ALBERT

Description: Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

Description: The deep-level traps induced by charged defects at the grain boundaries (GBs) of polycrystalline organic–inorganic halide perovskite (OIHP) films serve as major recombination centres, which limit the device performance. Herein, we incorporate specially designed poly(3-aminothiophenol)-coated gold (Au@PAT) nanoparticles into the perovskite absorber, in order to examine the influence of plasmonic resonance on carrier dynamics in perovskite solar cells. Local changes in the photophysical properties of the OIHP films reveal that plasmon excitation could fill trap sites at the GB region through photo-brightening, whereas transient absorption spectroscopy and density functional theory calculations correlate this photo-brightening of trap states with plasmon-induced interfacial processes. As a result, the device achieved the best efficiency of 22.0% with robust operational stability. Our work provides unambiguous evidence for plasmon-induced trap occupation in OIHP and reveals that plasmonic nanostructures may be one type of efficient additives to overcome the recombination losses in perovskite solar cells and thin-film solar cells in general.

Description: Orbital angular momentum interactions at the nanoscale have remained elusive because the phase structure becomes unresolved. Now researchers have shown how to overcome this with tightly focused beams, demonstrating a record-high six-dimensional encoding in an ultra-dense nanoscale volume.

Description: Engineering the spectral response of composite materials in a broad range from ultraviolet to infrared can lead to a significant passive cooling functionality. This principle is applied to the design of a novel type of metafabric for cooling the human body under direct sunlight. Besides cooling effect, the metafabric features other merits including superior mechanical and wetting properties.

Description: Optical interference is not only a fundamental phenomenon that has enabled new theories of light to be derived but it has also been used in interferometry for the measurement of small displacements, refractive index changes, and surface irregularities. In a two-beam interferometer, variations in the interference fringes are used as a diagnostic for anything that causes the optical path difference (OPD) to change; therefore, for a specified OPD, greater variation in the fringes indicates better measurement sensitivity. Here, we introduce and experimentally validate an interesting optical interference phenomenon that uses photons with a structured frequency-angular spectrum, which are generated from a spontaneous parametric down-conversion process in a nonlinear crystal. This interference phenomenon is manifested as interference fringes that vary much more rapidly with increasing OPD than the corresponding fringes for equal-inclination interference; the phenomenon is parameterised using an equivalent wavelength, which under our experimental conditions is 29.38 nm or about 1/27 of the real wavelength. This phenomenon not only enriches the knowledge with regard to optical interference but also offers promise for applications in interferometry.

Description: With rapid advances in high-speed communication and computation, augmented reality (AR) and virtual reality (VR) are emerging as next-generation display platforms for deeper human-digital interactions. Nonetheless, to simultaneously match the exceptional performance of human vision and keep the near-eye display module compact and lightweight imposes unprecedented challenges on optical engineering. Fortunately, recent progress in holographic optical elements (HOEs) and lithography-enabled devices provide innovative ways to tackle these obstacles in AR and VR that are otherwise difficult with traditional optics. In this review, we begin with introducing the basic structures of AR and VR headsets, and then describing the operation principles of various HOEs and lithography-enabled devices. Their properties are analyzed in detail, including strong selectivity on wavelength and incident angle, and multiplexing ability of volume HOEs, polarization dependency and active switching of liquid crystal HOEs, device fabrication, and properties of micro-LEDs (light-emitting diodes), and large design freedoms of metasurfaces. Afterwards, we discuss how these devices help enhance the AR and VR performance, with detailed description and analysis of some state-of-the-art architectures. Finally, we cast a perspective on potential developments and research directions of these photonic devices for future AR and VR displays.

Description: We review recent studies of cavity switching induced by the optical injection of free carriers in micropillar cavities containing quantum dots. Using the quantum dots as a broadband internal light source and a streak camera as detector, we track the resonance frequencies for a large set of modes with picosecond time resolution. We report a record-fast switch-on time constant (1.5 ps) and observe major transient modifications of the modal structure of the micropillar on the 10 ps time scale: mode crossings are induced by a focused symmetric injection of free carriers, while a lifting of several mode degeneracies is observed when off-axis injection breaks the rotational symmetry of the micropillar. We show theoretically and experimentally that cavity switching can be used to tailor the dynamic properties of the coupled QD–cavity system. We report the generation of ultrashort spontaneous emission pulses (as short as 6 ps duration) by a collection of frequency-selected QDs in a switched pillar microcavity. These pulses display a very small coherence length, attractive for ultrafast speckle-free imaging. Moreover, the control of QD-mode coupling on the 10 ps time scale establishes cavity switching as an appealing resource for quantum photonics.

Description: EditorialIn 2016, the news that Google’s artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor’s Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.

Description: Multiferroics are being studied increasingly in applications of photovoltaic devices for the carrier separation driven by polarization and magnetization. In this work, textured black silicon photovoltaic devices are fabricated with Bi6Fe1.6Co0.2Ni0.2Ti3O18/Bi2FeCrO6 (BFCNT/BFCO) multiferroic heterojunction as an absorber and graphene as an anode. The structural and optical analyses showed that the bandgap of Aurivillius-typed BFCNT and double perovskite BFCO are 1.62 ± 0.04 eV and 1.74 ± 0.04 eV respectively, meeting the requirements for the active layer in solar cells. Under the simulated AM 1.5 G illumination, the black silicon photovoltaic devices delivered a photoconversion efficiency (η) of 3.9% with open-circuit voltage (Voc), short-circuit current density (Jsc), and fill factor (FF) of 0.75 V, 10.8 mA cm−2, and 48.3%, respectively. Analyses of modulation of an applied electric and magnetic field on the photovoltaic properties revealed that both polarization and magnetization of multiferroics play an important role in tuning the built-in electric field and the transport mechanisms of charge carriers, thus providing a new idea for the design of future high-performance multiferroic oxide photovoltaic devices.

Description: The deployment of photonic integrated circuits (PICs) necessitates an integration platform that is scalable, high-throughput, cost-effective, and power-efficient. Here we present a monolithic InP on SOI platform to synergize the advantages of two mainstream photonic integration platforms: Si photonics and InP photonics. This monolithic InP/SOI platform is realized through the selective growth of both InP sub-micron wires and large dimension InP membranes on industry-standard (001)-oriented silicon-on-insulator (SOI) wafers. The epitaxial InP is in-plane, dislocation-free, site-controlled, intimately positioned with the Si device layer, and placed right on top of the buried oxide layer to form “InP-on-insulator”. These attributes allow for the realization of various photonic functionalities using the epitaxial InP, with efficient light interfacing between the III–V devices and the Si-based waveguides. We exemplify the potential of this InP/SOI platform for integrated photonics through the demonstration of lasers with different cavity designs including subwavelength wires, square cavities, and micro-disks. Our results here mark a critical step forward towards fully-integrated Si-based PICs.