With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture. To harness the benefits of optical parallelism while ensuring compatibility with natural light scenes, it becomes essential to implement two-dimensional spatial nonlinearity within an incoherent light environment. Here, we demonstrate a lensless opto-electrical neural network that incorporates optical nonlinearity, capable of performing convolution calculations and achieving nonlinear activation via a quantum dot film, all without an external power supply. Through simulation and experiments, the proposed nonlinear system can enhance the accuracy of image classification tasks, yielding a maximum improvement of 5.88% over linear models. The scheme shows a facile implementation of passive incoherent two-dimensional nonlinearities, paving the way for the applications of multilayer incoherent optical neural networks in the future.

Ever-growing deep-learning technologies are making revolutionary changes for modern life. However, conventional computing architectures are designed to process sequential and digital programs but are burdened with performing massive parallel and adaptive deep-learning applications. Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and the power-wall brought on by its electronic counterparts, showing great potential in ultrafast and energy-free high-performance computation. Here, we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed “optical convolution unit” (OCU). We demonstrate that any real-valued convolution kernels can be exploited by the OCU with a prominent computational throughput boosting via the concept of structral reparameterization. With the OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression. For classification, Fashion Modified National Institute of Standards and Technology (Fashion-MNIST) and Canadian Institute for Advanced Research (CIFAR-4) data sets are tested with accuracies of 91.63% and 86.25%, respectively. For regression, we build an optical denoising convolutional neural network to handle Gaussian noise in gray-scale images with noise level σ=10, 15, and 20, resulting in clean images with an average peak signal-to-noise ratio (PSNR) of 31.70, 29.39, and 27.72 dB, respectively. The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint, providing a parallel while lightweight solution for future compute-in-memory architecture to handle high dimensional tensors in deep learning.

In this paper, an artificial-intelligence-based fiber communication receiver model is put forward. With the multi-head attention mechanism it contains, this model can extract crucial patterns and map the transmitted signals into the bit stream. Once appropriately trained, it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km, signal-to-noise ratios range from 0 to 20 dB, modulation formats range from OOK to PAM4, and symbol rates range from 10 to 40 GBaud. The validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.

Microwave photonic receivers are a promising candidate in breaking the bandwidth limitation of traditional radio-frequency (RF) receivers. To further balance the performance superiority with the requirements regarding size, weight, and power consumption (SWaP), the implementation of integrated microwave photonic microsystems has been considered an upgrade path. However, up to now, to the best of our knowledge, chip-scale fully integrated microwave photonic receivers have not been reported due to the limitation of material platforms. In this paper, we report a fully integrated hybrid microwave photonic receiver (FIH-MWPR) obtained by comprising the indium phosphide (InP) laser chip and the monolithic silicon-on-insulator (SOI) photonic circuit into the same substrate based on the low-coupling-loss micro-optics method. Benefiting from the integration of all optoelectronic components, the packaged FIH-MWPR exhibits a compact volume of 6cm3 and low power consumption of 1.2 W. The FIH-MWPR supports a wide operation bandwidth from 2 to 18 GHz. Furthermore, its RF-link performance to down-convert the RF signals to the intermediate frequency is experimentally characterized by measuring the link gain, the noise figure, and the spurious-free dynamic range metrics across the whole operation frequency band. Moreover, we have utilized it as a de-chirp receiver to process the broadband linear frequency-modulated (LFM) radar echo signals at different frequency bands (S-, C-, X-, and Ku-bands) and successfully demonstrated its high-resolution-ranging capability. To the best of our knowledge, this is the first realization of a chip-scale broadband fully integrated microwave photonic receiver, which is expected to be an important step in demonstrating the feasibility of all-integrated microwave photonic microsystems oriented to miniaturized application scenarios.

This erratum corrects Fig. 5 in Photon. Res.9, 1924 (2021)PRHEIZ2327-912510.1364/PRJ.432292.

A hybrid integrated low-noise linear chirp frequency-modulated continuous-wave (FMCW) laser source with a wide frequency bandwidth is demonstrated. By employing two-dimensional thermal tuning, the laser source shows frequency modulation bandwidth of 10.3 GHz at 100 Hz chirped frequency and 5.6 GHz at 1 kHz chirped frequency. The intrinsic linewidth of 49.9 Hz with 42 GHz continuous frequency tuning bandwidth is measured under static operation. Furthermore, by pre-distortion linearization of the laser source, it can distinguish 3 m length difference at 45 km distance in the fiber length measurement experiment, demonstrating its application potential in ultra-long fiber sensing and FMCW light detection and ranging.

For moving objects, 3D mapping and tracking has found important applications in the 3D reconstruction for vision odometry or simultaneous localization and mapping. This paper presents a novel camera architecture to locate the fast-moving objects in four-dimensional (4D) space (x, y, z, t) through a single-shot image. Our 3D tracking system records two orthogonal fields-of-view (FoVs) with different polarization states on one polarization sensor. An optical spatial modulator is applied to build up temporal Fourier-phase coding channels, and the integration is performed in the corresponding CMOS pixels during the exposure time. With the 8 bit grayscale modulation, each coding channel can achieve 256 times temporal resolution improvement. A fast single-shot 3D tracking system with 0.78 ms temporal resolution in 200 ms exposure is experimentally demonstrated. Furthermore, it provides a new image format, Fourier-phase map, which has a compact data volume. The latent spatio-temporal information in one 2D image can be efficiently reconstructed at relatively low computation cost through the straightforward phase matching algorithm. Cooperated with scene-driven exposure as well as reasonable Fourier-phase prediction, one could acquire 4D data (x, y, z, t) of the moving objects, segment 3D motion based on temporal cues, and track targets in a complicated environment.

集成、宽带、大色散延时的器件在微波光子滤波、真延时相控阵天线等领域有着重要的应用，可以有效地降低系统尺寸和功耗。文中提出并实现了一种基于硅基光子集成的宽带大色散延时芯片，通过采用超低损耗波导结构和侧壁法向量调制结构实现了片上集成大色散波导光栅，色散值超过250 ps/nm， 最大群延时达到2440 ps，带宽大于9.4 nm，该芯片有望用于微波光子学、高速光纤通信系统等领域。