積體電路(Integrated Circuits, ICs)產業係一種技術、資本均高度密集的產業，因此如何使設備利用率達到最高，以期於最短時間內將設備成本攤平，降低產品成本、創造公司競爭優勢，乃成為業界不斷追求的目標。
就半導體製程而言，化學氣相沉積(Chemical Vapor Deposition, CVD)機台為製造積體電路之關鍵設備，爲維護設備正常運作與確保產量，設備之故障必須正確且及時地予以診斷，目前在設備維修作業上仍相當依賴資深維修人員的經驗，但諸多因素使得高科技從業人員流動率偏高，技術經驗傳承不易，企業知識難獲有效蓄積。在積體電路設備維修議題，已有學者提出案例式推理(Case-Based Reasoning, CBR)、小波理論(wavelet)等方法，但上述方法對故障診斷的效能仍難符實務之需。基此，本研究結合專業維修人員經驗與類神經網路(Artificial Neural Network, ANN)技術進行故障分類預測，建構一套化學氣相沉積機台故障診斷模式。倒傳遞類神經網路(Back-Propagation Neural Network, BPN)用以找出故障現象與故障原因之關係，模式效能則以網路績效加以衡量。
研究結果顯示，本文所提出之模式對化學氣相沉積機台的故障原因診斷可獲致良好效果(測試績效RMSE=0.050892)；對復機時間之預測，則由於填卷者對復機時間之認定分歧，導致網路測試績效稍差(RMSE=1.052125)，預測效果不盡理想。惟亦可由此延伸，如填卷者對故障復機時間之觀念一致，則對復機時間之診斷效能將能獲得提升，並可應用於相關領域之製造設備。

The integrated circuits (ICs) industry is the industry with technology and capital intensively. Thus how to make the equipment utilization higher and to compensate the equipment depreciation cost, to reduce product cost, and to create a company’s competitive advantages are the targets to be pursued.
In semiconductor manufacturing, the chemical vapor deposition (CVD) equipment is a key system in producing integrated circuits. To maintain equipment in good condition and stable throughput rate, CVD faults should be diagnosed accurately and timely. At present, the equipment maintenance still depends deeply on the engineers’ experience. Due to the fact that high-tech employee has higher leaving job rate, the technical experience is not easily transferred and enterprise knowledge can not be aggregated effectively. For maintaining the IC manufacturing equipments effectively, some methods were developed by scholars such as case-based reasoning (CBR) and wavelet theory et al. These fault diagnosis approaches, however, still can not meet the needs in practice. Thus a model for CVD fault diagnosis is needed. In this research, a system consisted of artificial neural network (ANN) and expert’s experience is presented. A back-propagation neural network (BPN) was used to capture the causal relationships between fault symptoms and root causes.
The results have shown that proposed model has an excellent prediction capability of CVD machine fault root causes diagnosis. On the other hand, the result for time prediction of recovering the CVD machine is not as good as the results of CVD machine fault root causes diagnosis due to the respondents have different cognition for recover time. However, if we can define the recover time clearly, the fault diagnosis performance could be raised and can be applied in process equipment for related manufacturing fields.

中文部份
周政宏，神經網路：理論與實務，松崗電腦圖書資料股份有限公 司，台北市，1995。
張勁燕，半導體製程設備，五南圖書出版股份有限公司，台北市，2005。
張雲景、賴礽仰，SPSS統計軟體的應用(12.0)，華騰文化股份有限公司，台北市，2005。
葉怡成，類神經網路模式應用與實作，儒林圖書有限公司，台北市，2003。
羅華強，類神經網路：MATLAB的應用，高立圖書有限公司，台北縣，2005。

