Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing

Kanon Toda; Kazuya Kishizawa; Yuma Toyoda; Kohei Noda; Heeyoung Lee; Kentaro Nakamura; Koichi Ichige; Yosuke Mizuno

doi:10.35848/1882-0786/ac749e

Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing

Kanon Toda, Kazuya Kishizawa, Yuma Toyoda, Kohei Noda, Heeyoung Lee, Kentaro Nakamura, Koichi Ichige, Yosuke Mizuno

Research output: Contribution to journal › Article › peer-review

Abstract

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 1/40.3 °C.

Original language	English
Article number	072002
Journal	Applied Physics Express
Volume	15
Issue number	7
DOIs	https://doi.org/10.35848/1882-0786/ac749e
Publication status	Published - 2022 Jul

Keywords

machine learning
modal interference
optical fiber sensing
polymer optical fibers
temperature effect

ASJC Scopus subject areas

Engineering(all)
Physics and Astronomy(all)

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10.35848/1882-0786/ac749e

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title = "Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing",

abstract = "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 1/40.3 °C.",

keywords = "machine learning, modal interference, optical fiber sensing, polymer optical fibers, temperature effect",

author = "Kanon Toda and Kazuya Kishizawa and Yuma Toyoda and Kohei Noda and Heeyoung Lee and Kentaro Nakamura and Koichi Ichige and Yosuke Mizuno",

note = "Funding Information: The authors are indebted to Asahi Kasei Corporation for providing the multi-core POF samples. This work was partly supported by the JSPS KAKENHI (20K22417, 20J22160, 21H04555, 22K14272) and research grants from the Murata Science Foundation, the Telecommunications Advancement Foundation, the Takahashi Industrial and Economic Research Foundation, the Yazaki Memorial Foundation for Science and Technology, and Konica Minolta Science and Technology Foundation. Publisher Copyright: {\textcopyright} 2022 The Japan Society of Applied Physics.",

year = "2022",

month = jul,

doi = "10.35848/1882-0786/ac749e",

language = "English",

volume = "15",

journal = "Applied Physics Express",

issn = "1882-0778",

publisher = "Japan Society of Applied Physics",

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T1 - Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing

AU - Toda, Kanon

AU - Kishizawa, Kazuya

AU - Toyoda, Yuma

AU - Noda, Kohei

AU - Lee, Heeyoung

AU - Nakamura, Kentaro

AU - Ichige, Koichi

AU - Mizuno, Yosuke

N1 - Funding Information: The authors are indebted to Asahi Kasei Corporation for providing the multi-core POF samples. This work was partly supported by the JSPS KAKENHI (20K22417, 20J22160, 21H04555, 22K14272) and research grants from the Murata Science Foundation, the Telecommunications Advancement Foundation, the Takahashi Industrial and Economic Research Foundation, the Yazaki Memorial Foundation for Science and Technology, and Konica Minolta Science and Technology Foundation. Publisher Copyright: © 2022 The Japan Society of Applied Physics.

PY - 2022/7

Y1 - 2022/7

N2 - 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 1/40.3 °C.

AB - 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 1/40.3 °C.

KW - machine learning

KW - modal interference

KW - optical fiber sensing

KW - polymer optical fibers

KW - temperature effect

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JO - Applied Physics Express

JF - Applied Physics Express

IS - 7

M1 - 072002

ER -

Characterization of modal interference in multi-core polymer optical fibers and its application to temperature sensing

Abstract

Keywords

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