system for the online quality monitoring of industrial manufacturing. Optics & Lasers in Engineering, 89,162-168. https://doi.org/10.1016/j.optlaseng.2016.05.007 Search in Google Scholar

[22] Harris, C., Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the 4th ALVEY Vision Conference. Manchester, UK: Alvety Vision Club, 147-151. http://dx.doi.org/10.5244/c.2.2310.5244/C.2.23 Search in Google Scholar

[23] Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision & Image Understanding, 110 (3), 346-359. https://doi.org/10.1016/j.cviu.2007.09.014 Search in Google Scholar

[24] Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60 (2), 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94 Search in Google Scholar

[25] Zhang, X. Wang, H. Smith, A., Ling, X., Lovell, B.C., Yang, D. (2010). Corner detection based on gradient correlation matrices of planar curves. Pattern Recognition, 43 (4), 1207-1223. https://doi.org/10.1016/j.patcog.2009.10.017 Search in Google Scholar

[26] Zhao, Y.J., Yan, Y.H., Song, K.C. (2017). Vision-based automatic detection of steel surface defects in the cold rolling process: Considering the influence of industrial liquids and surface textures. International Journal of Advanced Manufacturing Technology, 90, 1665-1678. https://doi.org/10.1007/s00170-016-9489-0 Search in Google Scholar

[27] Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (11), 1330-1334. https://doi.org/10.1109/34.888718 Search in Google Scholar

[28] Rosten, E., Porter, R., Drummond, T. (2010). Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (1), 105-119. https://doi.org/10.1109/TPAMI.2008.27519926902 Search in Google Scholar

[29] Calonder, M., Lepetit, V., Strecha, C., Fua, P. (2010). BRIEF: Binary robust independent elementary features. In Computer Vision – ECCV 2010. Springer, LNCS 6314, 778-792. https://doi.org/10.1007/978-3-642-15561-1_56 Search in Google Scholar

[30] Rublee, E., Rabaud, V., Konolige, K., Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 International Conference on Computer Vision. IEEE, 2564-2571. https://doi.org/10.1109/ICCV.2011.6126544 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo