在印刷電路版的組裝業中，雖然技術不斷的成熟、提升，但製程當中仍存有許多瑕疵，大致上分為四大類，包括有缺件、歪斜、反向、錫誤四種。本研究針對片狀電阻、片狀電容等元件做瑕疵檢測。
本研究應用文獻中之灰階分區統計法、相關係數法、白點統計法，以及吾人所提出之高灰階差值像素比例法(一)、高灰階差值像素比例法(二)等五種演算法，以實際生產線上的表面黏著元件(SMD)為樣本做檢測並分析各個演算法的優缺點。由於單一演算法並無法完全有效的檢測出所有的元件是否合格，且在分類缺陷時亦相當困難，因此本研究藉由類神經網路來整合各個演算法的優點，並將檢測的結果以誤判率、漏失率、正確率、檢測時間四個項目來評估其檢測效果。
由檢測的結果可得知，有5個輸入參數(Input)的神經網路會有較佳的檢測結果，其誤判率以及漏失率皆為0，所以可以有效的辨別元件是否合格。因此藉由類神經網路的輔助，不但能正確的辨別元件是否合格，且在分類不合格元件的效果也比單一指標來的佳。缺點在於仍無法將不合格元件的缺陷種類完全分辨出來，但這個問題是可以忽略的。

The SMT Machine in PCB Assembly has been improving rapidly in the past decades. However, there are still defects occurring during the working process. In general, the defects can be classified into four categories, such as component missing, misalignment, counter polarity, and solder defect.
This paper focuses on chip resistor and chip capacity inspection. We apply the algorithms in the technical literatures, such as Gray statistics method, Pattern matching method, Extreme point method and two algorithms that we proposed to inspect the on-line SMD samples.
Due to the fact that single algorithm cannot differentiate completely to determine whether the SMD samples are qualified and it is difficult to classify the defects of SMD samples; therefore, we integrate the advantages of these five algorithms by Neural Network. Then we estimate the inspection results in four indexes, which are False alarm rate, Fault missing rate, Incorrect flaw classification and inspection time. The goal is to design a complete inspection procedure in order to detect the defects more correctly and conveniently.
After being verified by numerical simulations and experiments, there are five inputs of Neural Network that have better inspection result. With these five inputs, the False alarm rate and Fault missing rate are both equal to zero. Therefore, Neutral Network is better than single algorithm in inspection and classification of the defects of SMD sample.

