マルチコアプラスチック光ファイバ中のモード間干渉の特性を解明し、機械学習を用いて高精度な温度計測に応用できることを実証した横浜国大・東工大との共同研究の成果が Applied Physics Express に掲載されました。
K. Toda, K. Kishizawa, Y. Toyoda, K. Noda, H. Lee, K. Nakamura, K. Ichige, and Y. Mizuno, “Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing,” Appl. Phys. Express, vol. 15, no. 7, 072002 (2022).
Various types of fiber-optic temperature sensors have been developed on the basis of modal interference in multimode fibers, which include not only glass fibers but also polymer optical fibers (POFs). Herein, we investigate the spectral patterns of the modal interference in multi-core POFs (originally developed for imaging) and observe their unique temperature dependencies with no clear frequency shift or critical wavelength. We then show that, by machine learning, the modal interference in the multi-core POFs can be potentially used for highly accurate temperature sensing with an error of ~0.3oC.