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.

Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data of the diffusion spot on the relay surface, which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation caused by target movement. In addition, we demonstrate the event-based cues based on the derivation of an event-NLOS forward model. Furthermore, we propose the first event-based NLOS imaging data set, EM-NLOS, and the movement feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity, which is 20% and 10% better than the frame-based method.

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.