Multimode temperature sensing fiber

Multimode fibers (MMFs) can serve as high-precision temperature sensors by exploiting speckle patterns, multimode interference, and temperature-sensitive transmission modes.

Working Principle

Multimode fibers guide light through multiple propagation paths, creating speckle patterns due to interference among modes. These patterns are highly sensitive to environmental changes, including temperature fluctuations. As temperature changes, the refractive index and physical length of the fiber vary, altering the speckle pattern. By analyzing these changes, the temperature along the fiber can be inferred without direct contact with the target object .

Sensing Techniques

1. Deep Learning-Based Speckle Analysis Recent approaches use Convolutional Neural Networks (CNNs) to interpret speckle patterns generated in MMFs. The CNN can simultaneously predict the temperature and the location of heating points along the fiber. Experiments have demonstrated temperature prediction accuracy within ±1 °C and 100% location prediction accuracy, offering a simpler and more efficient alternative to traditional fiber-optic sensing methods that rely on complex physical models .
2. Multimode Interference (MMI) Structures MMFs can be integrated into single-mode–multimode–single-mode (SMS) fiber structures, where a short MMF segment is sandwiched between two single-mode fibers. The interference pattern depends on the MMF's core diameter, numerical aperture (NA), and length, which influence temperature sensitivity. Larger core diameters increase temperature sensitivity, while longer MMF sections reduce it. These sensors can achieve strain-insensitive temperature measurements, making them robust in environments with mechanical stress .
3. Temperature Principal and Anti-Principal Modes By experimentally measuring the multi-temperature transmission matrix, special states of light can be generated that are either resilient (temperature principal modes) or highly sensitive (temperature anti-principal modes) to temperature fluctuations. Sensors based on anti-principal modes can achieve enhanced resolution and accuracy, providing robust control over the fiber's temperature response even under varying environmental conditions .

Advantages

• Non-contact measurement suitable for hazardous or hard-to-reach environments.
• High spatial resolution and distributed sensing along the fiber.
• Strain-insensitive designs allow accurate temperature readings even under mechanical stress.
• Integration with deep learning simplifies data interpretation and bypasses complex physical modeling.

Applications

• Industrial and structural monitoring, including dams, pipelines, and reactors.
• Environmental sensing in hazardous or remote locations.
• Fiber-optic communication systems where temperature-induced mode changes need to be monitored.
• Advanced imaging and spectroscopy applications leveraging temperature-sensitive multimode states . In summary, multimode temperature sensing fibers combine the inherent sensitivity of MMFs with modern techniques like deep learning and MMI structures to provide highly accurate, distributed, and robust temperature measurements suitable for a wide range of scientific and industrial applications.