Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process
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DOI: 10.1007/s10845-018-1437-4
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References listed on IDEAS
- D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
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Cited by:
- Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
- Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
- Liqiao Xia & Pai Zheng & Xiao Huang & Chao Liu, 2022. "A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2295-2306, December.
- 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.
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Keywords
Chemical mechanical planarization; Advanced process control; Virtual metrology; Recurrent neural network; Convolutional neural network;All these keywords.
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