The introduction of "metamaterials" has had a profound impact on several fields, including electromagnetics. Designing a metamaterial's structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for ondemand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE), with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes.
Based on the phase transition of vanadium dioxide(VO2), an ultra-broadband tunable terahertz metamaterial absorber is proposed. The absorber consists of bilayer VO2 square ring arrays with different sizes, which are completely wrapped in Topas and placed on gold substrate. The simulation results show that the absorption greater than 90% has frequencies ranging from 1.63 THz to 12.39 THz, which provides an absorption frequency bandwidth of 10.76 THz, and a relative bandwidth of 153.5%. By changing the electrical conductivity of VO2, the absorption intensity can be dynamically adjusted between 4.4% and 99.9%. The physical mechanism of complete absorption is elucidated by the impedance matching theory and field distribution. The proposed absorber has demonstrated its properties of polarization insensitivity and wide-angle absorption, and therefore has a variety of application prospects in the terahertz range, such as stealth, modulation, and sensing.
This paper proposes a Fabry–Perot pressure sensor based on AB epoxy adhesive with ultra-high sensitivity under low pressure. Fabry–Perot interference, located between single-mode fiber (SMF) and hollow-core fiber (HCF), is an ultra-thin AB epoxy film formed by capillary action. Then the thick HCF was used to fix the HCF and SMF at both ends with AB epoxy adhesive. Experimental results show that when the thickness of AB epoxy film is 8.74 μm, and the cavity length is 30 μm, the sensor has the highest sensitivity. The sensitivity is 257.79 nm/MPa within the pressure range of 0–70 kPa. It also investigated the influence of the curing time of AB epoxy on the interference spectrum. Experiments showed that the interference spectrum peak is blue-shifted with the increase of curing time. Our study also demonstrated the humidity stability of this pressure sensor. These characteristics mean that our sensor has potential applications in the biomedical field and ocean exploration.
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