In the last decade, silicon photonic switches are increasingly believed to be potential candidates for replacing the electrical switches in the applications of telecommunication networks, data center and high-throughput computing, due to their low power consumption (Picojoules per bit), large bandwidth (Terabits per second) and high-level integration (Square millimeters per port). This review paper focuses on the state of the art and our perspectives on silicon photonic switching technologies. It starts with a review of three types of fundamental switch engines, i.e., Mach-Zehnder interferometer, micro-ring resonator and micro-electro-mechanical-system actuated waveguide coupler. The working mechanisms are introduced and the key specifications such as insertion loss, crosstalk, switching time, footprint and power consumption are evaluated. Then it is followed by the discussion on the prototype of large-scale silicon photonic fabrics, which are based on the configuration of above-mentioned switch engines. In addition, the key technologies, such as topological architecture, passive components and optoelectronic packaging, to improve the overall performance are summarized. Finally, the critical challenges that might hamper the silicon photonic switching technologies transferring from proof-of-concept in lab to commercialization are also discussed.

In this work, a surface plasmon resonance (SPR) biosensor based on two-dimensional transition metal dichalcogenides (TMDCs) is proposed to improve the biosensor’s sensitivity. In this sensor, different kinds of two-dimensional TMDCs are coated on both surfaces of metal film. By optimizing the structural parameters, the angular sensitivity can reach as high as 315.5 Deg/RIU with 7-layers WS₂ and 36 nm Al thin film, which is 3.3 times of the conventional structure based on single Al thin film. We also obtain maximum phase sensitivity (3.85 × 10⁶ Deg/RIU) with bilayer WS₂ and 35 nm Al thin film. The phase sensitivity can be further improved by employing Ag and removing air layer. The proposed configuration is of great potential for biochemical sensing.

Deep neural networks (DNNs) have been introduced to achieve the rapid design of photonic devices by creating a nonlinear function mapping the geometric structure to the optical response. By building the DNN with a finite-difference time-domain (FDTD) solver, we have demonstrated that both forward and inverse design approaches can be used to design efficiently a silicon photonic grating coupler-one of the fundamental silicon photonic devices with a wavelength-sensitive optical response, respectively. A systematic study on the model parameters including number of hidden layers, number of nodes in each layer, initial learning rate, size of training batches, number of evolution epochs, and dataset size/distribution has been carried out to analyze the relationship between the DNNs and the performances of inverse-designed devices. The study shows that the forward design approach based on an optimal forward-modeling network can achieve a peak coupling efficiency with a prediction accuracy as high as 91.7% for the coupler. And the inverse design approach based on an optimal inverse-prediction network can obtain target optical response spectrum as well as provide possibility to get an alternative design for the device. This work is helpful for the designers to improve the machine learning methods and expedite the design progress towards the creation of novel silicon photonic devices.