Terahertz (THz) topological photonic structures are promising for last-centimeter communication in intra/interchip communication systems because they support bit-error-free THz signal transmission with topological robustness. Active and dynamically tunable THz topological photonic components have not yet been experimentally realized. Here, we experimentally demonstrate a THz topological switch (270–290 GHz) based on a valley Hall photonic crystal structured high-resistivity silicon substrate, in which the THz waves can be dynamically turned on/off by an external 447 nm continuous-wave laser. Our device exhibited an on/off ratio of 19 dB under a pumping light intensity of 240 mW / mm 2 . The 3 dB switching bandwidth was ∼ 60 kHz .

Influencing mechanisms of multi-scale pore-fracture responses of coals on their macro/micromechanical behaviors under ScCO2 injection

We employed a metallic wire grating loaded with graphene and operating in total internal reflection (TIR) geometry to realize deep and broadband THz modulation. The non-resonant field enhancement effect of the evanescent wave in TIR geometry and in the subwavelength wire grating was combined to demonstrate a $\sim 77\%$ modulation depth (MD) in the frequency range of 0.2–1.4 THz. This MD, achieved electrically with a ${\mathrm{SiO}}_2/\mathrm{Si}$ gated graphene device, was 4.5 times higher than that of the device without a metal grating in transmission geometry. By optimizing the parameters of the metallic wire grating, the required sheet conductivity of graphene for deep modulation was lowered to 0.87 mS. This work has potential applications in THz communication and real-time THz imaging.

Terahertz (THz) technology has seen significant advancements in the past decades, encompassing both fundamental scientific research, such as THz quantum optics, and highly applied areas like sixth-generation communications, medical imaging, and biosensing. However, the progress of on-chip THz integrated waveguides still lags behind that of THz sources and detectors. This is attributed to issues such as ohmic losses in microstrip lines, coplanar and hollow waveguides, bulky footprints, and reflection and scattering losses occurring at sharp bends or defects in conventional dielectric waveguides. Inspired by the quantum Hall effects and topological insulators in condensed matter systems, recent discoveries of topological phases of light have led to the development of topological waveguides. These waveguides exhibit remarkable phenomena, such as robust unidirectional propagation and reflectionless behavior against impurities or defects. As a result, they hold tremendous promise for THz on-chip applications. While THz photonic topological insulators (PTIs), including wave division, multiport couplers, and resonant cavities, have been demonstrated to cover a wavelength range of 800–2500 nm, research on tunable THz PTIs remains limited. In this perspective, we briefly reviewed a few examples of tunable PTIs, primarily concentrated in the infrared range. Furthermore, we proposed how these designs could benefit the development of THz on-chip PTIs. We explore the potential methods for achieving tunable THz PTIs through optical, electrical, and thermal means. Additionally, we present a design of THz PTIs for potential on-chip sensing applications. To support our speculation, several simulations were performed, providing valuable insights for future THz on-chip PTI designs.

Exception handling is an important built-in feature of many modern programming languages such as Java. It allows developers to deal with abnormal or unexpected conditions that may occur at runtime in advance by using try-catch blocks. Missing or improper implementation of exception handling can cause catastrophic consequences such as system crash. However, previous studies reveal that developers are unwilling or feel it hard to adopt exception handling mechanism, and tend to ignore it until a system failure forces them to do so. To help developers with exception handling, existing work produces recommendations such as code examples and exception types, which still requires developers to localize the try blocks and modify the catch block code to fit the context. In this paper, we propose a novel neural approach to automated exception handling, which can predict locations of try blocks and automatically generate the complete catch blocks. We collect a large number of Java methods from GitHub and conduct experiments to evaluate our approach. The evaluation results, including quantitative measurement and human evaluation, show that our approach is highly effective and outperforms all baselines. Our work makes one step further towards automated exception handling.