A high-performance, low-cost demodulation system is essential for fiber-optic sensor-based measurement applications. This paper presents a demodulation system for FBG sensors based on a long-period fiber grating (LPG) driven by artificial intelligence techniques. The LPG is applied as an edge filter to convert the spectrum drift of the FBG sensor into transmitted intensity variation, which is subsequently fed to the proposed sensor demodulation network to provide high-precision wavelength interrogation. The sensor demodulation network consists of a generative adversarial network (GAN) for data augmentation and a dense neural network (DNN) for wavelength interrogation, the former addresses the drawback that traditional machine learning models rely on a large-scale dataset for satisfactory performance, while the latter is used to model the relationship between transmitted intensity and wavelength for demodulation. Experiments demonstrate that the proposed system has excellent performance and can achieve wavelength interrogation precision of ±3 pm. In addition, the effectiveness of the GAN is demonstrated. With a wide demodulation range, high performance, and low cost, the system can provide a new platform for fiber-optic sensor-based measurement applications.

The Goos–Hänchen (GH) shift is simulated and experimentally studied when the Bloch surface wave is excited in the forbidden band of a one-dimensional photonic band-gap structure for an excitation beam with different divergence. By changing the beam waist radius, a correspondingly changed Goos–Hänchen shift curve can be observed, where with a larger waist radius and the corresponding less divergence of the excitation beam, a greater GH shift that is closer to the Artmann estimation is obtained. The experimental demonstration of this phenomenon agrees well with the theoretical simulation.

Suspended-core fibers (SCFs) are considered the best candidates for enhancing fiber nonlinearity in mid-infrared applications. Accurate modeling and optimization of its structure is a key part of the SCF structure design process. Due to the drawbacks of traditional numerical simulation methods, such as low speed and large errors, the deep learning-based inverse design of SCFs has become mainstream. However, the advantage of deep learning models over traditional optimization methods relies heavily on large-scale a priori datasets to train the models, a common bottleneck of data-driven methods. This paper presents a comprehensive deep learning model for the efficient inverse design of SCFs. A semi-supervised learning strategy is introduced to alleviate the burden of data acquisition. Taking SCF’s three key optical properties (effective mode area, nonlinear coefficient, and dispersion) as examples, we demonstrate that satisfactory computational results can be obtained based on small-scale training data. The proposed scheme can provide a new and effective platform for data-limited physical computing tasks.

Surface plasmon polaritons (SPPs) have been thoroughly studied in the past decades for not only sensing but also waveguiding applications. Various plasmonic device structures have been explored due to their ability to confine their optical mode to the subwavelength level. However, with the existence of metal, the large ohmic loss limits the propagation distance of the SPP and thus the scalability of such devices. Therefore, different hybrid waveguides have been proposed to overcome this shortcoming. Through fine tuning of the coupling between the SPP and a conventional waveguide mode, a hybrid mode could be excited with decent mode confinement and extended propagation distance. As an effective alternative of SPP, Bloch surface waves have been re-investigated more recently for their unique advantages. As is supported in all-dielectric structures, the optical loss for the Bloch surface wave is much lower, which stands for a much longer propagating distance. Yet, the confinement of the Bloch surface wave due to the reflections and refractions in the multilayer structure is not as tight as that of the SPP. In this work, by integrating a periodic multilayer structure that supports the Bloch surface wave with a metallic nanowire structure, a hybrid Bloch surface wave polariton could be excited. With the proposed hybrid nanowire structure, a hybrid mode is demonstrated with the deep subwavelength mode confinement and a propagation distance of tens of microns.