Nanosecond-latency all-optical fiber sensing with in-sensor computing Article Swipe

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Yu Tao , Yangyang Wan , Zhilin Long , Wenjia Zhang , Jiangbing Du , Zuyuan He ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.15376

Optical fiber sensing plays a crucial role in modern measurement systems and holds significant promise for a wide range of applications. This potential, though, has been fundamentally constrained by the intrinsic latency and power limitations associated with electronic signal processing. Here, we propose an all-optical fiber sensing architecture with in-sensor computing (AOFS-IC) that achieves fully optical-domain sensing signal demodulation at the speed of light. By integrating a scattering medium with an optimized diffractive optical network, AOFS-IC enables linear mapping of physical perturbations to detected intensity, and sensing results can be directly read out without electronic processing. The proposed system maintains high accuracy across various sensing tasks, providing sub-nano strain resolution and 100% torsional angle classification accuracy, as well as multiplexed sensing of multiple physical quantities, and performing multi-degree-of-freedom robot arm monitoring. AOFS-IC eliminates computing hardware requirements while providing <3 ns demodulation delay, which is more than 2 orders of magnitude faster than conventional fiber optic sensing systems. This work demonstrates the potential of next-generation optical sensing systems empowered by all-optical computing, and paves the way for expanded applications of fiber sensing through the integration of fully optical components, ultrafast measurement speed, and low power consumption.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.15376
PDF: https://arxiv.org/pdf/2507.15376
OA Status: green
OpenAlex ID: https://openalex.org/W4416840752

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DOI: https://doi.org/10.48550/arxiv.2507.15376

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Title: Nanosecond-latency all-optical fiber sensing with in-sensor computing

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-21

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Authors: Yu Tao, Yangyang Wan, Zhilin Long, Wenjia Zhang, Jiangbing Du, Zuyuan He

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Landing page: https://arxiv.org/abs/2507.15376

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PDF URL: https://arxiv.org/pdf/2507.15376

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