Silicon-based photonic integration has attracted the interest of semiconductor scientists because it has high luminous efficiency and electron mobility. Breakthroughs have been made in silicon-based integrated lasers over the past few decades. Here we review three main methods of integration of III–V materials on Si, namely direct growth, bonding, and selective-area hetero-epitaxy. The III–V materials we introduced mainly include materials such as GaAs and InP. The lasers are mainly lasers of related communication bands. We also introduced the advantages and challenges of the three methods.

Due to the advantages of fast scanning speed, good stability, flexible pointing, high resolution, small size and low cost, optical phased array has become a potential key component of beam scanners. The effective aperture of optical phased array determines properties such as divergence angles and ranging distance, and how to achieve it has been of interest to researchers. In this letter, we have designed and fabricated a 1024-channel passive optical phased array chip. A small divergence angle (0.05 $^{\circ }\,\,\times 0.04^{\circ }$ ) has been achieved by adopting a Si ₃ N ₄ perturbation grating structure on Si waveguide. Based on this chip, we have built a coherent detection demonstration system and carried out relevant experiments with it.

Optical neural networks (ONNs) are getting more and more attention due to their advantages such as high-speed and low power consumption. However, in a non-ideal environment, the noise and low-bits control may heavily lead to a decrease in the accuracy of ONNs. Since there is AD/DA conversion in a simulated neural network, it needs to be quantified in the model. In this paper, we propose a quantitative method to adapt ONN to a non-ideal environment with fixed-point transmission, based on the new chip structure we designed previously. An MNIST hand-written data set was used to test and simulate the model we established. The experimental results showed that the quantization-noise model we established has a good performance, for which the accuracy was up to about 96%. Compared with the electrical method, the proposed quantization method can effectively solve the non-ideal ONN problem.

Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing.

To construct functional photonic integrated circuits, an efficient and compact laser is expected to be incorporated into the complementary metal–oxide–semiconductor platform. Monolithic integration of III–V lasers on pre-patterned Si substrates by the aspect ratio trapping method is a promising solution. Here, microwires with reversed ridge waveguides (RRWs) on pre-patterned Si substrates were reported. By metal–organic chemical vapor deposition, high-quality InP microwires with RRWs were obtained, and InGaAs/InGaAsP multi-quantum-well structures with InGaAsP separate confinement hetero structure (SCH–MQW) were successfully achieved. The SCH–MQW structure was buried in the InP microwire, which was beneficial for transferring the heat generated in the active region. The micron size also contributes to the efficiency of thermal diffusion. Further, simulation results showed that the metal absorption loss could be less than 4 dB/cm by properly controlling the contact area between metal electrodes and microwires. This proposed structure opens up an alternative pathway for electrically driven III–V lasers seamlessly interfaced with Si-photonics.