偏光板製程上較困難之處在於如何做出一片完全無缺點的偏光板，其中最關鍵的部分就在濕製程，現今工程師仍以經驗法與試誤法找出參數，過程浪費太多的時間及成本。
本研究採用偏光板濕製程的參數及光學特性之資料，以倒傳遞類神經網路的架構，建立一模擬偏光板濕製程的預測模型，利用過去的資料對偏光板光學特性進行預測。其研究發現類神經網路對於偏光板產業濕製程參數有良好的預測結果，可作為製程工程師執行偏光板濕製程參數調整前的參考。

It is increasingly difficult to manufacture zero-defect polarizer of larger sizes. The polarizer manufacturing process consists of several stages, with the first stage-web process-being the most crucial. Nowadays, the choice of the parameters of the web process is based on the technicians’ experiences or some existing empirical rules. However, this approach usually takes a lot of time and hence incurs great cost. Therefore, this study proposes a parameter prediction model by using a neural network approach to help the technicians adjust the parameters in a more efficient way. The numerical study by adopting real-world data in the making of polarizer indicates that the proposed model yields good results and has adequate prediction power. It suggests that the proposed model can be used as a guide for the technicians to adjust the parameters in the web process of polarizer manufacturing.

一、中文部份

李世彪，實驗計畫法在碘系偏光膜延伸染色製程條件之研究，台北科技大學有機高分子研究所學報，2004年。
李柎梓，整合SPC與EPC之研究應用類神經網路於製程平均值變化之偵測，私立元智大學，碩士論文，2002年。
汪培庄，模糊集合論及其應用，台北：中國生產力中心，1990年。
吳柏林，時間數列分析導論，華泰文化，1995年。
張文山，利用類神經網路進行電漿化學氣相沉積製程特性預測之研究，成功大學工資所，碩士論文，2007年。
黃峰蕙、施勵行、林秉山，生產與作業管理，三民書局，1997年。
葉怡成，類神經網路模式應用與實作(八版三刷)，台北：儒林圖書有限
公司，2004年。
陳順宇，迴歸分析，華泰書局，2000年。
羅華強，類神經網路-MATLAB的應用，高立圖書有限公司，2005年。

二、英文部份

Aburto, L., Weber. R., “Improved supply chain management based on hybrid
demand forecasts.” Applied Soft Computing, 2005.
Cheng, C. S., “A Multi-layer neural network model for detecting changes in the process mean.”Computers and Industrial Engineering. 28 (1) 51-61, 1995.
Cheng, C. S. and C. A. Aw, “A neural fuzzy control chart for detecting and classifying process mean shifts.” International Journal of Production Research. 34 (8) 2265-2278, 1996.
Chu, C. W., and Zhang G. P., “A comparative study of linear and nonlinear models for aggregate retail sales forecasting.” International Journal of Production Economics. 86(3).217-231, 2003.
Cook, D. and C. C. Chiu, “Using radial basis function neural networks ro recognize shifts in correlated manufacturing process parameters.” IIE Transactions. 30 (8) 227-234, 1998.
Cui, W. Z., Zhu, C. C., and Zhau, H. P., “Prediction of thin film thickness of field emission using wavelet neural networks.” Thin Solid Films, 473, 224-229, 2005
Delurgio, Stephen A, “Forecasting principles and applications.” University of Missouri Cansas City, First Edition, 1999.
Dia, H., “An object-oriented neural network approach to short-term traffic
forecasting.” European Journal of Operational Research, 131, 253-261, 2001.
Hush, D. R., and Horne, B. G., “Progress in supervised neural networks：What’s new since lippmann,” IEEE Signal Processing Magazine . 8-39, 1993.
Hsieh, K. L., and Tong, L. I., “Optimization of multiple quality responses involving
qualitative and quantitative characteristics in IC manufacturing using neural
networks.” Computers in Industry, 46, 1-12, 2001.
Kim, B., and Kim, S., “Diagnosis of plasma processing equipment using neural
network recognition of wavelet-filtered impedance matching.” Microelectronic
Engineering,82, 44-52, 2005.
Li, D. C., Lina, Y. S., Tsai, T. I., and Wu, D. C., “Using mega-trend-diffusion and
artificial samples in small data set learning for early flexible manufacturing
system scheduling knowledge.” Computers & Operations Research, 34,
966-982, 2005.
Martinez, E. E., Smith, A. E., and Idanda, B., “Reducing waste in casting with a predictive neural model,” Journal of Intelligent Manufacturing. 5 (4) 277-286, 1994.
Montgomery, D. C., Introduction to Statistical Quality Control, New York, 1996.
Pyzdek, Thomas, and R. W. Berger, Quality engineering handbook. ASQC quality press, 1991.
Shao, Y. E. and C. C. Chiu , “Developing identification techniques with the integrated use of SPC/EPC and neural networks. ” Quality and Reliability Engineering International. 15 287-294, 1999.
Tang, Z. et al., “Time series forecasting using Neural Network vs. Box-Jenjins methodology.” Simulation. 15 287-294, 1991.
Wu, M. C., and Chang, W. J.,.“A short-term capacity trading method for semiconductor fabs with partnership.” Expert Systems with Applications.,2006
Wada, Y., and Kawato, M., “Estimation of generalization capability by combination of new Information criterion and cross validation,” Proceedings of International Conference Neural Networks, 2 1-6, 1991.
Wang, S., “An insight into the standard back-propagation neural network model for regression analysis,” International Journal of Management Science, 26 (1) 133-140, 1998.