印刷電路板檢測已經發展多年，但仍有許多地方有待提升，如縮短檢測時間、降低錯誤分類和缺陷漏失率等。
本研究將提出一套較為強健且檢測時間較短的檢測系統。此系統將分為線上缺陷檢測及離線缺陷分類兩部分。文獻中之相關係數法、灰階分區統計指標、誤差分佈誤差總和指標、高灰階差值像素比例指標將被分析之，並提出指標挑選方法以幫助尋找較好的檢測指標組合。另外，我們也將提出新的指標以達到系統需求並提升系統效能。由於缺陷分類較為困難，類神經網路也許將會使用於系統上。
當系統設置完成，為了測試系統的可靠性，大量的線上檢測影像將會被用於測試機台的可靠性。

PCB inspection has been developed for many years, but it still needs some improvement such as, inspection time, incorrect, and fault miss rate etc.
This paper will propose a more robust and faster PCB inspection system. The inspection system will be divided into two stages, namely, defects detection and defects classification. In order to find better inspection combination of indices, there are pattern matching method, regional gray level index (IL2), high gray level difference index (IT1), histogram subtraction index (IL1), run-length index, and projection to be analyzed and we will propose a new method to choose index. Because defects classification is more difficult, neural network may be adopted in inspection system. Furthermore, we will propose new indices in order to improve inspection system.
After inspection system is finished, there will be lots of online images to verify the feasibility of system.

[1] Optima 7300 post-reflow automated optical inspectionsystem manual, Teradyne Inc. , Agoura Hills, CA.
[2] R. T. Chin, “Automated visual inspection:a survey,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4(6), pp. 557–573, 1982.
[3] R. T. Chin, “Survey: automated visual inspection: 1981 to 1987,” Computer Vision Graphics Image Process, Vol. 41, pp. 346–381, 1988.
[4] M. Moganti and F. Ercal, “Automatic PCB inspection algorithms : a survey,” Computer Vision and Image Understanding, Vol. 63, No. 2, pp. 287-313, March 1996.
[5] H. H. Loh, and M. S. Lu, “Printed circuit board inspection using image analysis,” IEEE Transactions on Industry Applications, Vol. 35, No.2, pp. 426-432, 1999.
[6] R. Jain, R. Kasturi, and B. G. Schunk, “Machine Vision,” New York:McGraw-Hill, 1995.
[7] E. K. Teoh, D. P. Mital, B. W. Lee, and L. K. Wee, “Automated visual inspection of surface mount PCBs,” Industrial Electronics Society, 16th Annual Conference of IEEE, Vol. 1, pp. 576-580, 1990.
[8] Z. Ibrahim, Al-Attas, S. A. R. , Z. Aspar, “Analysis of the wavelet-based image difference algorithm for PCB inspection,” SICE 2002.Proceedings of the 41st SICE Annual Conference, Vol.4, pp.2108–2113, 5-7 Aug. 2002.
[9] Y. H. Lin, “The estimation for algorithms of visual inspection of surface mount PCBs,” Master Thesis, National Tsing Hua University, Taiwan (in Chinese), 2003.
[10] T. H. Kim, T. H. Cho, Y. S. Mon, and S. H. Park, “Visual inspection for the classification of solder joints,” Pattern Recognition, Vol. 32, pp. 565-575, 1999.
[11] L. V. Fausett, “Fundamentals of neural networks,” Prentice-Hall, Englewood, Cliffs, NJ, 1994.
[12] K. Tarun, and T. Khanna, “Foundations of neural networks,” Reading, Mass. :Addison-Wisley, 1990.
[13] P. Piction, “Introduction to neural networks,” Lon- don:Macillan, 1994.
[14] D. B. Perng, G. Y. Peng, C. J. Yan, and C. C. Chou, “A new machine vision system for SMD PCB auto-inspection,” The International Symposium on Measurement Technology & Intelligent Instrument ISMTII’ 2001, Cairo, Egypt, 25 – 29 Sept. 2001.
[15] D. Demir, S. Birecik, F. Kurugollu, M. Sezgin, I. O. Bucak, B. Sankur, and E. Anarim, “Quality inspection in PCBs and SMDs using computer vision techniques,” Industrial Electronics, Control and Instrumentation, 1994, 20th International Conference on Vol. 2, pp. 857–861, 5-9 Sept. 1994.
[16] W. Y. Wu, M. J. Wang, and C. M. Liu, “Automated inspecttion of printed circuit board through machine vision,” Computer in Industry, Vol. 28, No. 2, pp. 103-111, May 1996.
[17] D. L. Tsai, C. H. Tsai and S. C. Lin, “Using neural network for computer vision inspection of printed circuit board,” Proc. 4th Conference on Precision Machinery and Manufacturing, A03-004, Taipei, 2004.