[1]呂學地(2009),「應用資料探勘技術於半導體晶圓允收測詴參數預測之研究」,元智大學工業工程與管理研究所碩士論文。[2]李宗霖(2010),「叢聚式迴歸之收斂性質改善及其於晶圓允收測試資料建模的應用」,元智大學工業工程與管理研究所碩士論文。[3]林瑞山(2004),「類神經網路於預測晶圓測試良率之應用」,成功大學工程管理學系研究所碩士論文。[4]周文賢,「多變量統計分析: SAS/STAT使用方法」,智勝出版,2002年。
[5]張中瀚(2010),「結合主成分分析與叢聚式迴歸於晶圓允收測試資料之建模」,元智大學工業工程與管理研究所碩士論文。[6]張其聖(2008),「半導體產業研發設計階段以WAT參數資料建構黃金晶方分析模型」,國立清華大學工業工程與工程管理學系研究所碩士論文。[7]陳昌榮,林育臣(2002),「群聚演算法之比較及群聚參數的分析與探討」,朝揚科技學資訊管理研究所碩士論文。[8]游淑敏(2008),「以WAT參數資料建構半導體研發設計階段黃金晶方群聚分析」,國立清華大學工業工程與工程管理學系研究所碩士論文。[9]黃賢文(2009),「建立半導體晶圓允收測試參數之預測模型—以電容為例」,元智大學工業工程與管理研究所碩士論文。[10]葉怡成,「類神經網路模式應用與實作」,儒林出版社,2003年。
[11]蕭博文(2006),「台灣光電產業經營績效評估-結合DEA與DB-SCAN群聚分析」,國立交通大學管理學院碩士論文。[12]DeSarbo, W. S., R. L. Oliver, and A. Rangaswamy (1989). “A simulated annealing methodology for clusterwise linear regression”, Psychometrika, 54, 70–736.
[13]DeSarbo,W. S. and D. Grisaffe (1998).“ Combinatorial optimization approaches to constrainedmarket segmentation: An application to industrial market segmentation”, Marketing Letters, 9, 115–134.
[14]Dempster, A. P., N. M. Laird and D. B. Rubin (1977). “Maximum likelihood from incomplete data via the E-M algorithm”, Journal of the Royal Statistical Society B, 39, 1–38.
[15]Fish, K. E., J. H. Barnes and M. W. Aiken(1995). “Artificial neural networks: a new methodology for industrial market segmentation,” Industrial Marketing Management, 24, 431-438.
[16]Harrison, C.A., R. Good, D. Kadosh and S.J. Qin (2003), “Multi-step supervisory control of flash memory device production via a simple first-principles model”, In: AEC/APC Symposium XV.
[17]Jeff Wu, C. F. (1983), “On the Convergence Properties of the EM Algorithm, The Annals of Statistics”, Vol. 11, No. 1, pp. 95-103.
[18]Kohonen, T. (1982), “Self-organized formation of topologically correct feature maps” , Biological Cybernetics, 43, pp. 59-69.
[19]Kohonen, T. (1990), “The self-organizing map”, Proceedings of the IEEE, 78, pp. 1464-1480.
[20]Lu,C.Y. and Fan,C.M. (2009), “Correlation analysis between wafer acceptance test and in-line data for process control”, In: AEC/APC Symposium
[21]Leisch, F. (2004). “FlexMix: A general framework for finite mixture models and latent class regression in R”, Journal of Statistical Software, 11, 1–18.
[22]Muller, C. H., and T. Garlipp (2005). “Simple consistent cluster methods based on redescending Mestimators with an application to edge identification in images”, Journal of Multivariate Analysis, 92, 359–385.
[23]Moyne, J. (2004). “Making the move to fab-wide apc”, Solid State Technology 47 (9), 47
[24]Qin, S.J., G. Cherry, R. Good, J. Wang and C. A. Harrison (2004). “Control and monitoring of semiconductor manufacturing processes: Challenges and opportunities”, In: IFAC symposium on dynamics and control of process systems.
[25]Qin, S.J., G. Cherry, R. Good, J. Wang and C.A. Harrison (2006). “Semiconductor manufacturing process control and monitoring: A fab-wide framework”, Journal of Process Control16 (3), 179-191.
[26]Schoene, C., Qin, S.J., Kutanoglu, E. and Stuber, J. (2007), “Electrical parameter control for semiconductor device manufacturing: a fabwide approach”, In: IFAC symposium on dynamics and control of process systems.
[27]Spath, H. (1979). “Algorithm 39: Clusterwise linear regression”, Computing, 22, 367–373
[28]Spath, H. (1982). “Algorithm 48: A fast algorithm for clusterwise linear regression”, Computing, 29,175–181.
[29]Vellido, A., P. J. G. Lisboa and J. Vaughan(1999). “Neural networks in business: a survey of applications,” Expert Systems with Applications, 17, 51-70.