Terahertz Metamaterials for Biosensing Applications: A Review.

In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.

An Active Electromagnetically Induced Transparency (EIT) Metamaterial Based on Conductive Coupling.

In this paper, we demonstrate an active metamaterial manifesting electromagnetically induced transparency (EIT) effect in the microwave regime. The metamaterial unit cell consists of a double-cross structure, between which a varactor diode is integrated. The capacitance of the diode is controlled by a reversed electrical bias voltage supplied through two connected strip lines. The diode behaves as a radiative resonant mode and the strip lines as a non-radiative resonant mode. The two modes destructively interference with each other through conductive coupling, which leads to a transmission peak in EIT effect. Through electrical control of the diode capacitance, the transmission peak frequency is shifted from 7.4 GHz to 8.7 GHz, and the peak-to-dip ratio is tuned from 1.02 to 1.66, demonstrating a significant tunability.

Numerical and Experimental Investigation on the Optical Manipulation from an Axicon Lensed Fiber.

Here we numerically and experimentally studied the optical trapping on a microsphere from an axicon lensed fiber (ALF). The optical force from the fiber with different tapered lengths and by incident light at different wavelengths is calculated. Numerically, the microsphere can be trapped by the fiber with tapered outline y=±x/0.5 and y=±x at a short incident wavelength of 900 nm. While for the fiber with tapered outline y=±x/2, the microsphere can be trapped by the light with longer wavelength of 1100 nm, 1300 nm, or 1500 nm. The optical trapping to a polystyrene microsphere is experimentally demonstrated in a microfluidic channel and the corresponding optical force is derived according to the fluid flow speed. This study can provide a guidance for future tapered fibre design for optical trapping to microspheres.

Junctionless Poly-GeSn Ferroelectric Thin-Film Transistors with Improved Reliability by Interface Engineering for Neuromorphic Computing.

Ferroelectric HfZrOx (Fe-HZO) with a larger remnant polarization (Pr) is achieved by using a poly-GeSn film as a channel material as compared with a poly-Ge film because of the lower thermal expansion that induces higher stress. Then two-stage interface engineering of junctionless poly-GeSn (Sn of ∼5.1%) ferroelectric thin-film transistors (Fe-TFTs) based on HZO was employed to improve the reliability characteristics. With stage I of NH3 plasma treatment on poly-GeSn and subsequent stage II of Ta2O5 interfacial layer growth, the interfacial quality between Fe-HZO and the poly-GeSn channel is greatly improved, which in turn enhances the reliability performance in terms of negligible Pr degradation up to 106 cycles (±2.7 MV/1 ms) and 96% Pr after a 10 year retention at 85 °C. Furthermore, to emulate the synapse plasticity of the human brain for neuromorphic computing, besides manifesting the capability of short-term plasticity, the devices also exhibit long-term plasticity with the characteristics of analog conductance (G) states of 80 levels (>6 bit), small linearity for potentiation and depression of -0.83 and 0.62, high symmetry, and moderate Gmax/Gmin of 9.6. By employing deep neural network, the neuromorphic system with poly-GeSn Fe-TFT synaptic devices achieves 91.4% pattern recognition accuracy. In addition, the learning algorithm of spike-timing-dependent plasticity based on spiking neural network is demonstrated as well. The results are promising for on-chip training, making it possible to implement neuromorphic computing by monolithic 3D ICs based on poly-GeSn Fe-TFTs.