Tunable Mode Converter Device Based on Photonic Crystal Fiber with a Thermo-Responsive Liquid Crystal Core

Numerical Modeling of a Rectangular Hollow-Core Waveguide for the Detection of Fuel Adulteration in Terahertz Region

A petrol adulteration sensor based on a rectangular shaped hollow-core photonic crystal fiber is proposed and numerically analyzed in the terahertz regime. The performance of the proposed sensor was evaluated when it is employed to characterize different kerosene mixtures. In this research, the adulterated fuel sample is filled in the rectangular hollow channel and the electromagnetic signal of the terahertz band is also driven through the same channel. The received signal after the interaction of fuel with the terahertz signal will advise the refractive index of the fuel oil inside the core, which will also bear the information of how much extrinsic component is present in the fuel. The finite element method based simulation shows that the proposed sensor can reach a high relative sensitivity of 89% and presents low confinement losses at 2.8 THz. The reported sensing structure is easily realizable with the conventional manufacturing techniques. Consequently, this proposed fiber may be treated as an essential part of real-life applications of petrol adulteration measurements.

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy

The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure-performance relationship in an interpretable way, and solve the one-to-many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end-to-end training. On a data-driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.