Silicon photonics is an enabling technology that provides integrated photonic devices and systems with low-cost mass manufacturing capability. It has attracted increasing attention in both academia and industry in recent years, not only for its applications in communications, but also in sensing. One important issue of silicon photonics that comes with its high integration density is an interface between its high-performance integrated waveguide devices and optical fibers or free-space optics. Surface grating coupler is a preferred candidate that provides flexibility for circuit design and reduces effort for both fabrication and alignment. In the past decades, considerable research efforts have been made on in-plane grating couplers to address their insufficiency in coupling efficiency, wavelength sensitivity and polarization sensitivity compared with out-of-plane edge-coupling. Apart from improved performances, new functionalities are also on the horizon for grating couplers. In this paper, we review the current research progresses made on grating couplers, starting from their fundamental theories and concepts. Then, we conclude various methods to improve their performance, including coupling efficiency, polarization and wavelength sensitivity. Finally, we discuss some emerging research topics on grating couplers, as well as practical issues such as testing, packaging and promising applications.

In the past few decades, silicon photonics has witnessed a ramp-up of investment in both research and industry. As a basic building block, silicon waveguide crossing is inevitable for dense silicon photonic integrated circuits and efficient crossing designs will greatly improve the performance of photonic devices with multiple crossings. In this paper, we focus on the state-of-the-art and perspectives on silicon waveguide crossings. It reviews several classical structures in silicon waveguide crossing design, such as shaped taper, multimode interference, subwavelength grating, holey subwavelength grating and vertical directional coupler by forward or inverse design method. In addition, we introduce some emerging research directions in crossing design including polarization-division-multiplexing and mode-division-multiplexing technologies.

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

Silicon photonics enables compact integrated photonic devices with versatile functionalities and mass manufacturing capability. However, the optimization of high‐performance free‐form optical devices is still challenging due to the complex light‐matter interaction involved that requires time‐consuming electromagnetic simulations. This problem becomes even more prominent when multiple devices are required, typically requiring separate iterative optimizations. To facilitate multi‐task inverse design, we propose a topology optimization method based on deep neural network (DNN) in low‐dimensional Fourier domain. The DNN takes target optical responses as inputs and predicts low‐frequency Fourier components, which are then utilized to reconstruct device geometries. Removing high‐frequency components for reduced design degree‐of‐freedom (DOF) helps control minimal features and speed up training. For demonstration, the proposed method is utilized for wavelength filter design. The trained DNN can design multiple filters instantly and concurrently with high accuracy. Totally different targets can also be further optimized through transfer learning on existing network with greatly reduced optimization rounds. Our approach can be also adapted to other free‐form photonic devices, including a waveguide‐coupled single‐photon source that we demonstrate to prove generalizability. Such DNN‐assisted topology optimization significantly reduces the time and resources required for multi‐task optimization, enabling large‐scale photonic device design in various applications.

Recent research in silicon photonic chips has made huge progress in optical computing owing to their high speed, small footprint, and low energy consumption. Here, we employ nanostructured 2×2 optical processors in an optical neural network for implementing a binary classification task efficiently. The proposed optical neural network is composed of five linear layers including ten optical processors in each layer, and nonlinear activation functions. 2×2 optical processors are designed based on digitized meta-structures which have an extremely compact footprint of 1.6×4 μm ² . A brand-new end-to-end design strategy based on Deep Q-Network is proposed to optimize the optical neural network for classifying a generated ring data set with better generalization, robustness, and operability. A high-efficient transfer matrix multiplication method is applied to simplify the calculation process in traditional optical software. Our numerical results illustrate that the maximum and mean accuracy on the testing data set can reach 90.5% and 87.8%, respectively. The demonstrated optical processors with a significantly compact area, and the efficient optimization method exhibit high potential for large-scale integration of whole-passive optical neural network on a photonic chip.