CNN–BiLSTM Attention Hybrid Modeling Czochralski Silicon Diameter Prediction #WorldResearchAwards

Introduction

High-precision prediction of crystal diameter during the growth of electronic-grade silicon single crystals is a crucial requirement for achieving superior crystal quality and yield in semiconductor manufacturing. The Czochralski (Cz) crystal growth process operates under extreme thermal conditions and exhibits strong nonlinear behavior, time-delay effects, and sensitivity to external disturbances. These challenges significantly restrict the prediction accuracy of traditional mechanism-based models, which rely on simplified heat-transfer principles and geometric assumptions. As industrial demand for larger and defect-free silicon wafers continues to rise, advanced predictive strategies capable of handling complex process dynamics have become increasingly essential.

Limitations of Mechanism-Based Diameter Models

Mechanism-based models in crystal growth typically describe the relationship between heater power, pulling rate, and crystal diameter through physics-informed heat-transfer and geometric formulations. While these models provide interpretability and physical insight, they struggle to accurately represent real-world industrial conditions due to unmodeled disturbances, parameter uncertainties, and nonlinear coupling effects. Moreover, the presence of time delays and fluctuating thermal environments further degrades model performance, particularly during critical growth phases such as shoulder formation and constant-diameter stabilization.

Hybrid CNN–BiLSTM–Attention Modeling Framework

To overcome the limitations of conventional approaches, a hybrid deep learning framework integrating convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and self-attention mechanisms is proposed. This architecture is designed to simultaneously capture spatial correlations, temporal dependencies, and feature importance within multi-source sensor data. By combining these complementary components, the hybrid model significantly enhances its representational capacity, making it well-suited for modeling the complex, nonlinear dynamics of the Cz crystal growth process.

Feature Extraction and Temporal Dependency Learning

Within the proposed framework, the CNN component is employed to extract localized spatial features from high-dimensional, multi-sensor industrial data, effectively reducing noise and emphasizing meaningful patterns. The BiLSTM network then processes these features to capture long-range temporal dependencies in both forward and backward time directions. This bidirectional learning capability is particularly advantageous for crystal diameter prediction, as the growth process is influenced by both past and evolving operational conditions.

Role of Self-Attention in Diameter Prediction

The self-attention mechanism further strengthens the hybrid model by dynamically assigning weights to critical features and time steps during the prediction process. This allows the model to focus on the most influential process variables under varying operating conditions, improving adaptability and robustness. By selectively emphasizing key information, the attention module enhances prediction accuracy, especially during transient growth stages where process variability is most pronounced.

Industrial Validation and Performance Evaluation

The proposed hybrid CNN–BiLSTM–Attention model is validated using real operational data collected from an industrial Czochralski furnace (model TDR-180). Experimental results demonstrate that the model achieves superior prediction accuracy and robustness compared to both mechanism-based models and single data-driven baselines. These improvements highlight the model’s strong potential for practical deployment in industrial process control, enabling optimized diameter regulation, reduced defect rates, and enhanced production efficiency in silicon single-crystal manufacturing.

Global Particle Physics Excellence Awards


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#worldresearchawards #CrystalGrowth #SiliconSingleCrystal #CzochralskiProcess #DeepLearningResearch #CNNBiLSTM #AttentionMechanism #IndustrialAI #SmartManufacturing #ProcessControl #SemiconductorResearch #AdvancedModeling #NonlinearSystems #TimeSeriesPrediction #AIinEngineering #MaterialsScience #DataDrivenModels #Industry40 #HighPrecisionManufacturing

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