SPADnet: deep RGB-SPAD sensor fusion assisted by monocular depth estimation.

Single-photon light detection and ranging (LiDAR) techniques use emerging single-photon detectors (SPADs) to push 3D imaging capabilities to unprecedented ranges. However, it remains challenging to robustly estimate scene depth from the noisy and otherwise corrupted measurements recorded by a SPAD. Here, we propose a deep sensor fusion strategy that combines corrupted SPAD data and a conventional 2D image to estimate the depth of a scene. Our primary contribution is a neural network architecture-SPADnet-that uses a monocular depth estimation algorithm together with a SPAD denoising and sensor fusion strategy. This architecture, together with several techniques in network training, achieves state-of-the-art results for RGB-SPAD fusion with simulated and captured data. Moreover, SPADnet is more computationally efficient than previous RGB-SPAD fusion networks.

Interfacial engineering in graphene bandgap.

Graphene exhibits superior mechanical strength, high thermal conductivity, strong light-matter interactions, and, in particular, exceptional electronic properties. These merits make graphene an outstanding material for numerous potential applications. However, a graphene-based high-performance transistor, which is the most appealing application, has not yet been produced, which is mainly due to the absence of an intrinsic electronic bandgap in this material. Therefore, bandgap opening in graphene is urgently needed, and great efforts have been made regarding this topic over the past decade. In this review article, we summarise recent theoretical and experimental advances in interfacial engineering to achieve bandgap opening. These developments are divided into two categories: chemical engineering and physical engineering. Chemical engineering is usually destructive to the pristine graphene lattice via chemical functionalization, the introduction of defects, doping, chemical bonds with substrates, and quantum confinement; the latter largely maintains the atomic structure of graphene intact and includes the application of an external field, interactions with substrates, physical adsorption, strain, electron many-body effects and spin-orbit coupling. Although these pioneering works have not met all the requirements for electronic applications of graphene at once, they hold great promise in this direction and may eventually lead to future applications of graphene in semiconductor electronics and beyond.

Experimentally realized in situ backpropagation for deep learning in photonic neural networks.

Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using "in situ backpropagation," a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ([Formula: see text]94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.

Resonant scanning design and control for fast spatial sampling.

Two-dimensional, resonant scanners have been utilized in a large variety of imaging modules due to their compact form, low power consumption, large angular range, and high speed. However, resonant scanners have problems with non-optimal and inflexible scanning patterns and inherent phase uncertainty, which limit practical applications. Here we propose methods for optimized design and control of the scanning trajectory of two-dimensional resonant scanners under various physical constraints, including high frame-rate and limited actuation amplitude. First, we propose an analytical design rule for uniform spatial sampling. We demonstrate theoretically and experimentally that by expanding the design space, the proposed designs outperform previous designs in terms of scanning range and fill factor. Second, we show that we can create flexible scanning patterns that allow focusing on user-defined Regions-of-Interest (RoI) by modulation of the scanning parameters. The scanning parameters are found by an optimization algorithm. In simulations, we demonstrate the benefits of these designs with standard metrics and higher-level computer vision tasks (LiDAR odometry and 3D object detection). Finally, we experimentally implement and verify both unmodulated and modulated scanning modes using a two-dimensional, resonant MEMS scanner. Central to the implementations is high bandwidth monitoring of the phase of the angular scans in both dimensions. This task is carried out with a position-sensitive photodetector combined with high-bandwidth electronics, enabling fast spatial sampling at [Formula: see text] Hz frame-rate.

Greatly Enhanced Anticorrosion of Cu by Commensurate Graphene Coating.

Metal corrosion is a long-lasting problem in history and ultrahigh anticorrosion is one ultimate pursuit in the metal-related industry. Graphene, in principle, can be a revolutionary material for anticorrosion due to its excellent impermeability to any molecule or ion (except for protons). However, in real applications, it is found that the metallic graphene forms an electrochemical circuit with the protected metals to accelerate the corrosion once the corrosive fluids leaks into the interface. Therefore, whether graphene can be used as an excellent anticorrosion material is under intense debate now. Here, graphene-coated Cu is employed to investigate the facet-dependent anticorrosion of metals. It is demonstrated that as-grown graphene can protect Cu(111) surface from oxidation in humid air lasting for more than 2.5 years, in sharp contrast with the accelerated oxidation of graphene-coated Cu(100) surface. Further atomic-scale characterization and ab initio calculations reveal that the strong interfacial coupling of the commensurate graphene/Cu(111) prevents H2 O diffusion into the graphene/Cu(111) interface, but the one-dimensional wrinkles formed in the incommensurate graphene on Cu(100) can facilitate the H2 O diffusion at the interface. This study resolves the contradiction on the anticorrosion capacity of graphene and opens a new opportunity for ultrahigh metal anticorrosion through commensurate graphene coating.