Particle Swarm Optimization-Long Short-Term Memory-Based Dynamic Prediction Model of Single-Crystal Furnace Temperature and Heating Power

Precise temperature and heating power control are crucial for crystal quality and production efficiency in the Czochralski single-crystal growth process. Existing sensor technologies can only monitor these parameters in real time, lacking the ability to predict future trends, which limits the ability to implement preventive control before issues arise. To address this, a temperature and heating power prediction model based on Long Short-Term Memory (LSTM) is proposed and developed using extensive production data. Spearman's rank correlation coefficient is applied to identify the key parameters related to temperature and heating power. Hyperparameter optimization uses Particle Swarm Optimization (PSO) to improve prediction accuracy. The performance of the PSO-LSTM model is compared with two other widely used prediction models, demonstrating its superior predictive capability. The results show that the PSO-LSTM model achieves highly accurate temperature and heating power predictions in the crystal growth process, with a Mean Absolute Error (MAE) of 0.0295 for temperature and 0.0392 for heating power, further validating its effectiveness for real-time predictive control.