1 |
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
|
2 |
Han, Y. and Song, Y. H. (2003). Condition monitoring techniques for electrical equipment-a literature survey. IEEE Transactions on Power Delivery, 18, 4-13.
DOI
|
3 |
Quinlan, J. R. (1997). C5.0. http://www.rulequest.com/see5-info.html.
|
4 |
Ramsay, J. O., Hooker, G., and Graves, S. (2009). Functional data analysis with R and MATLAB: Springer Science & Business Media.
|
5 |
Romero-Torres, S., Moyne, J., and Kidambi, M. (2017). Towards Pharma 4.0; Leveraging Lessons and Innovation from Silicon Valley. American Pharmaceutical Review, 5.
|
6 |
SEMI, S. (2014). E133-1014-SEMI Standard Specification for Automated Process Control Systems Interface. Milpitas, CA,(Semiconductor Equipment and Materials.)
|
7 |
Shang, C. and You, F. (2019). Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era, Engineering, 5, 1010-1016.
DOI
|
8 |
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence research, 16, 321-357.
DOI
|
9 |
Wuest, T., Irgens, C., and Thoben, K. D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25, 1167-1180.
DOI
|
10 |
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
DOI
|
11 |
Chen, Y. J., Wang, B. C., Wu, J. Z., Wu, Y. C., and Chien, C. F. (2017). Big data analytic for multivariate fault detection and classification in semiconductor manufacturing. In 2017 13th IEEE Conference on Automation Science and Engineering (CASE) (pp. 731-736). IEEE.
|
12 |
Dalpiaz, G. and Rivola, A. (1997). Condition monitoring and diagnostics in automatic machines: comparison of vibration analysis techniques. Mechanical Systems and Signal Processing, 11, 53-73.
DOI
|
13 |
Chien, C. F., Hsu, C. Y., and Chen, P. N. (2013). Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal, 25, 367-388.
DOI
|
14 |
Choqueuse, V., Benbouzid, M. E. H., Amirat, Y., and Turri, S. (2011). Diagnosis of three-phase electrical machines using multidimensional demodulation techniques. IEEE Transactions on Industrial Electronics, 59, 2014-2023.
DOI
|
15 |
Cortes, C. and Vapnik, V. (1995). Support vector machine. Machine Learning, 20, 273-297.
DOI
|
16 |
Davis, J., Edgar, T., Porter, J., Bernaden, J., and Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145-156.
DOI
|
17 |
Fan, S. K. S., Hsu, C. Y., Tsai, D. M., He, F., and Cheng, C. C. (2020). Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing. IEEE Transactions on Automation Science and Engineering.
|
18 |
Gertler, J. and Cao, J. (2004). PCA based fault diagnosis in the presence of control and dynamics. AIChE Journal, 50, 388-402.
DOI
|
19 |
International Technology Roadmap for Semiconductors (ITRS): Factory Integration Chapter, 2016 Edition. Available online: www.itrs2.net (accessed on 1 June 2017).
|
20 |
Moyne, J., Schulze, B., Iskandar, J., and Armacost, M. (2016, May). Next generation advanced process control: Leveraging big data and prediction. In 2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) (pp. 191-196). IEEE.
|
21 |
Hastie, T., Tibshirani, R., and Buja, A. (1994). Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89, 1255-1270.
DOI
|
22 |
International Roadmap for Devices and Systems (IRDS): Factory Integration White Paper, 2016 Edition. Available online: http://irds.ieee.org/images/files/pdf/2016FI.pdf (accessed on 1 June 2017).
|
23 |
Khan, A. A., Moyne, J. R., and Tilbury, D. M. (2008). Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares. Journal of Process Control, 18, 961-974.
DOI
|
24 |
Li, G., Qin, S. J., and Zhou, D. (2010). Geometric properties of partial least squares for process monitoring. Automatica , 46, 204-210.
DOI
|
25 |
Lujan-Moreno, G. A., Howard, P. R., Rojas, O. G., and Montgomery, D. C. (2018). Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study, Expert Systems with Applications, 109, 195-205.
DOI
|
26 |
May, G. S., and Spanos, C. J. (2006). Fundamentals of Semiconductor Manufacturing and Process Control. John Wiley & Sons.
|
27 |
Moyne, J. and Armacost, M. (2017, March). Big Data Analytics Applied to Semiconductor Manufacturing. In 2017 Spring Meeting and 13th Global Congress on Process Safety. AIChE.
|
28 |
Cheng, F. T., Kao, C. A., Chen, C. F., and Tsai, W. H. (2014). Tutorial on applying the VM technology for TFT-LCD manufacturing. IEEE Transactions on Semiconductor Manufacturing, 28, 55-69.
DOI
|
29 |
Moyne, J. and Iskandar, J. (2017). Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing. Processes, 5, 39.
DOI
|
30 |
Cheng, F. T., Huang, H. C., and Kao, C. A. (2011). Developing an automatic virtual metrology system. IEEE Transactions on Automation Science and Engineering, 9, 181-188.
DOI
|