Principal component analysis based on wavelet characteristics applied to automated surface defect inspection

Automated visual inspection, a crucial manufacturing step, has been replacing the more time-consuming and less accurate human inspection. This research explores automated visual inspection of surface defects in a light-emitting diode (LED) chip. Commonly found on chip surface are water-spot blemishes which impair the appearance and functionality of LEDs. Automated inspection of water-spot defects is difficult because they have a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the defect may fall across two different background textures, which further increases detection difficulties. The one-level Haar wavelet transform is first used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based principal component analysis (WPCA) and Hotelling statistic (WHS) approaches are respectively applied to integrate the multiple wavelet characteristics. Finally, the principal component analysis of WPCA and the Hotelling control limit of WHS individually judge the existence of defects. Experimental results show that the proposed WPCA method achieves detection rates of above 93.8% and false alarm rates of below 3.6%, and outperforms other methods. A valid computer-aided visual defect inspection system is contributed to help meet the quality control needs of LED chip manufacturers.

[1]  Carlos E. Thomaz,et al.  A multivariate statistical analysis of the developing human brain in preterm infants , 2007, Image Vis. Comput..

[2]  Md. Khayrul Bashar,et al.  Wavelet transform-based locally orderless images for texture segmentation , 2003, Pattern Recognit. Lett..

[3]  Arivazhagan Selvaraj,et al.  Texture segmentation using wavelet transform , 2003, Pattern Recognit. Lett..

[4]  Hong-Dar Lin.  Computer-aided visual inspection of surface defects in ceramic capacitor chips , 2007 .

[5]  Du-Ming Tsai,et al.  DEFECT INSPECTION OF PATTERNED TFT-LCD PANELS USING A FAST SUB-IMAGE BASED SVD , 2004 .

[6]  N. G. Shankar,et al.  A clustering technique for defect inspection , 2005 .

[7]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[8]  Chi-Ho Chan,et al.  Fabric defect detection by Fourier analysis , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[9]  Tadashi Shibata,et al.  Automatic Defect Pattern Detection on LSI Wafers Using Image Processing Techniques , 1999 .

[10]  Ajay Kumar,et al.  Defect detection in textured materials using Gabor filters , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[11]  J. López-Higuera,et al.  Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks , 2007 .

[12]  Hong-Dar Lin,et al.  Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems , 2007 .

[13]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[14]  Robert M. Hodgson,et al.  Texture Measures for Carpet Wear Assessment , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Hong-Dar Lin,et al.  Tiny surface defect inspection of electronic passive components using discrete cosine transform decomposition and cumulative sum techniques , 2008, Image Vis. Comput..

[16]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[17]  Douglas C. Montgomery,et al.  A review of multivariate control charts , 1995 .

[18]  Lapo Governi,et al.  Development of an Artificial Vision Inspection System for Real-Time Defect Detection and Classification on Circular Knitting Machines , 2006 .

[19]  Aysin Ertüzün,et al.  An efficient method for texture defect detection: sub-band domain co-occurrence matrices , 2000, Image Vis. Comput..

[20]  Hong-Dar Lin,et al.  Computer-Aided Vision System for MURA-Type Defect Inspection in Liquid Crystal Displays , 2006, PSIVT.

[21]  Ajay Kumar,et al.  Neural network based detection of local textile defects , 2003, Pattern Recognit..

[22]  Melvin Alexander.  Applied Statistics and Probability for Engineers , 1995 .

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

[24]  Chenn-Jung Huang,et al.  Clustered defect detection of high quality chips using self-supervised multilayer perceptron , 2007, Expert Syst. Appl..

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

[26]  C. J. Weng,et al.  LED cosmetic flaw vision inspection system , 2005, Pattern Analysis and Applications.

[27]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[28]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[29]  Zhaowei Zhong,et al.  Defect detection on semiconductor wafer surfaces , 2005 .

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

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