Giant nonlinear self-phase modulation of large-amplitude spin waves in microscopic YIG waveguides.

Nonlinear self-phase modulation is a universal phenomenon responsible, for example, for the formation of propagating dynamic solitons. It has been reported for waves of different physical nature. However its direct experimental observation for spin waves has been challenging. Here we show that exceptionally strong phase modulation can be achieved for spin waves in microscopic waveguides fabricated from nanometer-thick films of magnetic insulator, which support propagation of spin waves with large amplitudes corresponding to angles of magnetization precession exceeding 10°. At these amplitudes, the nonstationary nonlinear dynamic response of the spin system causes an extreme broadening of the spectrum of spin-wave pulses resulting in a strong spatial variation of the spin-wave wavelength and a temporal variation of the spin-wave phase across the pulse. Our findings demonstrate great complexity of nonlinear wave processes in microscopic magnetic structures and importance of their understanding for technical applications of spin waves in integrated devices.

The rise of intelligent matter.

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.

Parallel convolutional processing using an integrated photonic tensor core.

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)1, the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important2. Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (1012 MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs3). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates3-5, ultralow-loss silicon nitride waveguides6,7, and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal-oxide-semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.

All-optical spiking neurosynaptic networks with self-learning capabilities.

Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.

Author Correction: Carbon nanotubes as emerging quantum-light sources.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Carbon nanotubes as emerging quantum-light sources.

In the version of this Perspective originally published, the x-axis label of Fig. 1d was missing; it should have read 'Wavelength (nm)'. The units of the y axis of Fig. 3b were incorrect; they should have been meV. And the citation of Fig. 3c in the main text was incorrect; it should have been to Fig. 3b. These issues have now been corrected.

Carbon nanotubes as emerging quantum-light sources.

Progress in quantum computing and quantum cryptography requires efficient, electrically triggered, single-photon sources at room temperature in the telecom wavelengths. It has been long known that semiconducting single-wall carbon nanotubes (SWCNTs) display strong excitonic binding and emit light over a broad range of wavelengths, but their use has been hampered by a low quantum yield and a high sensitivity to spectral diffusion and blinking. In this Perspective, we discuss recent advances in the mastering of SWCNT optical properties by chemistry, electrical contacting and resonator coupling towards advancing their use as quantum light sources. We describe the latest results in terms of single-photon purity, generation efficiency and indistinguishability. Finally, we consider the main fundamental challenges stemming from the unique properties of SWCNTs and the most promising roads for SWCNT-based chip integrated quantum photonic sources.

Calculating with light using a chip-scale all-optical abacus.

Machines that simultaneously process and store multistate data at one and the same location can provide a new class of fast, powerful and efficient general-purpose computers. We demonstrate the central element of an all-optical calculator, a photonic abacus, which provides multistate compute-and-store operation by integrating functional phase-change materials with nanophotonic chips. With picosecond optical pulses we perform the fundamental arithmetic operations of addition, subtraction, multiplication, and division, including a carryover into multiple cells. This basic processing unit is embedded into a scalable phase-change photonic network and addressed optically through a two-pulse random access scheme. Our framework provides first steps towards light-based non-von Neumann arithmetic.

Absorption engineering of NbN nanowires deposited on silicon nitride nanophotonic circuits.

We investigate the absorption properties of U-shaped niobium nitride (NbN) nanowires atop nanophotonic circuits. Nanowires as narrow as 20nm are realized in direct contact with Si3N4 waveguides and their absorption properties are extracted through balanced measurements. We perform a full characterization of the absorption coefficient in dependence of length, width and separation of the fabricated nanowires, as well as for waveguides with different cross-section and etch depth. Our results show excellent agreement with finite-element analysis simulations for all considered parameters. The experimental data thus allows for optimizing absorption properties of emerging single-photon detectors co-integrated with telecom wavelength optical circuits.

Waferscale nanophotonic circuits made from diamond-on-insulator substrates.

Wide bandgap dielectrics are attractive materials for the fabrication of photonic devices because they allow broadband optical operation and do not suffer from free-carrier absorption. Here we show that polycrystalline diamond thin films deposited by chemical vapor deposition provide a promising platform for the realization of large scale integrated photonic circuits. We present a full suite of photonic components required for the investigation of on-chip devices, including input grating couplers, millimeter long nanophotonic waveguides and microcavities. In microring resonators we measure loaded optical quality factors up to 11,000. Corresponding propagation loss of 5 dB/mm is also confirmed by measuring transmission through long waveguides.

High-quality Si3N4 circuits as a platform for graphene-based nanophotonic devices.

