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Material removal rate prediction in chemical mechanical planarization with conditional probabilistic autoencoder and stacking ensemble learning

Author

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  • Yupeng Wei

    (San Jose State University)

  • Dazhong Wu

    (University of Central Florida)

Abstract

Chemical mechanical planarization (CMP) is a complex and high-accuracy polishing process that creates a smooth and planar material surface. One of the key challenges of CMP is to predict the material removal rate (MRR) accurately. With the development of artificial intelligence techniques, numerous data-driven models have been developed to predict the MRR in the CMP process. However, these methods are not capable of considering surface topography in MRR predictions because it is difficult to observe and measure the surface topography. To address this issue, we propose a graphical model and a conditional variational autoencoder to extract the features of surface topography in the CMP process. Moreover, process variables and the extracted features of surface topography are fed into an ensemble learning-based predictive model to predict the MRR. Experimental results have shown that the proposed method can predict the MRR accurately with a root mean squared error of 6.12 nm/min, and it outperforms physics-based machine learning and data-driven methods.

Suggested Citation

  • Yupeng Wei & Dazhong Wu, 2024. "Material removal rate prediction in chemical mechanical planarization with conditional probabilistic autoencoder and stacking ensemble learning," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 115-127, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02040-w
    DOI: 10.1007/s10845-022-02040-w
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    References listed on IDEAS

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    1. Dazhong Wu & Yupeng Wei & Janis Terpenny, 2019. "Predictive modelling of surface roughness in fused deposition modelling using data fusion," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3992-4006, June.
    2. Ki Bum Lee & Chang Ouk Kim, 2020. "Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 73-86, January.
    3. Edoardo M Airoldi, 2007. "Getting Started in Probabilistic Graphical Models," PLOS Computational Biology, Public Library of Science, vol. 3(12), pages 1-5, December.
    Full references (including those not matched with items on IDEAS)

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