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ZHANG Zhongyi (2000-), male, Assistant Engineer. E-mail: 2841622099@qq.com
WANG Lang (1989-), male, Engineer. E-mail:wangl170326@163.com
Outline
Silicon carbide (SiC), as a representative third-generation wide-bandgap semiconductor material, exhibits superior performance over traditional silicon-based devices in high-voltage, high-power, and high-frequency applications. The doping concentration of the SiC epitaxial layer critically determines the performance of power devices. However, current doping control heavily relies on manual experience and offline testing, which incurs high costs and significant time delays. This study aims to address these limitations by proposing a data-driven model for accurate and real-time prediction of doping concentration in both in-situ and next-batch scenarios.
This paper proposes a Multi-scale Feature-enhanced Temporal Transformer Network (MFT-NET) for predicting the core-layer doping concentration during SiC epitaxial growth. The model integrates four key modules:
Multi-scale Convolution Module: Employs parallel convolutional branches with kernel sizes of [3,3], [9,3], and [21,5] to capture parameter dynamics across millisecond-to-hour scales.
Squeeze-and-Excitation (SE) Module: Enhances feature channels correlated with doping concentration and suppresses noise via channel-wise attention.
Transformer Module: Models global coupling relationships among all 75 process parameters using multi-head self-attention.
Gated Recurrent Unit (GRU) Module: Captures cross-batch state dependencies to account for residual dopant and equipment state drift between consecutive epitaxial runs.
The model was trained and evaluated on a dataset comprising 1200 epitaxial runs from a commercial SiC epitaxial reactor. Data preprocessing included Pearson correlation-based feature selection and min-max normalization. A weighted mean squared error loss function was adopted to prioritize mainstream process samples.
Experimental results demonstrate that MFT-NET significantly outperforms traditional models including Ridge Regression, Support Vector Regression (SVR), CNN, CNN+LSTM, Transformer, and CNN+Transformer. In the in-situ prediction scenario, MFT-NET achieved a mean absolute percentage error (MAPE) of 1.35%, a root mean square error (RMSE) of 2.81×1014 cm-3, and a coefficient of determination (R²) of 0.89. For next-batch prediction, it attained a MAPE of 1.66%, RMSE of 3.42×1014cm-3, and R² of 0.87.
Ablation studies confirmed the contribution of each module: the multi-scale convolution and SE modules improved feature extraction and selection; the Transformer enabled global parameter interaction modeling; and the GRU was crucial for capturing cross-batch state transfer, reducing next-batch prediction RMSE by 20.8% compared to the model without GRU.
Hyperparameter analysis revealed that a 3-layer Transformer, the proposed multi-scale kernel combination, and a Dropout ratio of 0.2 yielded optimal performance.
The MFT-NET model effectively addresses the multi-scale dynamics, global parameter coupling, and cross-batch state transfer challenges in SiC epitaxial growth. It provides highly accurate doping concentration predictions for both in-situ monitoring and next-batch forecasting, with the potential to reduce offline testing costs by 30% and improve process stability by 15%. This work offers a robust data-driven framework for intelligent optimization of SiC epitaxial processes.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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