半導體製造現場的重要生產指標為在製品量與流動量，現場管理者藉由生產指標的變動情形了解製造現場的生產狀況與進度，並根據過去的經驗做出適當的判斷，進行相對應的決策。但是這樣的做法，只能做到瞭解已經發生的「歷史」或稱為「實績」，並無法在事前防範異常的發生。如果能夠提出一個預警機制，事先預測未來生產績效的可能變化，就可以依據預測結果得知機台的生產績效變差，事先做出預防措施，以防止事態變嚴重，進而促進系統整體績效的提升。
因此本研究探討如何預測半導體廠機台生產指標中的在製品與流動量變動情況，做為管理者進行現場資料調配與控制的參考依據。本研究利用類神經網路為工具，以國內某半導體廠的資料進行實證研究，參考過去文獻以及專家建議的變數建立預測模型，並且透過網路修剪的過程，逐次刪除影響較小的變數，以找出最佳變數組合的類神經網路模型。實驗的結果顯示，本研究所提出的類神經網路建構方法能夠有效的預測生產指標未來一天的變動情況，在製品數量的預測誤差為7.33%，流動量的預測誤差為7.63%，可以達到實際應用上的參考依據。

WIP and Move are two important production indices for a semiconductor manufacturing fab. The shop managers need to know the production condition and progress based on the variation of these indices and make proper judgment and corresponding decisions based on their experiences. However, this kind of execution provides only understanding of history or facts that happened before. In order to predict possible production variation; to prevent problems getting worse; and to facilitate the whole system performance, it is necessary to have an early warning mechanism that can predict possible future production deviations.
Therefore this study focuses on how to predict the variation of WIP and Move to provide managers insights for material flow and control. The neural networks techniques are used to verify the proposed prediction model with data from one local semiconductor factory. The variables of the neural prediction model are chosen by reference to literature and consultation of experts. Through the process of net pruning to delete variables which have minor influence one by one to find the neural networks model with the best variables combination. The results showed the proposed prediction model can effectively predict the variation of production indices one day ahead, and had its practical contribution. The prediction error of WIP is about 7.33% and the prediction error of Move is about 7.63%.

