Research on the Optimization of Control Methods for Czochralski Single-Crystal Furnace

Research on the Optimization of Control Methods for Czochralski Single-Crystal Furnace

I. Introduction

II. Traditional Control Methods and Their Limitations

1. Experience-Dependent: The quality of crystals is highly dependent on the skills and experience of the operators, making it difficult to achieve high-precision and stable growth.
2. Lack of Real-Time Feedback: Traditional methods cannot monitor parameter changes in real-time during the crystal growth process, making it difficult to adjust growth conditions promptly.
3. Low Efficiency: Manual adjustment processes are cumbersome and not suitable for large-scale production.

III. Optimization of Intelligent Control Methods

1. Fuzzy Variable Coefficient PID Control Algorithm
• This algorithm introduces fuzzy logic control to dynamically adjust the parameters of the PID controller, adapting to the complex changes during crystal growth. Experiments have shown that the fuzzy variable coefficient PID control algorithm can significantly improve the precision and stability of temperature control, with control accuracy within the allowable range.
2. Closed-Loop Control Based on Weight Feedback
• The JGD single-crystal furnace uses a high-precision electronic scale as a sensor, with crystal weight as the control target. The control software compares the theoretical weight of the crystal with the actual weight, forming a closed-loop negative feedback system and utilizing PID theory for intelligent control. This method not only improves the automation level of crystal growth but also reduces human intervention.
3. Big Data Analysis and Remote Monitoring
• Modern single-crystal furnace control systems integrate SQL Server databases to collect data from the growth process, enabling big data analysis and remote monitoring functions. These functions not only improve production efficiency but also facilitate post-analysis and process optimization.

IV. Advantages of Optimized Control Methods

1. Improved Crystal Quality: Intelligent control methods can monitor and adjust growth conditions in real-time, reducing defects in the crystal growth process and enhancing the optical and electrical properties of the crystals.
2. Increased Production Efficiency: Automated control reduces human intervention, shortens the crystal growth cycle, and improves production efficiency.
3. Reduced Production Costs: By optimizing control methods, raw material waste and energy consumption are reduced, lowering production costs.

V. Future Development Directions

1. Multiphysics Coupling Control: Combining multiphysics coupling models of temperature fields, stress fields, and flow fields to achieve more precise control.
2. Artificial Intelligence and Machine Learning: Utilizing AI and machine learning algorithms to further optimize control strategies and enhance the system's adaptability.
3. Green Manufacturing: Developing more energy-efficient and environmentally friendly control methods to reduce energy consumption and environmental pollution during the production process.

VI. Conclusion