Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network
Abstract
:1. Introduction
- The proposed method is a deep learning model that can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Additionally, the remaining features can go through a deep neural network to learn discriminative feature embeddings.
- The experiments are performed on the dataset from the 2016 PHM Data Challenge. To the best of our knowledge, the performance based on the proposed model outperforms all other methods in the existing literature.
- Finally, the prediction accuracy of the proposed method is demonstrated through extensive experiments.
2. Related Work
3. Proposed Method
3.1. Dataset
3.2. Outlier Detection
3.3. Feature Engineering
3.4. Fusion Network
4. Results and Discussions
4.1. Comparison Methods
4.2. Evaluation Metric
4.3. Experimental Results
4.4. Fusion Network vs. Shallow Network vs. Deep Network
4.5. Ensemble Learning
4.6. The Impact of Dropout Probability and Batch Normalization
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Group | Chamber ID | Stage ID | MRR Range | Sample Size | |
---|---|---|---|---|---|
Training | Testing | ||||
High-speed | 1, 2, 3 | A | 138–163 | 431 | 73 |
Low-speed (A) | 4, 5, 6 | A | 53–89 | 983 | 165 |
Low-speed (B) | 4, 5, 6 | B | 53–102 | 987 | 186 |
Parameter | Description |
---|---|
Dropout probability | 0.1 |
Optimizer | Adam |
Initial learning rate | 0.01 |
Batch size | 16 |
Number of epochs | 100 |
Activation function | ReLU |
Loss function | Loss |
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Liu, C.-L.; Tseng, C.-J.; Hsaio, W.-H.; Wu, S.-H.; Lu, S.-R. Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network. Appl. Sci. 2022, 12, 11478. https://doi.org/10.3390/app122211478
Liu C-L, Tseng C-J, Hsaio W-H, Wu S-H, Lu S-R. Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network. Applied Sciences. 2022; 12(22):11478. https://doi.org/10.3390/app122211478
Chicago/Turabian StyleLiu, Chien-Liang, Chun-Jan Tseng, Wen-Hoar Hsaio, Sheng-Hao Wu, and Shu-Rong Lu. 2022. "Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network" Applied Sciences 12, no. 22: 11478. https://doi.org/10.3390/app122211478
APA StyleLiu, C. -L., Tseng, C. -J., Hsaio, W. -H., Wu, S. -H., & Lu, S. -R. (2022). Predicting the Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing Using a Fusion Network. Applied Sciences, 12(22), 11478. https://doi.org/10.3390/app122211478