Automated surface defect inspection system for capacitive touch sensor

Nowadays, touch panel is used as the interface of many portable consumer electronic products, such as smart phone, digital camera, GPS, and notebook. To ensure the quality of touch panel, it is necessary to inspect the serious defects during the production process. The manufacturing processes of the capacitive touch panel are complicated. The touch sensor is one of the most important components because it directly defines the function of touch panels. The quality of the touch sensor will greatly influence the overall quality and cost of the touch panel. Regular textures can be found on the touch sensor, and it would increase the workload of manual inspection. The automated machine vision can be applied to improve these problems if a good defect detection algorithm can be provided. This research develops an automated surface defect inspection system for capacitive touch sensor by using several image processing methods. First, Fourier transformation and a multi band-pass filter is applied to filter out regular texture. Second, based on Canny edge detection, binarization, and morphology method, the defects can be detected. 60 touch sensor images of size 640×320 are tested. The average accuracy is 96.67% and the processing time is 0.15 seconds for each image.

[1]  Du-Ming Tsai,et al.  Independent component analysis-based defect detection in patterned liquid crystal display surfaces , 2008, Image Vis. Comput..

[2]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .

[3]  Chao-Ton Su,et al.  Parameter Optimization Design for Touch Panel Laser Cutting Process , 2012, IEEE Transactions on Automation Science and Engineering.

[4]  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).

[5]  Der-Baau Perng,et al.  Directional textures auto-inspection using principal component analysis , 2011 .

[6]  Du-Ming Tsai,et al.  One-dimensional-based automatic defect inspection of multiple patterned TFT-LCD panels using Fourier image reconstruction , 2007 .

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Du-Ming Tsai,et al.  Automatic Band Selection for Wavelet Reconstruction in the Application of Defect Detection , 2022 .

[9]  Du-Ming Tsai,et al.  Automatic defect inspection for LCDs using singular value decomposition , 2005 .

[10]  Steven Guan,et al.  A golden-template self-generating method for patterned wafer inspection , 2000, Machine Vision and Applications.

[11]  Hamid K. Aghajan,et al.  Patterned wafer inspection by high resolution spectral estimation techniques , 2005, Machine Vision and Applications.

[12]  Ajay Kumar,et al.  Computer-Vision-Based Fabric Defect Detection: A Survey , 2008, IEEE Transactions on Industrial Electronics.

[13]  N. G. Shankar,et al.  A rule-based computing approach for the segmentation of semiconductor defects , 2006, Microelectron. J..

[14]  Michael K. Ng,et al.  Wavelet based methods on patterned fabric defect detection , 2005, Pattern Recognit..

[15]  D.-M. Tsa,et al.  Automated Surface Inspection Using Gabor Filters , 1900 .

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

[17]  D. Tsai,et al.  Automated surface inspection using Gabor filters , 2000 .