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.

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.

In this paper, a microstructured laser with modulated periodic microstructures for beam control is proposed for the first time. Lasers using modulated periodic microstructures can emit beam with lower vertical divergence angle for the same number of microstructures. And lasers with specific period length can transmit beam in specific direction. These lasers emitting beams with different emission directions can be integrated into an array. And this array can be used directly as a transmitter chip of LiDAR. Both simulation and experimental results show that the modulated periodic microstructures can effectively reduce the vertical divergence angle. In the experiment, the lowest vertical divergence angle of 0.88° and a large angle sweep range of 41.5° have been obtained. A horizontal divergence angle of 4.3° is achieved with the taper structure. Typical optical power is 29 mW.

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.

Antennas are important components in optical phased arrays. However, their far-field performance deteriorates when random phase noise is introduced because of fabricating errors. For the first time, we use a finite-difference time-domain solution to quantitatively analyze the far-field characteristics of Si and Si₃N₄ antennas considering process errors. Under rough surface conditions based on a fishbone structure, we find that the quality of the main lobe of the Si antenna deteriorates badly, with −0.87 dB and −0.51 dB decreases in the sidelobe level and 5.78% and 3.74% deteriorations in the main peak power in the φ (phase-controlled) and θ (wavelength-controlled) directions, respectively. However, the Si₃N₄ antenna is only slightly impacted, with mere 0.39% and 0.71% deteriorations in the main peak power in the φ and θ directions, respectively, which is statistically about 1/15 of the Si antenna in the φ direction and 1/5 in the θ direction. The decreases in the sidelobe level are also slight, at about −0.08 dB and −0.01 dB, respectively. Furthermore, the advantages of the Si₃N₄ antenna become more remarkable with the introduction of random errors into the waveguide width and thickness. This work is of great significance for the design and optimization of OPA chips.

The focal plane array is regarded as a promising solution for LiDAR. In this work, we present an ultra-large-scale focal plane array featuring 1024 antennas and 2113 micro-rings with a FoV of 85.7° × 29.5°.