Xibao Gao;Sichao Wang;Bo Liu;Xiao Liu;Shuai Qu;Chen Guan;Qinglan Wang
{"title":"Machine Learning Enhanced Single Crystal Fiber Fabrication via Laser Heated Pedestal Growth","authors":"Xibao Gao;Sichao Wang;Bo Liu;Xiao Liu;Shuai Qu;Chen Guan;Qinglan Wang","doi":"10.1109/LPT.2025.3580586","DOIUrl":null,"url":null,"abstract":"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 <inline-formula> <tex-math>$150~\\mu $ </tex-math></inline-formula>m with fluctuations below <inline-formula> <tex-math>$\\pm 3~\\mu $ </tex-math></inline-formula>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.","PeriodicalId":13065,"journal":{"name":"IEEE Photonics Technology Letters","volume":"37 18","pages":"1017-1020"},"PeriodicalIF":2.3000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Technology Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11037741/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
IEEE Photonics Technology Letters addresses all aspects of the IEEE Photonics Society Constitutional Field of Interest with emphasis on photonic/lightwave components and applications, laser physics and systems and laser/electro-optics technology. Examples of subject areas for the above areas of concentration are integrated optic and optoelectronic devices, high-power laser arrays (e.g. diode, CO2), free electron lasers, solid, state lasers, laser materials'' interactions and femtosecond laser techniques. The letters journal publishes engineering, applied physics and physics oriented papers. Emphasis is on rapid publication of timely manuscripts. A goal is to provide a focal point of quality engineering-oriented papers in the electro-optics field not found in other rapid-publication journals.