在全球主要國家紛紛邁向先進製造策略的發展過程中，大數據與物聯網將扮演著關鍵性的角色，其主要目的不只是要減少人力成本讓生產製造流程更具備時間彈性，更是要讓產品的生產效率極大化。隨著軟硬體設備與大環境技術發展的完善，製程資料快速累積，使巨量資料近幾年迅速的崛起，其中又以製造業的資料特性與其最為相近。然而目前要即時監控出製程問題或造成產品缺陷的原因有一定的難度，必須經過事後檢測才能知道產品是否有達到品質標準，這種做法並無法降低產品重工的現象，且多數的製程工程師多半是依靠自身的經驗與簡單的資料判讀來推估缺陷問題，並透過試誤法的方式進行缺陷問題的診斷與機台故障排除，此方法不僅缺乏效率且可能會因為每個工程師經驗不同而造成誤判。
本研究以製造業製程品質良率提昇問題進行探討，在產品為少量多樣的生產模式下建構資料探勘架構，透過製程資料面的探索與挖掘來協助工程師更有效率的掌握造成產品不良之因素。其步驟包括，蒐集產品缺陷、產品良率與機台製程參數等資訊，進行資料預處理工作，先利用統計檢定的方式確認製程機台、製程配方與良率之相關性，再以關聯規則與決策樹演算法進行兩階段的資料挖掘，並透過規則的萃取找出影響良率之機台與製程參數區間，最後將分析結果與領域專家進行模型的調整與修正。本研究以台灣某光學薄膜式濾光廠之實際製程資料進行模型架構之驗證，結果顯示此資料探勘架構不僅可以找出影響良率之機台與機台設定參數區間，且可以歸納出良率規則，更有效率的提供製程工程師作為良率診斷與改善之依據，並有效的提昇了產品最終良率。

Big data analytics approaches have been developed to extract potentially useful information and manufacturing intelligence from massive data in various problems in different fields. In the manufacturing industry, real-time monitoring of process problems or product defects remains difficult, most engineers rely on their own experience to estimate defect problems, and apply trial and error methods to diagnosis identified problems and troubleshooting. This is not only inefficient, but may cause miscarriage of justice due to lack of relevant experience, and few studies have applied big data analysis to practical manufacturing production lines.
This study proposed a big data mining framework to improve manufacturing quality and yield. The framework contains six phases: problem definition,
data preparation, data preprocess, target setting, model construction, and performance evaluation. In summary, the procedure is described as following.
Step1.Product defect descriptors and process parameters were collected for data preprocessing.
Step2.Statistical techniques identified correlations between specific process machines and yield.
Step3.Association rules and a decision tree algorithm were devised to allow second stage data mining, and identify the impact of yield.
Step4.Experts opinion was used to adjust and modify the model.
Thus, the proposed data mining framework accurately identified the factors affecting efficiency, and provided immediate manufacturing value. The result shows that the proposed big data mining framework effectively reduced the product non-performing rate. This study firstly employ data mining and big data analytics for troubleshooting and yield enhancement of Optical Thin-Film Filter manufacturing production line and developed an effective solution.

Abadi, D. J. (2009). Data management in the cloud: limitations and opportunities. IEEE Data Eng. Bull., 32(1), 3-12.
Acharjya, D., & Ahmed, K. P. (2016). A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 7(2), 511-518.
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Paper presented at the Acm Sigmod Record.
Alonso, F., Martínez, L., Pérez, A., & Valente, J. P. (2012). Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned. Expert Systems with Applications, 39(8), 7524-7535.
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.
Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support: John Wiley & Sons, Inc.
Blodgett, K. B. (1939). Use of interference to extinguish reflection of light from glass. Physical Review, 55(4), 391.
Braha, D., & Shmilovici, A. (2002). Data mining for improving a cleaning process in the semiconductor industry. IEEE transactions on semiconductor manufacturing, 15(1), 91-101.
Braha, D., & Shmilovici, A. (2003). On the use of decision tree induction for discovery of interactions in a photolithographic process. IEEE transactions on semiconductor manufacturing, 16(4), 644-652.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees Belmont. CA: Wadsworth International Group.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: using vision to think: Morgan Kaufmann.
Casali, A., & Ernst, C. (2012). Discovering correlated parameters in semiconductor manufacturing processes: A data mining approach. IEEE transactions on semiconductor manufacturing, 25(1), 118-127.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide.
Chen, A., & Hong, A. (2010). Sample-efficient regression trees (SERT) for semiconductor yield loss analysis. IEEE transactions on semiconductor manufacturing, 23(3), 358-369.
Chen, H.-M., Chang, K.-C., & Lin, T.-H. (2016). A cloud-based system framework for performing online viewing, storage, and analysis on big data of massive BIMs. Automation in Construction.
Chen, Y.-J., Fan, C.-Y., & Chang, K.-H. (2016). Manufacturing intelligence for reducing false alarm of defect classification by integrating similarity matching approach in CMOS image sensor manufacturing. Computers & Industrial Engineering, 99, 465-473.
Chen, Y.-J., Lin, T.-H., Chang, K.-H., & Chien, C.-F. (2013). Feature extraction for defect classification and yield enhancement in color filter and micro-lens manufacturing: An empirical study. Journal of Industrial and Production Engineering, 30(8), 510-517.
Chi, E. H.-h. (2000). A taxonomy of visualization techniques using the data state reference model. Paper presented at the Information Visualization, 2000. InfoVis 2000. IEEE Symposium on.
Chien, C.-F., Chang, K.-H., & Wang, W.-C. (2014). An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing. Journal of Intelligent Manufacturing, 25(5), 961-972.
Chien, C.-F., & Hsu, C.-Y. (2014). Data mining for optimizing IC feature designs to enhance overall wafer effectiveness. IEEE transactions on semiconductor manufacturing, 27(1), 71-82.
Chien, C.-F., Hsu, C.-Y., & Chen, P.-N. (2013). Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal, 25(3), 367-388.
Chien, C.-F., Lin, S., & Cheng, J.-C. (2008). Construct Fuzzy Decision Tree for Mining Interrelated Semiconductor Manufacturing Data for Yield Enhancement. Journal of Quality, 15(3).
Chien, C.-F., Wang, W.-C., & Cheng, J.-C. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33(1), 192-198.
Chu, P.-C., Chen, C.-C., & Chien, C.-F. (2017). ANALYZING TFT-LCD ARRAY BIG DATA FOR YIELD ENHANCEMENT AND AN EMPIRICAL STUDY OF TFT-LCD MANUFACTURING IN TAIWAN. International Journal of Industrial Engineering: Theory, Applications and Practice, 23(5).
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: new analysis practices for big data. Proceedings of the VLDB Endowment, 2(2), 1481-1492.
Cook, K. A., & Thomas, J. J. (2005). Illuminating the path: The research and development agenda for visual analytics. Retrieved from
Crewson, P. (2006). Applied statistics handbook. AcaStat Software, Leesburg.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
Fraunhofer, J. (1817). Bestimmung des Brechungs‐und des Farbenzerstreungs‐Vermögens verschiedener Glasarten, in Bezug auf die Vervollkommnung achromatischer Fernröhre. Annalen der Physik, 56(7), 264-313.
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003). The Google file system. Paper presented at the ACM SIGOPS operating systems review.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
Hartigan, J. A., & Hartigan, J. (1975). Clustering algorithms (Vol. 209): Wiley New York.
Hecht, R., & Jablonski, S. (2011). Nosql evaluation. Paper presented at the International conference on cloud and service computing.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
Kamsu-Foguem, B., Tchuenté-Foguem, G., Allart, L., Zennir, Y., Vilhelm, C., Mehdaoui, H., . . . Ravaux, P. (2012). User-centered visual analysis using a hybrid reasoning architecture for intensive care units. Decision Support Systems, 54(1), 496-509.
Kart, L. (2012). Market trends: Big data opportunities in vertical industries. Industry Market Strategies Worldwide, Gartner.
Keim, D., Qu, H., & Ma, K.-L. (2013). Big-data visualization. IEEE Computer Graphics and Applications, 33(4), 20-21.
Keim, D. A. (2002). Information visualization and visual data mining. IEEE transactions on Visualization and Computer Graphics, 8(1), 1-8.
Keim, D. A., Kohlhammer, J., Ellis, G., & Mansmann, F. (2010). Mastering the information age-solving problems with visual analytics: Florian Mansmann.
Kimmel, R. K., & Parks, R. E. (2002). ISO 10110 Optics and Optical Instruments: Preparation of Drawings for Optical Elements and Systems: a User's Guide: Optical Society of America.
Kittler, R., & Wang, W. (2000). Data mining for yield improvements. Proceedings from MASM.
Kudyba, S. (2014). Big data, mining, and analytics: components of strategic decision making: CRC Press.
Kung, S.-Y. (2015). Visualization of big data. Paper presented at the Cognitive Informatics & Cognitive Computing (ICCI* CC), 2015 IEEE 14th International Conference on.
Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine learning, 40(3), 203-228.
Ma, H., Jen, A. Y., & Dalton, L. R. (2002). Polymer‐Based Optical Waveguides: Materials, Processing, and Devices. Advanced materials, 14(19), 1339-1365.
Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the digital humanities, 2, 460-475.
Manyika, J. (2012). Manufacturing the future: The next era of global growth and innovation: McKinsey Global Institute.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
Olofson, C., & Vesset, D. (2012). Big Data: Trends, Strategies, and SAP Technology. White Paper, IDC.
Oweis, N. E., Owais, S. S., George, W., Suliman, M. G., & Snášel, V. (2015). A survey on big data, mining:(tools, techniques, applications and notable uses) Intelligent Data Analysis and Applications (pp. 109-119): Springer.
Pantazos, K. (2013). Custom visualization without real programming: IT University of Copenhagen, Software and Systems.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
Quinlan, J. R. (1996). Improved use of continuous attributes in C4. 5. Journal of artificial intelligence research, 4, 77-90.
Rani, S., Talwar, R., Malhotra, J., Ahmed, S. H., Sarkar, M., & Song, H. (2015). A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks. Sensors (Basel), 15(11), 28603-28627. doi:10.3390/s151128603
Ritter, E. (1981). Properties of optical film materials. Applied optics, 20(1), 21-25.
Schmidt, H., by Sol-Gel, I.-O. C., & Techniques, J. (1989). Sol-Gel Science and Technology. by M. A. Aegerter, M. Jafelicci, Jr., DF Souza and ED Zanotto, World Scientific, 432-469.
Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., . . . Liu, H. (2010). Data warehousing and analytics infrastructure at facebook. Paper presented at the Proceedings of the 2010 ACM SIGMOD International Conference on Management of data.
Tu, K. K.-W., Lee, J. C.-s., & Lu, H. H.-S. (2009). A novel statistical method for automatically partitioning tools according to engineers' tolerance control in process improvement. IEEE transactions on semiconductor manufacturing, 22(3), 373-380.
Van Wijk, J. J. (2005). The value of visualization. Paper presented at the VIS 05. IEEE Visualization, 2005.
Wu, X. (2000). Building intelligent learning database systems. AI magazine, 21(3), 61.
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107.
石守勇(1991)。光纖通訊導論。廈門大學出版社:福建,5-10。
李正中. (1998). 高密度多波分工干涉濾光片之製作. 光學工程(61), 37-41.
朱正煒. (1999). DWDM 干涉濾光片之設計及製作. 光學工程(68), 40-50.
王崗嶸, & 陳鴻仁. (2000). DWDM 產業及技術動態調查, 光電科技協進會.
顾培夫, 白胜元, 李海峰, 章岳光, 刘旭, & 唐晋发. (2002). 密集型波分复用薄膜干涉滤光片的设计. 光学学报, 22(7), 794-797.
彭金堂、張盛鴻、簡禎富、楊景晴(2005),『建構關聯規則資料挖礦架構及其在台電配電事故定位之研究』,中華民國資訊管理學報,第十二卷,第四期,頁121-141。
李正中. (2006). 光學薄膜與鍍膜技術: 台北: 藝軒圖書出版社.
陳建人. (2007). 光學元件精密製造與檢測. 國家實驗研究院儀器科技中心, 國家實驗研究院儀器科技中心.
簡禎富, & 許嘉裕. (2014). 資料挖礦與大數據分析: 前程文化.
行政院（2015），行政院生產力 4.0 發展方案。
簡禎富, 林國義, 許鉅秉, & 吳政鴻. (2016). 台灣生產與作業管理之相關期刊文獻回顧與前瞻: 從工業 3.0 到工業 3.5. 管理學報, 33(1), 87-103.
余承叡, 盧冠宇, 吳維文, & 丁士翔. (2016). 邁向工業 4.0-製造業的大數據分析應用實例. 電工通訊季刊, 68-77.
國際數據資訊 https://www.idc.com
IEK產業情報局 http://ieknet.iek.org.tw
光電科技協進會 http://www.pida.org.tw