參考文獻

[1] 丁志文, “影像處理於SMD元件定位之應用,” 國立台灣科技大學電機工程系碩士論文,” 2001.
[2] 彭瑞勳, “移動框法在電路板上IC的檢測,” 國立交通大學機械工程系碩士論文, 2000.
[3] E.K. Teoh, D.P. Mital, B.W. Lee, L.K. Wee, “Automated Visual Inspection of Surface Mount PCBs ,” Industrial Electronics Society, 16th Annual Conference of IEEE , Vol. 1, pp. 576-580, 1990.
[4] 林育興, “應用機械視覺於印刷電路板表面著裝元件檢測演算法之評估與比較,” 國立大學清華大學動力機械工程系碩士論文, 2003.
[5] Horng-Hai Loh, Ming-Sing Lu, “Printed Circuit Board Inspection Using Image Analysis,” IEEE Transactions on Industry Applications, Vol. 35, No. 2, pp. 426-432, 1999.
[6] 彭光裕, “應用電腦視覺技術於表面黏著元件印刷電路板之自動檢測新系統設計及開發,” 國立交通大學工業工程與管理學系碩士論文, 2000.
[7] D. Demir, S. Birecik, F. Kurugollu, M. Sezgin, I.O. Bucak, B. Sankur, E. Anarim, “Quality Inspection in PCBs and SMDs Using Computer Vision Techniques,” Industrial Electronics, Control and Instrumentation, 20th International Conference on IECON '94, Vol. 2, pp. 857-861, 1994.
[8] M. Moganti, F. Ercal, “Automatic PCB inspection systems,” Potentials, IEEE, Vol. 14, No. 3, pp. 6-10, 1995.
[9] M. Moganti, F. Ercal, “Automatic PCB inspection Algorithms : A Survey ,” Computer Vision and Image Understanding, Vol. 63, No. 2, pp. 287-313, 1996.
[10] M.H. Tatibana, R. de A. Lotufo, “Novel automatic PCB inspection technique based on connectivity,” Computer Graphics and Image Processing, X Brazilian Symposium on Proceedings, pp.187-197, 1997.
[11] 邱劭農, “銲點檢驗之照明設計與特徵萃取,” 國立清華大學動力機械工程學系碩士論文, 2003.
[12] P.J. Besl, E.J. Delp, R. Jain, “Automatic visual solder joint inspection,” IEEE Journal of Robotics and Automation, vRA-1, No. 1, pp. 42-56, 1985.
[13] S.L. Bartlett, P.J. Besl, R. Jain, “Automatic solder joint inspection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 1, pp. 31-43, 1988.
[14] M.R. Driels, D.J. Nolan, “Automatic defect classification of printed wiring board solder joints,” IEEE Transactions on Components, Hybrids, and Manufacturing Technology, Vol. 13, No. 2, pp. 331-340, 1990.
[15] A.C. Sanderson, L.E. Weiss, S.K. Nayar, “Structured highlight inspection of specular surfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 1, pp.44-55, 1988.
[16] S.K. Nayar, A.C. Sanderson, L.E. Weiss, D.A. Simon, “Specular surface inspection using structured highlight and Gaussian images,” IEEE Transactions on Robotics and Automation, Vol. 6, No. 2, pp. 208-218, 1990.
[17] D.W. Capson, S.K. Eng, “A tiered-color illumination approach for machine inspection of solder joints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 3, pp.387-393, 1988.
[18] J.H. Kim, H.S. Cho, “Neural network-based inspection of solder joints using a circular illumination,” Image and Vision Computing, Vol. 13, No. 6, pp. 479-490, 1995.
[19] K.W. Ko, H.S. Cho, “Solder joint inspection using a neural network and fuzzy rule-based classification method,” IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 2, pp. 93-103, 2000.
[20] T.H. Kim, T.H. Cho, Y.S. Moon, S.H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recognition, Vol. 32, No. 4, pp. 565-575, 1999.
[21] R. Schneider, A. Schick, “High-speed optical three-dimensional scanner for automatic solder joint inspection,” Society of Photo-Optical Instrumentation Engineers, Vol. 36, No. 10, pp. 2878-2885, 1997.
[22] O. Oyeleye, E.A. Lehtihet, “Automatic visual inspection of surface mount solder joint defects,” Int. J. Prod. Res., Vol. 37, No. 6, pp.1217-1242, 1999.
[23] R. W. Woodgate, “The Handbook of Machine Soldering, SMT and TH,” John Wiley and Sons, Inc, 1996.
[24] Foley, V. Dam, “Introduction to Computer Graphics,” Addison - Wesley Publication Co., 1994.
[25] M. Moganti, F. Ercal, “A subpattern level inspection system for printed circuit boards,” Computer Vision and Image Understanding, Vol. 70, No. 1, pp. 51-62, 1998.
[26] M. Moganti, F. Ercal, “Segmentation of printed circuit board images into basic patterns,” Computer Vision and Image Understanding, Vol. 70, No. 1, pp. 74-86, 1998.
[27] Z. C. Chen, The automatic optical inspection of surface mounting devices, Master Dissertation, Department of Power Mechanical Engineering, National Tsin- Hua University, 2003.
[28] 羅華強，類神經網路-MATLAB的應用，清蔚科技股份有限公司，2001.
[29] 蘇木春，張孝德，機械學習：類神經網路、模糊系統以及基因演算法則，全華科技圖書股份有限公司，1999.
[30] 周鵬程，類神經網路入門，全華科技圖書股份有限公司，2002.