Materials Science in Semiconductor Processing
GRU-XGBoost 模型
GRU-XGBoost modelA machine learning model combining Gated Recurrent Units (GRU) and Extreme Gradient Boosting (XGBoost) for predicting warpage and MP-stress in glass substrates / 一种结合门控循环单元（GRU）和极限梯度提升（XGBoost）的机器学习模型，用于预测玻璃基板的翘曲和MP应力。
- Knowledge type
- 工艺技术
- First proposed or discovered
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- Turkey Farm member
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- 农场主
- Last updated
- 2026-10-30 22:05:53
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Main parameters and performance indicators
multi-process-step correlation features: Input data representing correlations across multiple processing stepsGRU: Gated Recurrent Unit component for sequence modelingXGBoost: Extreme Gradient Boosting component for regression
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Related literature
ranked by community starsPrediction of warpage and MP-stress in glass substrates based on multi-process-step correlation features and GRU-XGBoost modelOpen source
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1 recordsRecord title: GRU-XGBoost model.
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