| FANG Wei,WANG Yuyu,SONG Zhilong,LYU Binghai,ZHAO Wenhong.Prediction of CMP Polishing Material Removal Rate of Semiconductor Wafers Based on Multi-source Data Fusion[J],53(2):150-157, 167 |
| Prediction of CMP Polishing Material Removal Rate of Semiconductor Wafers Based on Multi-source Data Fusion |
| Received:January 01, 2023 Revised:July 05, 2023 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2024.02.014 |
| KeyWord:CMP MRR data fusion deep auto-encoder BP neural network prediction |
| Author | Institution |
| FANG Wei |
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| WANG Yuyu |
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| SONG Zhilong |
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| LYU Binghai |
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| ZHAO Wenhong |
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
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| Abstract: |
| The multi-source data, such as process parameters, consumption of consumables and polishing pad state parameters, were preprocessed for multi-source data fusion, and the material removal rate (MRR) prediction model was established to realize the decision of semiconductor wafer polishing process. The data characteristics and data fusion requirements of multiple sources of data in wafer polishing were analyzed. The statistical features of each wafer in the original data set were extracted to create a new data set. In order to address the impact of dynamic factors in wafer processing on the material removal rate, the neighborhood features were introduced. There were multiple sources and multiple modes of data in polishing of semiconductor wafers, such as the contact pressure between the wafer and the vehicle, the flow rate change of the polishing slurry, and the rotational speed of the polishing disc. There was a certain correlation between the data of these modalities, each modality could provide specific information for the other modal data. Processing the data of different modalities in the same way or simply concatenating all modal features could not guarantee the effectiveness of subsequent regression prediction. An efficient method to combine and reduce the dimensionality of these multiple sources and multiple modes of data was required. The material removal rate prediction method with deep automatic encoder for multi-source data fusion was proposed:the deep autoencoder parameters was designed and the loss function of the deep autoencoder was optimized to enhance the reconstruction of strongly correlated characteristic variables by the deep autoencoder. Considering the degree of data completeness of samples, completeness weights were introduced to reduce the impact of incomplete data on model training. Multi-source sensor signal fusion and data dimensionality reduction based on deep autoencoder were performed, and the fused data were fed into a BP neural network for MRR prediction during semiconductor wafer polishing (The hyperparameters of the BP neural network were optimized by the Hyperband hyperparameter search algorithm). The model was verified in the PHM2016 data for comparative analysis with other models (linear regression model, KNN regressor model and BP neural network model). The model in this study was based on an deep auto-encoder to achieve feature fusion and dimensionality reduction, and the processed data were employed in a BP neural network to fit the material removal rate of semiconductor wafers. Compared with the linear regression model (the mean square error MSE reached 31.186), the KNN regressor model (the mean square error MSE reached 26.17 and the correlation R2 reached 54.3%) and the BP neural network model (the mean square error MSE reached 11.445 and the correlation R2 reached 82.4%), the model in this study had a prediction value closer to the actual value in the three scenarios, with the mean square error MSE reached 7.862 and the correlation R2 reached 91.2%. The model based on multi-source data fusion can effectively predict MRR, which plays a benign auxiliary role in intelligent decision-making and control of the CMP process of semiconductor wafers. |
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