英文部份
Becraft, W. R. and P. L. Lee, “An Integrated Neural Network Expert System Approach for Fault Diagnosis,” Computers & Chemical Engineering, 17 (10), 1001-1014, 1993.
Chang, M., J.-C. Chen, J.-W. Cheng and J.-S. Heh, “Advanced Process Control Expert System of CVD Membrane Thickness Based on Neural Network,” Progress on Advanced Manufacture for Micro/Nano Technology 2005, Pt 1 and 2 Materials Science Forum, 505-507, 313-318, 2006.
Chen, W.-C., Amy H. I. Lee, W.-J. Deng and K.-Y. Liu, “The Implementation of Neural Network for Semiconductor PECVD Process,” Expert Systems with Applications, 32 (4), 1148-1153, 2007.
Chien, C.-F., W.-C. Wang and J.-C. Cheng, “Data Mining for Yield Enhancement in Semiconductor Manufacturing and An Empirical Study,” Expert Systems with Applications, 33 (1), 192-198, 2007.
Cybenko, G., “Approximation by Superpositions of a Sigmoidal Function,” Mathematics of Control Signals and System, 2, 303-314, 1989.
Dollet, A., “Multiscale Modeling of CVD Film Growth: A Review of Recent Works,” Surface & Coatings Technology, 30(177), 245-251, 2004.
Fong, ACM and S.-C. Hui, “An Intelligent Online Machine Fault Diagnosis System,” Computing & Control Engineering Journal, 12(5), 217-223, 2001.
Goodlin, B. E., D. S. Boning, H. H. Sawin and B. M. Wise, “Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools,” Journal of The Electrochemical Society, 150(12), G778-G784, 2003.
Hagan, M. T. and M. B. Menhaj, “Training Feedforward Networks with the Marquardt Algorithm,” IEEE Transactions on Neural Networks, 5, 989-993, 1994.
Hong, S. J. and G. S. May, “Neural Network-Based Real-Time Malfunction Diagnosis of Reactive Ion Etching Using In Situ Metrology Data,” IEEE Transactions on Semiconductor Manufacturing, 17(3), 408-421, 2004.
Hung, M.-H., K.-Y. Chen, R.-W. Ho and F.-T. Cheng, “Development of An e-Diagnostics/Maintenance Framework for Semiconductor Factories with Security Considerations,” Advanced Engineering Informatics, 17(3-4), 165-178, 2003.
Kim, B. and S. Kim, “Diagnosis of Plasma Processing Equipment Using Neural Network Recognition of Wavelet-Filtered Impedance Matching,” Microelectronic Engineering, 82, 44–52, 2005.
Kim, J. Y., J. K. Sim, M. J. Song, C. H. Kim and L. K. Kwac, “The Performance Advancement of Test Algorithm Using Neural Network for Semiconductor Packages,” Advances in Fracture and Failure Prevention, Pts 1 and 2 Key Engineering Materials, 261-263, 411-416, 2004.
Kweon, K. E., J. H. Lee, Y.-D. Ko, M.-C. Jeong, J.-M. Myoung and I. Yun, “Neural Network Based Modeling of HfO2 Thin Film Characteristics Using Latin Hypercube Sampling,” Expert Systems with Applications, 32, 358–363, 2007.
Negnevitsky, M., Artificial Intelligence: A Guide to Intelligent System, Pearson A/W, UK, 2002.
Park, S.-J., M.-S. Lee, S.-Y. Shin, K.-H. Cho, J.-T. Lim, B.-S. Cho, Y.-H. Jei, M.-K. Kim and C.-H. Park, “Run-to-Run Overlay Control of Steppers in Semiconductor Manufacturing Systems Based on History Data Analysis and Neural Network Modeling,” IEEE Transactions on Semiconductor Manufacturing, 18(4), 605-613, 2005.
Sheu, D. D. and J.-Y. Kuo, “A Model for Preventive Maintenance Operations and Forecasting,” Journal of Intelligent Manufacturing, 17(4), 441-451, 2006.
Su, C.-T., T. Yang and C.-M. Ke, “A Neural-Network Approach for Semiconductor Wafer Post-Sawing Inspection,” IEEE Transactions on Semiconductor Manufacturing, 15(2), 260-266, 2002.
Wen, Y.-L., M.-D. Jeng and Y.-S. Huang, “Diagnosability of Semiconductor Manufacturing Equipment,” Progress on Advanced Manufacture for Micro/Nano Technology 2005, Pt 1 and 2 Materials Science Forum, 505-507, 1135-1140, 2006.
Zurada, J. M., Artificial Neural Systems, West Publishing Company, Singapore, 1992.

相關網址
工研院產業資訊服務網：96年5月7日，http://www.itis.org.tw/rptDetailFree.screen?rptidno=6C6EA7CF3165DCC9482572980011BB65
行政院主計處：96年5月24日，http://www.dgbas.gov.tw/ct.asp?xItem=17637&ctNode=2800