A novel optical antenna for optical phased arrays is proposed and simulated. A high-contrast grating structure is used to achieve extremely efficient emission. The emission efficiency is as high as 93.94% at 1.55 μm, which exceeds 50% in a range of wavelength from 1.48 μm to 1.62 μm. The antenna can achieve a perfect grating lobe suppression with background suppression of 28.4 dB when the phase difference between adjacent waveguides is 0. A 16-wire optical phased array can easily achieve a scan range of ± 22.8° × 20.2° with a beam width of 2.4° × 2.5°, by employing the optical antenna proposed.

Metal organic chemical vapor deposition of InGaAs/InP multi-quantum-well in nanoscale V-grooved trenches on Si (001) substrate was studied using the aspect ratio trapping method. A high quality GaAs/InP buffer layer with two convex {111} B facets was selectively grown to promote the highly uniform, single-crystal ridge InP/InGaAs multi-quantum-well structure growth. Material quality was confirmed by transmission electron microscopy and room temperature micro-photoluminescence measurements. This approach shows great promise for the fabrication of photonics devices and nanolasers on Si substrate.

In this article, the lineshape of Fano-like resonance of graphene plasmonic oligomers is investigated as a function of the parameters of the nanostructures, such as disk size, chemical potential and electron momentum relaxation time in mid-infrared frequencies. Also, the mechanism of the optimization is discussed. Furthermore, the environmental index sensing effect of the proposed structure is revealed, and a figure of merit of 25.58 is achieved with the optimized graphene oligomer. The proposed nanostructure could find applications in the fields of chemical or biochemical sensing.

We propose the photonic architecture based on microring resonator (MRR) arrays for binary convolutional neural networks (BCNNs) accelerated computing. The MRR crossbar unit is used for computing weight {−1, 1} and the single MRR is for input {0, 1}. The computing parallelism is improved through wavelength division multiplexing. The photonic BCNN achieves 97.29% classification accuracy on the MNIST test set which is only 1.94% lower than the accuracy of the 32-bit neural network, and saves $32\times $ at memory usage. We analyze effects of input and weight encoding errors on the photonic BCNN. When the input or weight error rate is less than 0.01%, the test accuracy remains unchanged. We evaluate the performance of the photonic BCNN architecture considering optical loss, inter-channel crosstalk, operation frequency and device power consumption. The energy efficiency of the designed photonic BCNN architecture is 1.72 pJ/MAC, which is $4.80\times $ and $61.32\times $ better than the 8-bit and 16-bit architecture respectively. The photonic BCNN is promising to be used for edge computing.

Optical phased array has become a potentially critical component for beam scanner benefiting from its advantages in cost, mass production, integration, stability and scanning rate. However, the practicalization of OPA still has some great challenges, such as complex circuit control and the need for pre-calibrated phase. In this paper, we propose two passive OPA chips based on silicon nitride delay lines, which can achieve two-dimensional scanning only by wavelength tuning. The two passive OPA chips have different delay line lengths between 128 channels, achieving 2D scanning efficiency of 0.44°/nm×0.07°/nm and 0.87°/nm×0.07°/nm. The beam quality of both passive OPA chips is excellent, with a side lobe level of about 12 dB, without any degradation compared to the OPA chip without the delay line. The delay length of the proposed passive OPA chip can be further increased to obtain denser 2D scans, thereby improving the practicality of the OPA chip.

A chip-level optical beam steerer is an inevitable choice for next-generation light detection and ranging (LiDAR). The research on optical phased array (OPA) is the most intriguing. However, the complexity of control and calibration speed limit the full potential as the number of channels increases. In this paper, an improved stochastic parallel gradient-descent algorithm combined with the Nesterov accelerated gradient method (NSPGD) is presented and applied in a 512-channel OPA. This algorithm can reduce the phase calibration time of large-scale OPA and demonstrates a better convergence performance than traditional SPGD. Compared with the traditional SPGD and hill-climbing (HC) algorithm, optimized convergence performance of NSPGD is shown. The side mode suppression ratio (SMSR) of over 10dB for 512-channel OPA is obtained with the NSPGD algorithm, and the convergence speed is twice that of traditional SPGD. In addition, a temperature-controlled OPA is also studied to stabilize the whole calibration system.