Next generation photonic waveguide sensors enabled by machine learning

由机器学习支持的下一代光子波导传感器

• 批准号: FT200100154
• 负责人: Dr Stephen Warren-Smith
• 金额: $ 55.26万
• 依托单位: University of South Australia
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT200100154
• 关键词: Next; generation; photonic; waveguide; sensors

This project aims to establish the next frontier in photonic waveguide sensing, by using machine learning to shift the complexity out of conventional photonic-waveguide/optical-fibre sensors and into smart detection algorithms. The complexity and instability of multimode photonic waveguides, traditionally a hinderance to sensing, will be advantageously employed to train deep learning models for sensing. Expected outcomes include the creation of intelligent photonic sensors that can, in principle, measure any environmental parameter using any optical waveguide material. It will create new critically needed measurement capabilities for challenging harsh environments, such as extreme temperature and in-vivo biochemical sensing.

该项目旨在通过使用机器学习将传统光子波导/光纤传感器的复杂性转移到智能检测算法中，从而建立光子波导传感的下一个前沿。多模光子波导的复杂性和不稳定性，传统上是传感的障碍，将有利于训练用于传感的深度学习模型。预期的结果包括创造智能光子传感器，原则上可以使用任何光波导材料测量任何环境参数。它将为具有挑战性的恶劣环境（如极端温度和体内生化传感）创造新的急需的测量能力。