Automated SMD LED inspection using machine vision

Light-emitting diodes (LED) are used in many different applications. However, some LED defects are unavoidable in large-volume fabrication and taping processes. These defects may include missing components, incorrect orientations, inverse polarity, mouse bites, missing gold wires, and surface stains. Human visual inspection has traditionally been used in LED-packaging factories. However, it is subjective, time consuming, and lacking consistent inspection results. This paper proposes a machine vision system combining an automatic system-generated inspection regions (IR) method to inspect two types of LED surface-mounted devices (SMDs). Experimentation revealed that the proposed automatic inspection method could successfully detect defects with up to 95% accuracy for both types (Types 1 and 2) of SMD LEDs. The online inspecting speed was on average under 0.3 s per image.

[1]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[2]  Bernard C. Jiang,et al.  Machine vision and background remover-based approach for PCB solder joints inspection , 2007 .

[3]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[4]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[5]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[6]  Michel Jourlin,et al.  A new minimum variance region growing algorithm for image segmentation , 1997, Pattern Recognit. Lett..

[7]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[8]  Zude Zhou,et al.  An online defects inspection method for float glass fabrication based on machine vision , 2008 .

[9]  Anil K. Jain,et al.  A Survey of Automated Visual Inspection , 1995, Comput. Vis. Image Underst..

[10]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[11]  Garrett M. Johnson,et al.  Color Imaging: Fundamentals and Applications , 2008 .

[12]  Roger M. Sauter,et al.  Introduction to Statistical Quality Control (2nd ed.) , 1992 .

[13]  Euripides G. M. Petrakis,et al.  A survey on industrial vision systems, applications, tools , 2003, Image Vis. Comput..

[14]  Paulo Carvalho,et al.  An automatic optical sensor for vessels and fibbers quality inspection in pulp production , 1999 .

[15]  Der-Baau Perng,et al.  A novel internal thread defect auto-inspection system , 2010 .

[16]  Azriel Rosenfeld,et al.  Digital Picture Processing, Volume 1 , 1982 .

[17]  Fuh-Shyang Juang,et al.  Nonspherical LED packaging lens for uniformity improvement , 2009 .

[18]  Shu-Ming Lee,et al.  Design and development of a new machine vision wire bonding inspection system , 2007 .

[19]  MuDer Jeng,et al.  Application of Two Hopfield Neural Networks for Automatic Four-Element LED Inspection , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  Markus Ulrich,et al.  Machine Vision Algorithms and Applications , 2007 .

[21]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[22]  Xavier Cufí,et al.  Strategies for image segmentation combining region and boundary information , 2003, Pattern Recognit. Lett..

[23]  Wen-Yen Wu,et al.  Automated inspection of printed circuit boards through machine vision , 1996 .

[24]  H. Rau,et al.  Automatic optical inspection for detecting defects on printed circuit board inner layers , 2005 .

[25]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[26]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .