半導體製程由於受到動態工件到達、工件迴流、生產流程漫長與瓶頸機台飄移…等等因素之挑戰，是最複雜的生產環境之一。傳統的生產計劃方法受到這些不確定因素與限制條件的影響，對於推估不同在製品(WIP)水位時對應的流程時間與產出之能力難免受到限制。
本研究的目的在於建構一可預測流程時間之資料挖礦架構，以生產線現況包括在製品、產能、產能利用率等做為輸入因子，考量該領域相關知識以推衍實證規則，並且藉由控制輸入因子達成流程時間與產出之控管。本研究研究架構整合不同的資料挖礦技術，包含自我組織映射網路、決策樹分析、導傳遞類神經網路與高斯─牛頓非線性迴歸法等。本研究利用蒐集自一座位於新竹科學園區實際半導體製造廠的生產資料進行實證研究。預測結果顯示本研究所提出之研究架構可在大部分的情況下，以低預測誤差推導出該廠之生產力表現曲線；即便在少部分時間中該廠之生產力變動過於劇烈，本預測模型仍舊可以得到有效的預測結果以降低預測誤差，同時在數日內重新校正，因此證明本研究之效度。

Semiconductor manufacturing process is one of the most complicated production environments owing to the challenges of dynamic job arrival, job re-circulation, long production length, and bottleneck drifts. Traditional production planning methodologies were limited to estimate the corresponding throughput and cycle time under various WIP levels with uncertain factors and production constraints.
This study aims to develop a data mining framework for cycle time prediction with input factors of production line status such as WIP, capacity, utilization, etc, combining with domain knowledge, to derive empirical rules that the levels of input production factors can be controlled to thus control cycle time and throughput. This approach integrated several data mining techniques in this two-phase research framework including self-organizing maps, decision tree analysis, back propagation neural network, and Gauss-Newton nonlinear regression method. We conducted an empirical study in which real production line data from a semiconductor fabrication factory in Hsinchu Science Park are collected for validation of this framework. The forecast results showed that the proposed framework can derive the productivity performance curves with low forecast error most of times; even sometimes the fab productivity change violently, the forecast models can still obtain an effective result to decrease the forecast error and re-align the forecast models immediately in a few days.

賴彥中，2005，發展主幹式決策樹法則以提升半導體良率之研究，國立清華大學工業工程與工程管理研究所碩士論文。
Adiraans, P., and Zantinge, D., 1996. Data mining, Addison Wesley, New York.
Berry, M. and Linoff, G., 1997. Data mining techniques for marketing, sales and customer support, John Wiley & Sons, New York.
Bonal, J., Fernadez, M., Richard, O. M., Aparicio, S., Olica, R., Garcia, S., Gonzalez, B., and Rodriquez, L, 2001. A statistical approach to cycle time management, 2001 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 11-15.
Braha, D., and Shmilovici, A., 2002. Data mining for improving a cleaning process in the semiconductor industry, IEEE Transactions on Semiconductor Manufacturing, vol. 15, pp. 91-101.
Breiman, L., Friedman, J. H., Olshen, R. J., and Stone, C. J., 1984. Classification and regression trees, Belmont, CA.
Cabena, P., Hadjinian, P., Stadler, R., Verhess, J. And Zanasi, A., 1998. Discovering data mining: from concept to implementation, Prentice Hall, New Jersey.
Carlos, S. C., 1996. Self organizing neural networks for financial diagnosis, Decisions Support System, vol. 17, pp. 227-238.
Catay, C., Erenguc, S. S., and Vakharia, A. J., 2003. Tool capacity planning in semiconductor manufacturing, Computers & Operations Research, vol. 30, pp. 1349-1366.
Chapmen, P., Clinton, J., Kerber, R., Khabaza, T., Reinart, T.,Shearer, C., and Wirth, R., 2000. CRISP−DM step-by-step data mining guide, Retrieved February 25, 2006, from http://www.crisp-dm.org/
Chen, M. C., Chiu, A. L., and Chang H. W., 2005. Mining changes in customer behavior in retail marketing, Expert Systems with Applications, vol. 28, pp. 773-781.
Chien, C. F., Hsiao, A., and Wang, I., 2004. Constructing semiconductor manufacturing performance index and applying data mining for manufacturing data analysis, Journal of Chinese Institute of Industrial Engineering, vol.21, pp.313-327.
Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. D., Wang, S. T., 2005. Cycle time prediction and control based on production line status and manufacturing data mining, Proceedings of International Symposium on Semiconductor Manufacturing Conference 2005, 13-15 September, San Jose, California, USA, pp.327-330.
Chien, C. F., Wang, W. C., and Cheng, J. C., 2007. Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Expert Systems with Applications, vol. 33, pp. 1-7.
Chung, S. H., and Kim, S. H., 2004. Data mining for financial prediction and trading: application to single and multiple markets, Expert Systems with Applications, vol. 26, pp. 131-139.
Dabbas, R. M., and Chen, H. N., 2001. Mining semiconductor manufacturing data for productivity improvement − an integrated relational database approach, Computers in Industry, vol. 45, pp. 29-44.
Delesie, L., and Croes, L., 2000. Operations research and knowledge discovery: a data mining method applied to health care management, International Transactions in Operational Research, vol. 7, pp. 159-170.
Dhar, V., 1998. Data mining in finance: using counterfactuals to generate knowledge from organizational information systems, Information Systems, vol.23 (7), pp. 423-437.
Dragan, J., and Scitovski, R., 1996. The existence of optimal parameters of the generalized logistic function. Applied Mathematics and Computation, vol.77, pp. 281-294.
Easignwood, J. E., 1987. Early product life cycle forms for infrequently purchased major products, Inter. J. of Research in Marketing, vol. 4, pp.3-9.
Enke, D, and Thawornwong, S,. 2005. The use of data mining and neural networks for forecasting stock market returns, Expert Systems with Applications, vol. 29, pp. 927-940.
Facca, F. M., and Lanzi, P. L., 2005, Mining interesting knowledge from weblogs: a survey, Data & Knowledge Engineering, vol. 53, pp. 225-241.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., 1996. From data mining to knowledge discovery in databases (a survey), AI Magazine, 17(3), pp. 37-54.
Fowler, J. W., Brown, S., Gold, H., and Schoemig, A., 1997. Measure improvement in cycle-time-constrained capacity, Proceedings of the 6th IEEE International Symposium on Semiconductor Manufacturing.
Gallant, S. I., 1993. Neural network learning and expert systems, The MIT Press, London.
Gise, P. E., and Blanchard, R., 1979. Semiconductor and integrated circuit fabrication techniques, Reston Pub. Co., Reston, Va.
Goodwin, L., VanDyne, M., Lin, S., and Talbert, S., 2003. Data mining issues and opportunities for building nursing knowledge, Journal of Biomedical Informatics, vol. 36, pp. 379-388.
Han, J. and Kamber, M., 2000. Data mining: concepts and techniques, Morgan Kaufmann, San Francisco, CA
Hand, D., Mannila, H. and Smyth, P., 2001. Principles if data mining, MIT Press, Cambridge, MA.
Hauske, G., 1997. A self organizing map approach to image quality, BioSystems, vol. 40, pp. 93-102.
Huysmans, J., Baesens, B., Vanthienen, J., and Gestel, T. V., 2006. Failure prediction with self organizing maps, Expert Systems with Applications, vol. 30, pp. 479-487.
Kass, G. V., 1975. Significance testing in automatic interaction detection (AID), Applied Statistics, vol. 24, pp. 178-189.
Kass, G. V., 1980. An exploratory technique for investigating large quantities of categorical data, Applied Statistics, vol. 29(2), pp. 119-127.
Kumar, K. and Alsaleh, M. A., 1996. A comparative study for the estimation of parameters in nonlinear models, Applied Mathematics and Computation, vol. 77, pp. 179-183.
Larose, D. T., 2005. Discovering knowledge in data, John Wiley & Sons, New Jersey.
Lee, Y., Kim, S., Yea, S., and Kim, B., 1997. Production planning in semiconductor wafer fab considering variable cycle times, Computers Industrial Engineering, vol. 33, pp. 713-716.
Lewandowsky, R., 1980. Prognose und informationssysteme und ihre anwendungen, Walter de Gruyter, New York.
Mangiameli, P., Chen, S. K., and West, D., 1996. A comparison of SOM neural network and hierarchical clustering methods, European Journal of Operational Research, vol. 93, pp. 402-417.
Mason, A., 2000. Overview of semiconductor fabrication technology, Retrieved May 25, 2006, from http:// www.engr.uky.edu/~ee461g/semi_fab_techno.htm.
McDonald, C. J., 1999. New tools for yield improvement in integrated circuit manufacturing: can they be applied to reliability? Microelectronics Reliability, col. 39, pp.731-739.
Mieno, F., Sato, T., Shibuya, Y., Odagiri, K., Tsuda, H., and Take, R., 1999. Yield improvement using data mining system, semiconductor manufacturing conference proceedings, 1999 IEEE International Symposium, pp.391-394.
Mitra, S. and Acharya, T., 2003. Data mining: multimedia, soft computing and bioinformatics, John Wiely & Sons, New Jersey.
Nelder, J. A., 1961. The fitting of a generalization of the logistic curve, Biometrics, pp. 89-100.
Palma, F. D., Nicolao, G. D., Miraglia, G., Pasquinetti, E., and Piccinini, F., 2005. Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing, Pattern Recognition Letters, vol. 26, pp. 1857-1865.
Quinlan, J. R., 1993. C4.5: Programs for machine learning, Morgan Kaufmann, San Francisco, CA.
Reinschmidt, J., Gottschalk, H., Kim, H., and Zwietering, D., 1999. Intelligent miner for data: enhance your business intelligence, IBM International Techniqual Support Organization, USA.
Ren, S., 2003. Phenol mechanism of toxic action classification and prediction: a decision tree approach, Toxicology Letters, vol. 144, pp. 313-323.
Ritter, H., 1995. Self-organizing feature maps: Kohonen maps, in Arbib, M. A., ed., The handbook of brian theory and neural networks, pp.846-851, MIT Press, Cambridge, MA.
Robertson, T. B.,1908. On the normal rate of growth of an individual and its biochemical significance. Roux’ Arch. Entwicklungsmech, Organismen, vol. 25, pp. 581-614.
Rygielsky, C., Wang, J. H., and Yen, D. C., 2002. Data mining techniques for customer relationship management, Technology in Society, vol. 24, pp. 483-502.
Sattler, L., 1996. Using queuing curve approximation in a fab to determing productivity improvement, 1996 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 140-145.
Seber, G. A. F. and Wild, C. J., 1989. Nonlinear regression, John Wiley & Sons, New York.
Semenova, T, 2004. Discovering patterns of medical practice in large administrative health database, Data & Knowledge Engineering, vol. 51, pp. 149-160.
Sen, A. and Srivastava, M., 1990. Regression analysis, Springer-Verlag, New York.
Shaw, M. J., Subramaniam, C., Tan, G. W., and Welge, M. E., 2001. Knowledge management and data mining for marketing, Decision Support Systems, vol. 31, pp. 127-137.
Smith, K. A., and Gupta, J. N. D., 2000. Neural network in business: techniques and applications for the operations researcher, Computer & Operations Research, vol. 27, pp. 1023-1044.
Smith, K. A., and Ng, A., 2003, Web page clustering using a self-organizing map of user navigation patterns, Decision Support Systems, vol. 35, pp. 245-256.
Thuraisinghan, B., 1998. Data mining: technologies, techniques, tools, and trends, CRC Press LLC, Boca Raton, USA.
Vesanto, J., 1999. SOM-based data visualization methods, Intelligent Data Analysis, vol. 3, pp. 111-126.
Vesanto, J., and Alhoniemi, E., 2000. Clustering of the self-organizing map, IEEE Transactions on Neural Network, vol. 11, pp. 586-600.
Verhulst, P. F., 1838. Notice sur la loi que la population suit dans son accroissement. Corr. Math. et Phys. publ. par A. Quetelet , pp. 113–121.
Wang, X., Abraham, A, and Smith, K. A., 2005. Intelligent web traffic mining and analysis, Journal of Network and Computer Applications, vol. 28, pp. 147-165.
Wong, B. K., Bodnovich, T. A., and Selvi, Y., 1997. Neural network applications in business: a review and analysis of the literature (1988-1995). Decision Support Systems, vol. 19, pp. 301-320.
Yu, C. Y., and Huang, H. P., 2002. On-line learning delivery decision support system for highly product mixed semiconductor foundry, IEEE Transactions on Semiconductor Manufacturing, vol. 15, pp. 274-278.
Zant, P. V., 2000. Microchip fabrication: a practical guide to semiconductor processing, McGraw-Hill, New York.