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Computational Intelligence and Neuroscience
Volume 2019, Article ID 8729367, 16 pages
https://doi.org/10.1155/2019/8729367
Research Article

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

1Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
2X-FAB Sarawak Sdn. Bhd., 1 Silicon Drive, Sama Jaya Free Industrial Zone, 93350 Kuching, Sarawak, Malaysia

Correspondence should be addressed to Kang Leng Chiew; ym.saminu@weihclk

Received 19 September 2018; Revised 22 November 2018; Accepted 12 December 2018; Published 2 January 2019

Academic Editor: Paolo Gastaldo

Copyright © 2019 Tze Chiang Tin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. Ramírez-Hernández, J. Crabtree, X. Yao et al., “Optimal preventive maintenance scheduling in semiconductor manufacturing systems: software tool and simulation case studies,” IEEE Transactions on Semiconductor Manufacturing, vol. 23, no. 3, pp. 477–489, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Tian and L. Pan, “Predicting short term traffic flow by long short-term memory recurrent neural network,” in Proceedings of 2015 IEEE International Conference on Smart City, pp. 153–158, Chengdu, China, December 2015.
  3. K. Zhang, J. Xu, M. R. Min, G. Jiang, K. Pelechrinis, and H. Zhang, “Automated IT system failure prediction: a deep learning approach,” in Proceedings of 2016 IEEE International Conference on Big Data (Big Data), pp. 1291–1300, Washington, DC, USA, March 2016.
  4. G. Zhu, L. Zhang, P. Shen, and J. Song, “Multimodal gesture recognition using 3D convolution and convolutional LSTM,” IEEE Access, vol. 5, pp. 4517–4524, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Lai, B. Chen, T. Tan, S. Tong, and K. Yu, “Phone-aware LSTM-RNN for voice conversion,” in Proceedings of 2016 IEEE 13th International Conference on Signal Processing (ICSP), pp. 177–182, Chengdu, China, November 2016.
  6. A. ElSaid, B. Wild, J. Higgins, and T. Desell, “Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines,” in Proceedings of 2016 IEEE 12th International Conference on e-Science, pp. 260–269, Baltimore, MD, USA, October 2016.
  7. J. Wang, J. Zhang, and X. Wang, “A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system,” IEEE Transactions on Semiconductor Manufacturing, vol. 31, no. 1, pp. 173–182, 2018. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Wang, J. Zhang, and X. Wang, “Bilateral LSTM: a two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems,” IEEE Transactions on Industrial Informatics, vol. 14, no. 2, pp. 748–758, 2018. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Scholl, B. P. Gan, P. Lendermann et al., “Implementation of a simulation-based short-term lot arrival forecast in a mature 200 mm semiconductor FAB,” in Proceedings of 2011 Winter Simulation Conference (WSC), pp. 1927–1938, Phoenix, AZ, USA, December 2011.
  10. M. Mosinski, D. Noack, F. S. Pappert, O. Rose, and W. Scholl, “Cluster based analytical method for the lot delivery forecast in semiconductor fab with wide product range,” in Proceedings of 2011 Winter Simulation Conference (WSC), pp. 1829–1839, Phoenix, AZ, USA, December 2011.
  11. H. K. Larry, “Event-based short-term traffic flow prediction model,” Transportation Research Record, vol. 1510, pp. 45–52, 1995. View at Google Scholar
  12. B. M. Williams and L. A. Hoel, “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results,” Journal of Transportation Engineering, vol. 129, no. 6, pp. 664–672, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Van Der Voort, M. Dougherty, and S. Watson, “Combining Kohonen maps with ARIMA time series models to forecast traffic flow,” Transportation Research Part C: Emerging Technologies, vol. 4, no. 5, pp. 307–318, 1996. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Xie, Y. Zhang, and Z. Ye, “Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition,” Computer-Aided Civil and Infrastructure Engineering, vol. 22, no. 5, pp. 326–334, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Huang, G. Song, H. Hong, and K. Xie, “Deep architecture for traffic flow prediction: deep belief networks with multitask learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2191–2201, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Abadi, T. Rajabioun, and P. A. Ioannou, “Traffic flow prediction for road transportation networks with limited traffic data,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 653–662, 2015. View at Google Scholar
  17. R. Fu, Z. Zhang, and L. Li, “Using LSTM and GRU neural network methods for traffic flow prediction,” in Proceedings of 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328, Wuhan, China, November 2016.
  18. H. Shao and B. Soong, “Traffic flow prediction with long short-term memory networks (LSTMs),” in Proceedings of 2016 IEEE Region 10 Conference (TENCON), pp. 2986–2989, Singapore, November 2016.
  19. M. S. Ahmed and A. R. Cook, “Analysis of freeway traffic time-series data by using Box-Jenkins Techniques,” Transportation Research Record, vol. 773, no. 722, pp. 1–9, 1979. View at Google Scholar
  20. I. Okutani, “The Kalman filtering approaches in some transportation and traffic problems,” Transportation Research Record, vol. 2, no. 1, pp. 397–416, 1987. View at Google Scholar
  21. I. Okutani and Y. J. Stephanedes, “Dynamic prediction of traffic volume through Kalman filtering theory,” Transportation Research Part B: Methodological, vol. 18, no. 1, pp. 1–11, 1984. View at Publisher · View at Google Scholar · View at Scopus
  22. H. J. H. Ji, A. X. A. Xu, X. S. X. Sui, and L. L. L. Li, “The applied research of Kalman in the dynamic travel time prediction,” in Proceedings of 18th International Conference on Geoinformatics, pp. 1–5, Beijing, China, June 2010.
  23. Y. Wang and M. Papageorgiou, “Real-time freeway traffic state estimation based on extended Kalman filter: a general approach,” Transportation Research Part B: Methodological, vol. 39, no. 2, pp. 141–167, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends® in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proceedings of NIPS, Lake Tahoe, NV, USA, December 2012.
  26. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Collobert and J. Weston, “A unified architecture for natural language processing: deep neural networks with multitask learning,” in Proceedings of 25th ICML, pp. 160–167, Helsinki, Finland, July 2008.
  28. I. J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet, “Multi-digit number recognition from street view imagery using deep convolutional neural networks,” 2013, https://arxiv.org/abs/1312.6082. View at Google Scholar
  29. B. Huval, A. Coates, and A. Ng, “Deep learning for class-generic object detection,” 2013, https://arxiv.org/abs/1312.6885. View at Google Scholar
  30. H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1930–1943, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2015. View at Google Scholar
  32. R. Zhao, J. Wang, R. Yan, and K. Mao, “Machine health monitoring with LSTM networks,” in Proceedings of 2016 10th International Conference on Sensing Technology (ICST), pp. 1–6, Nanjing, China, November 2016.
  33. X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 187–197, 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 3, pp. 211–234, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. View at Publisher · View at Google Scholar · View at Scopus
  36. Keras: The Python Deep Learning Library, 2018, https://keras.io/.