英文部分
[1] Uzsoy, R., C. Y. Lee and L. A. Martin-Vega, “A review of production planning and scheduling models in semiconductor industry. Part I: System characteristics, performance evaluation and production planning,” IIE Transactions, Vol. 24, No. 4, pp. 47-60, 1992.
[2] Uzsoy, R., C. Y. Lee and L. A. Martin-Vega, “A review of production planning and scheduling models in semiconductor industry. Part II: Shop floor control,” IIE Transactions, Vol. 26, No.5, pp. 44-54, 1994.
[3] Bauer, A., R. Bowden, J. Browne, J. Duggen and G. Lyons, “Shop Flow Control System, From Design to Implement,” Chapman & Hall, U.S.A., 1994.
[4] Hopp, W. and M. Spearman, “Factory Physics,” IRWIN, 1996.
[5] Wein, L. M. , “Scheduling Semiconductor wafer fabrication,” IEEE Transactions on Semiconductor Manufacturing, Vol. 1, No. 3, pp. 115-130, 1988.
[6] Glassey, C. R. and M. G. C. Resende, “Closed-loop job shop release control for VLSI circuit manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 1, No. 1, pp. 36-46, 1988.
[7] Li, S., T. Tang and D. W. Collins, “Minimum Inventory Variability Schedule with Applications in Semiconductor Fabrication,” IEEE Transaction on Semiconductor Manufacturing, Vol. 9, No. 1, pp. 145-149, 1996.
[8] Glassey, C. R. and M. G. C. Resende, “A Scheduling Rule for Job Release in Semiconductor Fabrication,” Operation Research Letters, Vol. 7, No. 5, pp. 213-217, 1988.
[9] Kim ,Y. D., J. U. Kim, S. K. Lim and H. B. Jun, “Due-date based scheduling and control policies in a multiproduct Semiconductor wafer fabrication facility, ”IEEE Transactions On Semiconductor Manufacturing, Vol. 11, pp. 155-164, Feb. 1998.
[10] Higgins, P. and J. Browne, “The monitor in Production activity control system,” Production Planning & Control, Vol. 1, No. 1, pp. 17-26, 1990.
[11] Huang, C. L., Y. H. Huang, T. Y. Chang, S. H. Chang, C. H. Chung, D. T. Huang and R. K. Li, “The Construction of Production Performance Prediction System for Semiconductor Manufacturing with Artificial Neural Networks,” International Journal of Production Research, Vol. 37, No. 6, pp. 1387-1402, 1999.
[12] Srivatsan, N. and K. Kempf, “Effective Modeling of Factory Throughput Times,” IEEE/CMPT, pp. 377-383, 1995.
[13] Montgomery, D. C., E. A. Peck and G. G. Vining, “Introduction to linear regression analysis,” Wiley Inter. Science, New York, 2006.
[14] Huan, J. S., T. L. James and P. W. Trefor, “Using Nerual Networks to Predict Component Inspection Requirements for Aging Aircraft,” Computers and Industrial Engineering, Vol. 30, No. 2, 1996.
[15] Freeman, J. A. and D. M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques,” Addison-Wesley Publishing Company, New York, 1992.
[16] Williams, R. J. and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural Computation, Vol. 1, pp. 270-280, 1989.
[17] Rumelhart, D. E., G. E. Hintion and R. J. Willians, “Learning Representations by Back-Propagating errors,” Nature, Vol. 323, pp. 533-536, 1986.
[18] Werbos, P., “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,” PHD thesis, Harvard, Cambridge, MA. 1974.
[19] Vellido, A. , P. J. Lisboa and J. Vaughan, “Neural Networks in Business: A Survey of Applications ,” Expert Systems with Applications, Vol.17, pp. 51-70, 1999.
[20] Fausett, L., “Fundamentals of Neural Networks,” Prentice Hall, Inc, 1994.
[21] Chen, T., “A hybrid SOM-BPN approach to lot output time prediction in a wafer fab,” Neural Processing Letters, Vol. 24, No. 3, pp. 271-288, December 2006.
[22] Warner, B. and M. Misra, “Understanding Neural Networks as Statistical Tools,” The American Statistician, Vol. 50 No. 4, pp. 284-293, 1996.
[23] Beale, R. and T. Jackson, “Neural Computing-An Introduction,” Adam Hilger, 1990.
[24] Lippmann, R. P., “An introduction to computing with neural nets,” IEEE ASSP Magazine, Vol. 4, No. 2, pp. 4-22, 1987.
[25] Hornik, K., M. Stinchcombe and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, Vol. 2, pp. 359-366, 1989.
[26] Jacobs, R. A., “Increased rates of convergence through learning rate adaptation,” Neural Networks, Vol. 1, pp. 295-307, 1988.
[27] Sietsma, J. and R. J. F. Dow, “Neural net pruning — why and how,” IEEE International Conference on Neural Networks, Vol. 1, pp. 325-333, 1988.
[28] Setiono, R. A., “Penalty function approach for pruning feedforward neural networks,” Neural computation, Vol. 9, pp. 301-320, 1997.
[29] Apolloni, B., G. Avanzini, N. Cesa-Bianchi and G. Ronchini, “Diagnosis of Epilepsy via Backpropagation,” International Joint Conference on Neural Networks, Vol. 2, pp. 571-574, 1990.

中文部分
[30] 葉怡成，「類神經網路模式應用與實作」，第八版，儒林圖書有限公司，民國九十二年。
[31] 侯惠月，「統計方法與類神經網路在台股指數期貨之研究」,國立成功大學碩士論文，民國八十八年。
[32] 陳國玄，「人工神經網路與統計方法應用於台灣上市公司電子指數預測與分類之研究」，國立成功大學碩士論文，民國九十二年。
[33] 蔡宗憲，「類神經網路模式於短期列車旅運量需求預測之應用」,國立成功大學博士論文，民國九十五年。
[34] 蘇朝墩，「類神經網路模式與應用---上課補充教材」，清華大學工工系，台灣，民國九十五年。
[35] 陳景堂，統計分析：SPSS for Windows 入門與應用，四版，儒林圖書有限公司，台北，民國八十九年。
[36] 陳順宇、鄭碧娥，統計學，華泰書局，三版，台北，民國八十七年。