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| Citation: |
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
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Abstract
This work introduces special states for light in multimode fibers featuring strongly enhanced or reduced correlations between output fields in the presence of environmental temperature fluctuations. Using experimentally measured multi-temperature transmission matrix, a set of temperature principal modes that exhibit resilience to disturbances caused by temperature fluctuations can be generated. Reversing this concept also allows the construction of temperature anti-principal modes, with output profiles more susceptible to temperature influences than the unmodulated wavefront. Despite changes in the length of the multimode fiber within the temperature-fluctuating region, the proposed approach remains capable of robustly controlling the temperature response within the fiber. 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. This sensor exhibits outstanding superiority over traditional approaches in terms of resolution and accuracy. These novel states are anticipated to have wide-ranging applications in fiber communication, sensing, imaging, and spectroscopy, and serve as a source of inspiration for the discovery of other novel states.-
Keywords:
- multimode fiber /
- principal mode /
- wavefront shaping /
- optical fiber sensor /
- temperature response
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References
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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
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Figure 1.
Schematics of the experimental setup. OBJ: microscopic objective (OBJ1: 20×, NA (numerical aperture
) = 0.40; OBJ2: 40×, NA= 0.75); CCD: charge-coupled device camera; MMF: multimode fiber; SLM: spatial light modulator; P: polarizer; M: mirror; BS: beam splitter; L: lens. -
Figure 2.
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).
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Figure 3.
Recorded intensity profiles of (a) temperature principal mode (top row), (b) temperature anti-principal mode (middle row) and (c) unmodulated wavefront (bottom row) at the distal end of the fiber.
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Figure 4.
Normalized bandwidth of the temperature principal mode and the temperature anti-principal mode for different fiber length.
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Figure 5.
Overview of learning empowered fiber specklegram temperature sensing schemes based on temperature anti-principal mode sensitization.
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Figure 6.
The contribution rate and cumulative contribution rate of principal components under different dimensions.
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Figure 7.
The trained deep learning model is used to predict learned 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.
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Figure 8.
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.
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Figure 9.
The performance of fiber specklegram temperature sensors based on temperature principal modes. (a, b) Prediction errors and error distribution histograms for learned configurations. (c, d) Prediction errors and error distribution histograms for unlearned configurations.
