A Triangle Hybrid Plasmonic Waveguide with Long Propagation Length for Ultradeep Subwavelength Confinement

Facing the problems of ohmic loss and short propagation length, the application of plasmonic waveguides is limited. Here, a triangle hybrid plasmonic waveguide is introduced, where a cylinder silicon waveguide is separated from the triangle prism silver waveguide by a nanoscale silica gap. The process of constant optimization of waveguide structure is completed and simulation results indicate that the propagation length could reach a length of 510 μm, and the normalized mode area could reach 0.03 along with a high figure of merit 3150. This implies that longer propagation length could be simultaneously achieved along with relatively ultra-deep subwavelength mode confinement due to the hybridization between metallic plasmon polarization mode and silicon waveguide mode, compared with previous study. By an analysis of fabrication errors, it is confirmed that this waveguide is fairly stable over a wide error range. Additionally, the excellent performance of this is further proved by the comparison with other hybrid plasmonic waveguides. Our work is significant to manipulate light waves at sub-wavelength dimensions and enlarge the application fields, such as light detection and photoelectric sensors, which also benefit the improvement of the integration of optical devices.

Theoretical Investigation of an Air-Slot Mode-Size Matcher between Dielectric and MDM Plasmonic Waveguides

Hybrid integration of dielectric and plasmonic waveguides is necessary to reduce the propagation losses due to the metallic interactions and support of nanofabrication of plasmonic devices that deal with large data transfer. In this paper, we propose a direct yet efficient, very short air-slot coupler (ASC) of a length of 36 nm to increase the coupling efficiency between a silicon waveguide and a silver-air-silver plasmonic waveguide. Our numerical simulation results show that having the ASC at the interface makes the fabrication process much easier and ensures that light couples from a dielectric waveguide into and out of a plasmonic waveguide. The proposed coupler works over a broad frequency range achieving a coupling efficiency of 86% from a dielectric waveguide into a metal-dielectric-metal (MDM) plasmonic waveguide and 68% from a dielectric waveguide to an MDM plasmonic waveguide and back into another dielectric waveguide. In addition, we show that even if there are no high-precision fabrication techniques, light couples from a conventional dielectric waveguide (CDW) into an MDM plasmonic waveguide as long as there is an overlap between the CDW and ASC, which reduces the fabrication process tremendously. Our proposed coupler has an impact on the miniaturization of ultracompact nanoplasmonic devices.

Ti3C2Tx MXene Enabled All-Optical Nonlinear Activation Function for On-Chip Photonic Deep Neural Networks

Neural networks are one of the first major milestones in developing artificial intelligence systems. The utilisation of integrated photonics in neural networks offers a promising alternative approach to microelectronic and hybrid optical-electronic implementations due to improvements in computational speed and low energy consumption in machine-learning tasks. However, at present, most of the neural network hardware systems are still electronic-based due to a lack of optical realisation of the nonlinear activation function. Here, we experimentally demonstrate two novel approaches for implementing an all-optical neural nonlinear activation function based on utilising unique light-matter interactions in 2D Ti3C2Tx (MXene) in the infrared (IR) range in two configurations: 1) a saturable absorber made of MXene thin film, and 2) a silicon waveguide with MXene flakes overlayer. These configurations may serve as nonlinear units in photonic neural networks, while their nonlinear transfer function can be flexibly designed to optimise the performance of different neuromorphic tasks, depending on the operating wavelength. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure. We confirm the capability and feasibility of the obtained results in machine-learning applications via an Modified National Institute of Standards and Technology (MNIST) handwritten digit classifications task, with near 99% accuracy. Our developed concept for an all-optical neuron is expected to constitute a major step towards the realization of all-optically implemented deep neural networks.