Tailoring Temperature Response for A Multimode Fiber
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Tailoring Temperature Response for A Multimode Fiber

11/10/2024 Compuscript Ltd

A new publication from Opto-Electronic Sciences; DOI 10.29026/oes.2025.240004 , discusses tailoring temperature response for a multimode fiber.

A multimode fiber with a larger core diameter can transmit multiple modes of light waves. Due to its advantages of low optical loss, resistance to electromagnetic interference, compact size, and good stability, it is widely used in fields such as imaging, communication, spectroscopy, and high-power lasers.

However, modal dispersion, the inevitable random mixing of modes with different propagation constants, inherent defects of the fiber, and disorder caused by external disturbances all lead to the distortion of light signals transmitted through multimode fibers. When coherent light is coupled into a multimode fiber, it produces a seemingly chaotic pattern with bright and dark spots at the output end, known as a speckle pattern. Understanding and controlling this distortion in multimode fibers remains a significant challenge in applications such as fiber-optic communication, endoscopic imaging, and micro-manipulation.

Center for Complex Optical Fields and Meta-Optical Structures (COSMOS) at the University of Shanghai for Science and Technology has tackled the issue of multimode fiber response to environmental temperature fluctuations. They constructed a generalized Wigner-Smith operator based on the multimode fiber's multi-temperature transmission matrix. Using wavefront shaping technology, they experimentally generated a temperature principal mode that exhibits significant resilience to temperature-induced distortion. Additionally, this approach was employed to create a temperature anti-principal mode with an extremely narrow temperature bandwidth. To illustrate the practicality of the proposed special state, a learning-empowered fiber specklegram temperature sensor based on temperature anti-principal mode sensitization is proposed.

The researchers began by measuring the transmission matrix of the multimode fiber at various environmental temperatures. They then constructed a generalized Wigner-Smith operator, with its eigenstates representing the wavefront of the temperature principal mode. By designing a loss function, they were able to generate the temperature anti-principal mode based on the temperature principal mode. As illustrated in Fig. 1, compared to an unmodulated wavefront, the temperature principal mode can increase the temperature bandwidth by approximately 40%, while the temperature anti-principal mode can reduce the temperature bandwidth by about 30%.

By leveraging the temperature sensitivity of the temperature anti-principal mode, its application in optimizing the performance of learning empowered fiber specklegram temperature sensors is feasible, as illustrated in Fig. 2. The process begins with calibrating the temperature anti-principal mode and then collecting its output fields at various environmental temperatures to create a dataset. A regression-based deep learning model is employed to learn the mapping relationship between the speckle patterns and temperature fluctuations. Once trained, the model can directly predict the corresponding environmental temperature based solely on the speckle patterns collected at the fiber's output end.

Experiment validation of the effectiveness of the sensitivity enhancement scheme is presented in Fig. 3. The average measurement error of the sensor based on the unmodulated wavefront is 0.84°C, with a prediction error range of ±2°C. In contrast, the sensor based on the temperature anti-principal mode exhibits an average measurement error of 0.12°C, with a prediction error range of ±0.4°C, significantly improving sensor performance.

Keywords: multimode fiber / principal mode / wavefront shaping / optical fiber sensor / temperature response

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The center of complex optical fields and meta-optical structures (COSMOS) was established in 2018 and is led by Professor Zhan Qiwen. The research areas include light field manipulation and its interaction with micro-nano structures, nanophotonics, biophotonics, super-resolution imaging, and nanostructure characterization. To date, the team has published over 100 papers and applied for more than 10 invention patents, maintaining good cooperative relationships with several well-known research groups in the field both domestically and internationally. The research achievements published in the journal Nature Photonics, "Spatiotemporal Optical Vortices and Photonic Transverse Orbital Angular Momentum" and "Optical Vortices," were respectively selected as the Top Ten Progress in Optics in China for 2020 and 2022.
Website: https://cosmos.usst.edu.cn/
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Opto-Electronic Science (OES) is a peer-reviewed, open access, interdisciplinary and international journal published by The Institute of Optics and Electronics, Chinese Academy of Sciences as a sister journal of Opto-Electronic Advances (OEA, IF=15.3). OES is dedicated to providing a professional platform to promote academic exchange and accelerate innovation. OES publishes articles, reviews, and letters of the fundamental breakthroughs in basic science of optics and optoelectronics.
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More information: https://www.oejournal.org/oes
Editorial Board: https://www.oejournal.org/oes/editorialboard/list
OES is available on OE journals (https://www.oejournal.org/oes/archive)
Submission of OES may be made using ScholarOne (https://mc03.manuscriptcentral.com/oes)
CN 51-1800/O4
ISSN 2097-0382
Contact Us: oes@ioe.ac.cn
Twitter: @OptoElectronAdv (https://twitter.com/OptoElectronAdv?lang=en)
WeChat: OE_Journal
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Gao H, Hu HF, Zhan QW. Tailoring temperature response for a multimode fiber. Opto-Electron Sci 4, 240004 (2025). doi: 10.29026/oes.2025.240004
Gao H, Hu HF, Zhan QW. Tailoring temperature response for a multimode fiber. Opto-Electron Sci 4, 240004 (2025). doi: 10.29026/oes.2025.240004 
Attached files
  • Fig. 1. Calculated correlation function for output signals of the unmodulated wavefront (blue solid line), the temperature principal mode (red solid line) and the temperature anti-principal mode (green solid line). Fig. 2. Overview of learning empowered fiber specklegram temperature sensing schemes based on temperature anti-principal mode sensitization.
  • Fig. 3. The trained deep learning model is used to predict unlearned configurations. (a)-(b) Prediction error and error distribution histogram of a fiber specklegram temperature sensor with unmodulated wavefront. (c)-(d) The prediction error and error distribution histogram of the fiber specklegram temperature sensor sensitized by the temperature anti-principal mode.
  • Fig. 2. Overview of learning empowered fiber specklegram temperature sensing schemes based on temperature anti-principal mode sensitization
11/10/2024 Compuscript Ltd
Regions: Europe, Ireland, Asia, China
Keywords: Applied science, Technology

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