Hybrid circuits combining traditional nanophotonic components with carbon-based materials are emerging as a promising platform for optoelectronic devices. We demonstrate such circuits by integrating single-layer graphene films with silicon nitride waveguides as a new architecture for broadband optical operation. Using high-quality microring resonators and Mach-Zehnder interferometers with extinction ratios beyond 40 dB we realize flexible circuits for phase-sensitive detection on chip. Hybrid graphene-photonic devices are fabricated via mechanical transfer and lithographic structuring, allowing for prolonged light-matter interactions. Our approach holds promise for studying optical processes in low-dimensional physical systems and for realizing electrically tunable photonic circuits.

High-speed and high-efficiency travelling wave single-photon detectors embedded in nanophotonic circuits.

Ultrafast, high-efficiency single-photon detectors are among the most sought-after elements in modern quantum optics and quantum communication. However, imperfect modal matching and finite photon absorption rates have usually limited their maximum attainable detection efficiency. Here we demonstrate superconducting nanowire detectors atop nanophotonic waveguides, which enable a drastic increase of the absorption length for incoming photons. This allows us to achieve high on-chip single-photon detection efficiency up to 91% at telecom wavelengths, repeatable across several fabricated chips. We also observe remarkably low dark count rates without significant compromise of the on-chip detection efficiency. The detectors are fully embedded in scalable silicon photonic circuits and provide ultrashort timing jitter of 18 ps. Exploiting this high temporal resolution, we demonstrate ballistic photon transport in silicon ring resonators. Our direct implementation of a high-performance single-photon detector on chip overcomes a major barrier in integrated quantum photonics.

Modeling of the optical force between propagating lightwaves in parallel 3D waveguides.

We present a rigorous analysis of the optical gradient force between coupled single-mode waveguides in three dimensions. Using eigenmode expansion we determine the optical mode patterns in the coupled system. In contrast to previous work, the sign and amplitude of the optical force is found to vary along the waveguide with a characteristic beating length. Our results establish design principles for optomechanically tunable directional couplers.

Optomechanical coupling in photonic crystal supported nanomechanical waveguides.

We report improved optomechanical properties of a nano-mechanical beam resonator embedded in an optical race-track resonator. Precise control of the mechanical resonator is achieved by clamping the beam between two low-loss photonic crystal waveguide couplers. The low insertion loss and the rigid mechanical support provided by the couplers yield both good mechanical and optical Q-factors for improved signal quality.

Broadband all-photonic transduction of nanocantilevers.

Nanoelectromechanical systems based on cantilevers have consistently set records for sensitivity in measurements of displacement, force and mass over the past decade. Continued progress will require the integration of efficient transduction on a chip so that nanoelectromechanical systems may be operated at higher speeds and sensitivities. Conventional electrical schemes have limited bandwidth, and although optical methods are fast, they are subject to the diffraction limit. Here, we demonstrate the integration of nanocantilevers on a silicon photonic platform with a non-interferometric transduction scheme that avoids the diffraction limit by making use of near-field effects in optomechanical interactions. The use of a non-interferometric method means that a coherent light source is not required, making the monolithic integration of optomechanical systems with on-chip light sources feasible. We further demonstrate optomechanical multiplexing of an array of ten nanocantilevers with a displacement sensitivity of 40 fm Hz(-1/2).

Theoretical investigation of the transverse optical force between a silicon nanowire waveguide and a substrate.

We present a study of transverse optical forces arising in a free-standing silicon nanowire waveguide. A theoretical framework is provided for the calculation of the optical forces existing between a waveguide and a dielectric substrate. The force is evaluated using a numerical procedure based on finite-element simulations. In addition, an analytical formalism is developed which allows for a simple approximate analysis of the problem. We find that in this configuration optical forces on the order of pN can be obtained, sufficient to actuate nano-mechanical devices.

Harnessing optical forces in integrated photonic circuits.

The force exerted by photons is of fundamental importance in light-matter interactions. For example, in free space, optical tweezers have been widely used to manipulate atoms and microscale dielectric particles. This optical force is expected to be greatly enhanced in integrated photonic circuits in which light is highly concentrated at the nanoscale. Harnessing the optical force on a semiconductor chip will allow solid state devices, such as electromechanical systems, to operate under new physical principles. Indeed, recent experiments have elucidated the radiation forces of light in high-finesse optical microcavities, but the large footprint of these devices ultimately prevents scaling down to nanoscale dimensions. Recent theoretical work has predicted that a transverse optical force can be generated and used directly for electromechanical actuation without the need for a high-finesse cavity. However, on-chip exploitation of this force has been a significant challenge, primarily owing to the lack of efficient nanoscale mechanical transducers in the photonics domain. Here we report the direct detection and exploitation of transverse optical forces in an integrated silicon photonic circuit through an embedded nanomechanical resonator. The nanomechanical device, a free-standing waveguide, is driven by the optical force and read out through evanescent coupling of the guided light to the dielectric substrate. This new optical force enables all-optical operation of nanomechanical systems on a CMOS (complementary metal-oxide-semiconductor)-compatible platform, with substantial bandwidth and design flexibility compared to conventional electrical-based schemes.