Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process

• Abdel-Hamid, O., Deng, L., & Yu, D. (2013). Exploring convolutional neural network structures and optimization techniques for speech recognition. In Interspeech (Vol. 2013, pp. 3366–3370). https://pdfs.semanticscholar.org/655a/e6f82c24e3e01b2b27c56512b06ba36d49c1.pdf. Accessed 15 Feb 2018.

• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends® in Machine Learning,2(1), 1–127. https://doi.org/10.1561/2200000006.

  Article  Google Scholar 

• García, V., Sánchez, J. S., Rodríguez-Picón, L. A., Méndez-González, L. C., & de Jesús Ochoa-Domínguez, H. (2018). Using regression models for predicting the product quality in a tubing extrusion process. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1418-7.

  Article  Google Scholar 

• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of neural information processing systems (pp. 2672–2680).

• Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.

• Haeusser, P., Mordvintsev, A., & Cremers, D. (2017). Learning by association-a versatile semi-supervised training method for neural networks. In Proceedings of IEEE conference on computer vision and pattern recognition. arXiv preprint arXiv:1706.00909.

• Hinton, G., Srivastava, N., & Swersky, K. (2012). Lecture 6a overview of mini-batch gradient descent. In Neural networks for machine learning. https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf. Accessed 30 June 2018.

• Hsieh, Y. S., Cheng, F. T., Huang, H. C., Wang, C. R., Wang, S. C., & Yang, H. C. (2013). VM-based baseline predictive maintenance scheme. IEEE Transactions on Semiconductor Manufacturing,26(1), 132–144.

  Article  Google Scholar 

• Jebri, M. A., El Adel, E. M., Graton, G., Ouladsine, M., & Pinaton, J. (2017). Virtual metrology applied in run-to-run control for a chemical mechanical planarization process. Journal of Physics: Conference Series,783, 012042.

  Google Scholar 

• Kang, P., Kim, D., & Cho, S. (2016). Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Expert Systems with Applications,51, 85–106.

  Article  Google Scholar 

• Kang, P., Kim, D., Lee, H. J., Doh, S., & Cho, S. (2011). Virtual metrology for run-to-run control in semiconductor manufacturing. Expert Systems with Applications,38(3), 2508–2522.

  Article  Google Scholar 

• Khan, A. A., Moyne, J. R., & Tilbury, D. M. (2008). Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares. Journal of Process Control,18(10), 961–974.

  Article  Google Scholar 

• Kim, M., Kang, S., Lee, J., Cho, H., Cho, S., & Park, J. S. (2017). Virtual metrology for copper-clad laminate manufacturing. Computers & Industrial Engineering,109, 280–287.

  Article  Google Scholar 

• Ko, H. H., Kim, J., Park, S. H., Baek, J. G., & Kim, S. S. (2012). Advanced semiconductor fabrication process control using dual filter exponentially weighted moving average. Journal of Intelligent Manufacturing,23(3), 443–455.

  Article  Google Scholar 

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of neural information processing systems (pp. 1097–1105).

• Lee, K. B., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing,30(2), 135–142.

  Article  Google Scholar 

• Lee, S. K., Kang, P., & Cho, S. (2014). Probabilistic local reconstruction for k-NN regression and its application to virtual metrology in semiconductor manufacturing. Neurocomputing,131, 427–439.

  Article  Google Scholar 

• Lenz, B., Barak, B., Mührwald, J., & Leicht, C. (2013). Virtual metrology in semiconductor manufacturing by means of predictive machine learning models. In 12th IEEE international conference on machine learning and applications (Vol. 2, pp. 174–177). IEEE.

• Li, Z., Wang, Y., & Wang, K. (2017). A data-driven method based on deep belief networks for backlash error prediction in machining centers. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1380-9.

  Article  Google Scholar 

• Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2018). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1415-x.

  Article  Google Scholar 

• Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.

• Luo, M., Yan, H. C., Hu, B., Zhou, J. H., & Pang, C. K. (2015). A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Computers & Industrial Engineering,85, 414–422.

  Article  Google Scholar 

• May, G. S., & Spanos, C. J. (2006). Fundamentals of semiconductor manufacturing and process control. Hoboken, NJ: Wiley.

  Book  Google Scholar 

• Melhem, M., Ananou, B., Ouladsine, M., & Pinaton, J. (2016). Regression methods for predicting the product’s quality in the semiconductor manufacturing process. IFAC-PapersOnLine,49(12), 83–88.

  Article  Google Scholar 

• Moyne, J., Del Castillo, E., & Hurwitz, A. M. (2001). Run-to-run control in semiconductor manufacturing. New York: CRC Press LLC.

  Google Scholar 

• Moyne, J., Schulze, B., Iskandar, J., & Armacost, M. (2016). Next generation advanced process control: Leveraging big data and prediction. In 27th IEEE international conference on advanced semiconductor manufacturing (pp. 191–196). IEEE.

• Nakata, K., Orihara, R., Mizuoka, Y., & Takagi, K. (2017). A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing,30(4), 339–344.

  Article  Google Scholar 

• Pampuri, S., Schirru, A., Fazio, G., & De Nicolao, G. (2011). Multilevel lasso applied to virtual metrology in semiconductor manufacturing. In 2011 IEEE international conference on automation science and engineering (pp. 244–249). IEEE.

• Park, C., & Kim, S. B. (2016). Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm. Journal of Process Control,42, 51–58.

  Article  Google Scholar 

• Pimenov, D. Y., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing,29(5), 1045–1061.

  Article  Google Scholar 

• Purwins, H., Barak, B., Nagi, A., Engel, R., Hockele, U., Kyek, A., et al. (2014). Regression methods for virtual metrology of layer thickness in chemical vapor deposition. IEEE/ASME Transactions on Mechatronics,19(1), 1–8.

  Article  Google Scholar 

• Sharma, D., Armer, H., & Moyne, J. (2012). A comparison of data mining methods for yield modeling, chamber matching and virtual metrology applications. In 23rd IEEE international conference on advanced semiconductor manufacturing (pp. 231–236). IEEE.

• Somari, N. M., Abdullah, M. F., Osman, M. K., Nazelan, A. M. I., Ahmad, K. A., Appanan, S. P. R. S., & Hooi, L. K. (2016). Particles contaminations detection during plasma etching process by using k-nearest neighbors and fuzzy k-nearest neighbors. In 6th IEEE international conference on control system, computing and engineering (pp. 512–516). IEEE.

• Susto, G. A., Johnston, A. B., O’Hara, P. G., & McLoone, S. (2013). Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes. In 2013 IEEE international conference on automation science and engineering (pp. 201–206). IEEE.

• Susto, G. A., Pampuri, S., Schirru, A., Beghi, A., & De Nicolao, G. (2015). Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach. Computers & Operations Research,53, 328–337.

  Article  Google Scholar 

• Teixidor, D., Grzenda, M., Bustillo, A., & Ciurana, J. (2015). Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. Journal of Intelligent Manufacturing,26(4), 801–814.

  Article  Google Scholar 

• Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research. http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf. Accessed 10 Jan 2018.

• Wang, P., Gao, R. X., & Yan, R. (2017). A deep learning-based approach to material removal rate prediction in polishing. CIRP Annals,66(1), 429–432.

  Article  Google Scholar 

• Werbos, P. J. (1990). Backpropagation through time: What it does and how to do it. Proceedings of the IEEE,78(10), 1550–1560.

  Article  Google Scholar 

• Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818–833). Berlin: Springer.

Download references