4.7 Article

In-Fiber Hybrid Cladding Waveguide by Femtosecond Inscription for Two-Dimensional Vector Bend Sensing

期刊

JOURNAL OF LIGHTWAVE TECHNOLOGY
卷 39, 期 7, 页码 2194-2204

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2020.3046272

关键词

Periodic structures; Optical fiber sensors; Optical waveguides; Optical fiber couplers; Sensitivity; Robot sensing systems; Fiber gratings; Bend sensing; cladding waveguide; lab-on-fiber

资金

  1. National Key R&D Program of China [2018YFE0117400]
  2. National Natural Science Foundation of China (NSFC) [61775074]

向作者/读者索取更多资源

The 2D vector bending fiber sensor demonstrated in this study is based on a femtosecond laser inscribed hybrid cladding waveguide structure, offering high sensitivity, a relatively large curvature range, and 2D orientation identification. This sensor has the potential to be applied in public infrastructure health monitoring and wearable artificial devices.
A 2D vector bending fiber sensor is demonstrated based on a femtosecond (fs) laser inscribed hybrid cladding waveguide (CWG) structure, which is mainly composed of two eccentric cladding waveguides distributed oppositely in a single mode fiber (SMF). The structure can enable light in the central core to efficiently couple into different CWGs under respective resonant conditions, thereby providing two separate local dips in response to the two CWGs in the transmission spectrum. When the bending is applied, the two CWGs will suffer asymmetric effective refractive index (RI) variation, resulting in that the wavelength shift for each dip differs significantly. The curvature sensitivity can be enhanced by tracking the wavelength interval between the two dips, reaching up to -14.25 nm/m(-1) and +14.80 nm/m(-1) at the 0 degrees and 180 degrees orientations ranging from 0 m(-1) to 8 m(-1), respectively. Featured with comparatively high sensitivity, relatively large curvature range, 2D orientation identification, compact size, and reduced temperature crosstalk, the proposed structure offers the opportunity to be applied from public infrastructure health monitoring to wearable artificial devices.

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