For a long time, optical interferometric sensors have been widely used in rapid and real-time detection in fields such as physics, chemistry, biology, and medicine owing to their unparalleled high sensitivity and high-quality factor. However, their limited free spectral range makes it difficult to achieve unambiguous monitoring over a broad measurement range while maintaining high sensitivity, which has significantly constrained their wider application in precision measurement scenarios. In recent years, researchers at home and abroad have attempted to integrate fiber Bragg gratings for spectral marking and track the changes of FSR with the measured quantity, etc., to alleviate these limitations. However, this often leads to complex manual calculations and other limitations that compromise sensitivity.
In a new paper published in Light: Science & Applications , a team of scientists, led by Professor Hailiang Chen from Shuguang Li 's group at Yanshan University, in collaboration with the team of Associate Professor Sigang Yang from Tsinghua University, innovatively proposed a full-spectrum recognition technology for optical interference spectra based on long short-term memory (LSTM). This method not only resolves the long-standing trade-off between high sensitivity and wide measurement range but successfully achieves both simultaneously.
To address this challenge, the research team integrated LSTM neural networks with fiber-optic interferometric sensing to overcome the limitation imposed by the FSR on the measurement range. Unlike traditional methods confined to a single spectral cycle, this technology leverages the gating mechanisms and sequence learning capabilities of LSTM to model long-term dependencies in complex interference spectra. It enables accurate identification even with spectral overlap, tripling the detection range while maintaining high sensitivity. This achieves a synergistic enhancement in both wide dynamic range and high precision.
Another key innovation is an efficient down-sampling strategy. By optimizing spectral sampling points, it significantly reduces data acquisition and processing loads without compromising measurement accuracy. This greatly improves system response speed, laying the foundation for practical use in dynamic, rapid-detection scenarios.
This technology uses algorithms to push hardware beyond its conventional physical limits, advancing optical sensing toward more intelligent and practical applications. By establishing an end-to-end system that maps interference spectra directly to target quantities, it circumvents the limitations of manual feature extraction. This provides a novel technical pathway for intelligent monitoring in biochemical and medical sensing within complex environments.
Light Science & Applications
LSTM-assisted optical fiber interferometric sensing: breaking the limitation of free spectral range