Use of machine learning in a speckle-based optical fiber sensor for temperature detection

The speckle is a granular pattern observed at the end of a multimode fiber due to the interference of propagating modes. The speckle pattern will change in case of any perturbations at any point in the fiber and thus forms the basis of highly sensitive fiber specklegram sensors. Early researchers developed speckle interferometry and imaging tools to extract information from the speckle. However, general drawbacks in these approaches are difficulty in extracting information from non-linear speckle patterns and associated high computational costs. For this reason, we propose a machine learning-based solution to analyze speckle patterns in gold-coated multimode fibers for temperature sensing. We build a temperature-controlled environment on a 4 cm length of a copper rod using a heater with a proportional-integral-derivative control. We coat around a 10 nm gold layer on a 4 cm stripped region of a 50/125 μm multimode optical fiber using the DC sputtering process. The gold-coated fiber is then attached to the copper rod. We find that a thin layer of gold on the fiber surface enhances the overall temperature sensing sensitivity. We record speckle pattern images through a CCD camera over a range of 25.9-28.9° C with roughly 150 images per temperature setting. Using time-efficient and straightforward machine learning models such as k-nearest neighbors and ridge regression, we achieve temperature sensing accuracy as high as 96% and mean squared error as low as 0.000844° C. We anticipate that the current work can pave the way for efficient sensing applications in photo-acoustics, strain, and pressure measurements.