Machine Learning Enhanced Single Crystal Fiber Fabrication via Laser Heated Pedestal Growth

This study introduces a machine learning (ML)-assisted framework for optimizing Proportional-Integral-Derivative (PID) coefficients, enabling stable, long-length single crystal fiber (SCF) fabrication via the Laser Heated Pedestal Growth (LHPG) technique. Compared with traditional curve-fitting methods, the proposed ML approach significantly enhances computational efficiency and achieves up to 20dB improvement in prediction accuracy, leading to notable improvements in SCF uniformity and achievable fiber length. The fabricated sapphire SCF demonstrates a length exceeding 3 m and an average diameter of $150~\mu $ m with fluctuations below $\pm 3~\mu $ m, validating the effectiveness of the ML model. Additionally, fundamental guidelines for optimizing PID coefficients in LHPG systems are provided. These results offer valuable insights for high-quality SCF fabrication and are expected to facilitate the production of improved fibers suitable for harsh environment sensing applications.