Open Access
MATEC Web Conf.
Volume 119, 2017
The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
Article Number 01021
Number of page(s) 8
Published online 04 August 2017
  1. J.P. Card, M. Naimo, and W. Ziminsky, Run-to-run process control of a plasma etch process with neural network modelling, Qual Reliab Eng. Int., 14 (4), 247-260 (1998) [CrossRef] [Google Scholar]
  2. F.C. Chen, Back-propagation neural networks for nonlinear self-tuning adaptive control, IEEE Contr Syst Mag, 10 (3), 44-48 (1990) [CrossRef] [Google Scholar]
  3. W.C. Chen, A.H. Lee, W.J. Deng, and K.Y. Liu, The implementation of neural network for semiconductor PECVD process, Expert Syst. Appl., 32 (4), 1148-1153 (2007) [CrossRef] [Google Scholar]
  4. J.E. Dayhoff and J.M.D. Leo, Artificial neural networks, Cancer, 91 (S8), 1615-1635 (2001) [Google Scholar]
  5. C.M. Fan, R.S. Guo, S.C. Chang, and C.S. Wei, SHEWMA: an end-of-line SPC scheme using wafer acceptance test data, IEEE T Semi Conduct M, 13 (3), 344-358 (2000) [CrossRef] [Google Scholar]
  6. A. Goh, Back-propagation neural networks for modelling complex systems, Artif Intell Med, 9 (3), 143-151 (1995) [CrossRef] [Google Scholar]
  7. H.C. Pu and Y.T. Hung, Use of artificial neural networks: Predicting trickling filter performance in a municipal wastewater treatment plant, Envion Manage, 6, 16-27 (1995) [Google Scholar]
  8. C.D. Himmel and G.S. May, Advantages of plasma etch modelling using neural networks over statistical techniques, IEEE T Semi Conduct M, 6 (2), 103-111 (1993) [CrossRef] [Google Scholar]
  9. S.J. Hong, W.Y. Lim, T. Cheong, and G.S. May, Fault detection and classification in plasma etch equipment for semiconductor manufacturing e-Diagnostics, IEEE T Semi Conduct M, 25 (1), 83-93 (2012) [CrossRef] [EDP Sciences] [Google Scholar]
  10. SC-IQ: Semiconductor Intelligence, Semiconductors down 2.7% in ‘12, may grow 7.5% in ‘13., (2013) [Google Scholar]
  11. G.S. May and C.J. Spanos, Fundamentals of Semiconductor Manufacturing and Process Control, John Wiley & Sons (2006) [Google Scholar]
  12. K. Mehrotra, C.K. Mohan, and S. Ranka, Artificial Neural Networks, the MIT Press (1997) [Google Scholar]
  13. MIC. Output of Taiwan’s Semiconductor Industry to Reach Approx. US$35 Billion in 2009, Says MIC. from, Accessed 03 March 2015 (2009) [Google Scholar]
  14. J. Li, J. Cheng, J. Shi, and F. Huang, Brief introduction of back propagation (BP) neural network algorithm and its improvement, Advances in Computer Science and Information Engineering, 553-558 (2012) [CrossRef] [EDP Sciences] [Google Scholar]
  15. B.S. de Ugarte, A. Artiba, and R. Pellerin, Manufacturing execution system-a literature review, Prod Plan Control, 20 (6), 525-539 (2009) [CrossRef] [Google Scholar]
  16. Y.E. Shao, C.J. Lu and C.C. Chiu, A fault detection system for an autocorrelated process using SPC/EPC/ANN and SPC/EPC/SVM schemes, Int. J. Innov. Comput. Int., 7 (9), 5417-5428 (2011) [Google Scholar]
  17. G. Smith, Statistical Process Control and Quality Improvement: Prentice Hall, 576 (1998) [Google Scholar]
  18. J.Z. Wu, Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle and product structure with real settings, Computers & Industrial Engineering, 65 (1), 128-136 (2011) [Google Scholar]
  19. H.H. Yue, S.J. Qin, R.J. Markle, C. Nauert, and M. Gatto, Fault detection of plasma etchers using optical emission spectra, IEEE T Semi Conduct M, 13 (3), 374-385 (2000) [CrossRef] [Google Scholar]

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