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Optical magnetic field sensors have gained prominence due to their compact size, high sensitivity, and broad dynamic range. Photonic crystal fiber (PCF) sensors have emerged as a significant role in optical sensing. Traditional numerical design methods of PCF such as finite element method (FEM) are computationally demanding and often restrict the design space due to iterative trial-and-error processes. In this work, a bidirectional design approach using a deep learning model based on deep neural networks (DNNs) and augmented gray wolf optimizer (AGWO) is proposed for surface plasmon resonance (SPR)-based PCF magnetic field sensor. This bidirectional design method contains forward modeling and inverse design. The forward modeling predicts the optical responses of sensor structures accurately in 55.99 ms with an ${R}$ -squared value of 0.9942. The inverse design approach, with a tandem network configuration for addressing the nonuniqueness challenge in inverse design, identifies the sensor design aligning with desired optical responses in less than 0.5 s. In addition, AGWO is utilized to further enhance the accuracy and convergence ability of DNN. The key results indicate that our proposed approach reduces computational effort and enhances design effectiveness in PCF sensor design, which can also offer an alternative method for the design of various optical devices.

Er3+/Yb3+ co-doped tellurite glass (TeO2-ZnO-WO3-Li $_{{2}}\text{O}$ -BaF2-Nb2O3-Er2O3 + Yb2O3, TZWLBN) was investigated in the range of 253–443 K, and by varying the molar ratio of Yb3+ ions, the ion concentration of the strongest luminescent emission was found. Based on it, by combining mold casting and fiber drawing techniques, a no-core tellurite fiber (NTF) was fabricated, and then connected with two multimode fibers (MMFs) to form a temperature sensor. Under a pumping power of 2.20 mW, a comprehensive evaluation was conducted to investigate the temperature sensing characteristics of this sensor, employing the fluorescence intensity ratio (FIR) technique. The repeatability and stability of the experimental protocol were established through numerous heating-cooling cycles and constant temperature test. The highest possible absolute and relative sensitivities were determined as 0.00264 and 0.0114 $\text{K}^{-{1}}$ . The temperature sensor exhibited a maximum temperature error of 0.88 K when employed for monitoring the temperature of the metal–oxide–semiconductor field effect transistor (MOSFET) in complex circuits, thereby demonstrating its practical applicability in thermal detection scenarios.

In this article, we propose a magnetic field sensor based on a multimode-D-type-multimode (MMF-D-type-MMF) fiber structure employing surface plasmon resonance (SPR). The sensor’s surface is coated with a layer composed of indium tin oxide (ITO), silver (Ag), and a 2-D material known as Bi2O2Se. Bi2O2Se exhibits varying magnetic permeability within a specific magnetic field strength range, a characteristic confirmed in practical tests. To enhance the sensor’s sensitivity to magnetic fields, we implemented several optimization measures. These measures encompassed adjustments to: 1) the concentration of the magnetic fluid (MF); 2) the thickness of the Bi2O2Se layer; and 3) the selection of fiber type. We conducted simulations and experimental tests to refine these parameters. Consequently, the proposed D-type sensor demonstrates a sensitivity of 0.4325 nm/GS within the 40–150 GS magnetic field strength range, representing a significant 75.31% sensitivity improvement achieved through the aforementioned enhancement measures. When compared to other fiber-optic magnetic field sensors, our proposed sensor stands out for its exceptional sensitivity, straightforward design, robust repeatability, strong anti-interference capabilities, and high stability. As a result, it holds significant potential for a wide range of applications in aerospace, aviation, environmental monitoring, and related fields. The sensor has a high sensitivity in the range of 40–150 GS, which can monitor the magnetic field strength in life in real time and determine whether it meets the human body safety magnetic field standard.

In this article, Er $^{{3}+}$ /Yb $^{{3}+}$ co- doped TeO2-Li $_{{2}}\text{O}$ -ZnF2-WO3-Nb2O5 (TZWLN) glasses were prepared using the melt annealing methods, and the rare Earth (RE) ion concentrations were optimized to achieve the strongest green up-conversion (UC) luminescence. Based on it, a temperature sensor drawing on the fluorescence intensity ratio (FIR) technique was developed via a dual solidification process for real-time temperature monitoring of automotive 3-D LiDAR (A3DL). The sensing performance of the sensor was evaluated in the temperature range of 252 to 453 K. The maximum absolute sensitivity ( ${S}_{a}{)}$ was $3.98\times 10^{-{3}}\,\,\text{K}^{-{1}}$ at 453 K, and the maximum relative sensitivity ( ${S}_{r}{)}$ was 0.0146 $\text{K}^{-{1}}$ at 252 K. Global rapid thermal cycling repetition experiments and local temperature jump stability experiments were conducted, and temperature information was demodulated using the FIR mathematical model. In the repetition experiments, the maximum residual sum of squares (RSSs) was $9.98\times 10^{-{3}}$ , and in the temperature jump experiments, the standard deviation of demodulated temperatures was 0.064 and 0.063. It is worth noting that the maximum measurement error of the sensor in the real-time temperature detection of the onboard LiDAR was only 0.77 K. To the best of our knowledge, this was the first report on using fluorescent temperature sensors for real-time temperature detection of A3DL. The proposed sensor has the advantages of excellent stability and repeatability, resistance to electromagnetic interference and corrosion, as well as the capacity to avoid cross-sensitivity. It offers an effective and reliable measurement scheme for real-time temperature detection in A3DL.