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An on-chip asymmetric directional coupler (DC) can convert fundamental modes to higher-order modes and is one of the core components of mode-division multiplexing (MDM) technology. In this study, we propose that waveguides of the asymmetric DC can be trimmed by silicon ion implantation to tune the effective refractive index and facilitate mode conversion into higher-order modes. Through this method of tuning, transmission changes of up to 18 dB have been realized with one ion implantation step. In addition, adjusting the position of the ion implantation on the waveguide can provide a further degree of control over the transmission into the resulting mode. The results of this work present a promising new route for the development of high-efficiency, low-loss mode converters for integrated photonic platforms, and aim to facilitate the application of MDM technology in emerging photonic neuromorphic computing.

Advancements in optical coherence control1–5have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Fast modulation of optical signals that carry multidimensional information in the form of wavelength, phase or polarization has fueled an explosion of interest in integrated photonics. This interest however masks a significant challenge which is that independent modulation of multi-wavelength carrier signals in a single waveguide is not trivial. Such challenge is attributed to the longitudinal direction of guided-mode propagation, limiting the spatial separation and modulation of electric-field. Here, we overcome this using a single photonic element that utilizes active coherent (near) perfect absorption. We make use of standing wave patterns to exploit the spatial-degrees-of-freedom of in-plane modes and individually address elements according to their mode number. By combining the concept of coherent absorption in spatio-spectral domain with active phase-change nanoantennas, we engineer and test an integrated, reconfigurable and multi-spectral modulator operating within a single element. Our approach demonstrates for the first time, a non-volatile, wavelength-addressable element, providing a pathway for exploring the tunable capabilities in both spatial and spectral domains of coherent nanophotonics.

New developments in hardware-based ‘accelerators’ range from electronic tensor cores and memristor-based arrays to photonic implementations. The goal of these approaches is to handle the exponentially growing computational load of machine learning, which currently requires the doubling of hardware capability approximately every 3.5 months. One solution is increasing the data dimensionality that is processable by such hardware. Although two-dimensional data processing by multiplexing space and wavelength has been previously reported, the use of three-dimensional processing has not yet been implemented in hardware. In this paper, we introduce the radio-frequency modulation of photonic signals to increase parallelization, adding an additional dimension to the data alongside spatially distributed non-volatile memories and wavelength multiplexing. We leverage higher-dimensional processing to configure such a system to an architecture compatible with edge computing frameworks. Our system achieves a parallelism of 100, two orders higher than implementations using only the spatial and wavelength degrees of freedom. We demonstrate this by performing a synchronous convolution of 100 clinical electrocardiogram signals from patients with cardiovascular diseases, and constructing a convolutional neural network capable of identifying patients at sudden death risk with 93.5% accuracy.

Memristors in electronics have shown the potential for a range of applications, ranging from circuit elements to neuromorphic computing. In recent years, the ability to vary the conductance of a channel in electronics has enabled in-memory computing, thus leading to substantial interest in memristors. Optical analogues will require modulation of the transmission of light in a semicontinuous and nonvolatile manner. With the proliferation of photonic computing, such an optical analogue, which involves modulating the optical response in integrated circuits while maintaining the modulated state afterwards, is being pursued using a range of functional materials. Here we review recent progress in this important and emerging aspect of photonic integrated circuits and provide an overview of the current state of the art. Optical memristors are of particular interest for applications in high-bandwidth neuromorphic computing, machine learning hardware and artificial intelligence, as these optical analogues of memristors allow for technology that combines the ultrafast, high-bandwidth communication of optics with local information processing.

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide’s effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics.

Integrated neuromorphic photonic circuits aim to power complex artificial neural networks (ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low loss of photonic structures. However, scaling photonic circuits to match the requirements of modern ANNs still remains challenging. In this perspective, we give an overview over the usual sizes of matrices processed in ANNs and compare them with the capability of existing photonic matrix processors. To address shortcomings of existing architectures, we propose a time multiplexed matrix processing scheme which virtually increases the size of a physical photonic crossbar array without requiring any additional electrical post-processing. We investigate the underlying process of time multiplexed incoherent optical accumulation and achieve accumulation accuracy of 98.9% with 1 ns pulses. Assuming state of the art active components and a reasonable crossbar array size, this processor architecture would enable matrix vector multiplications with 16,000 × 64 matrices all optically on an estimated area of 51.2 mm2, while performing more than 110 trillion multiply and accumulate operations per second.

The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of materials known as phase change materials has been extensively used to demonstrate the viability of such computing. However, such materials continue to have relatively slow switching speeds, and issues with cyclability related to phase segregation of phase change alloys. Here, using antimony (Sb) thin films with thicknesses less than 5 nm we demonstrate reversible, ultrafast switching on an integrated photonic platform with retention time of tens of seconds. We use subpicosecond pulses, the shortest used to switch such elements, to program seven distinct memory levels. This portends their use in ultrafast nanophotonic applications ranging from nanophotonic beam steerers to nanoscale integrated elements for photonic computing.

We study degenerate spontaneous parametric downconversion in a structure composed of two linearly uncoupled resonators, in which the linear properties of the fundamental and second-harmonic field can be engineered independently. As an example, we show that in this system it is simple to generate photon pairs that are nearly uncorrelated in energy. These results extend the use of linearly uncoupled resonators to the case of second-order nonlinear interactions.

The integration of artificial intelligence (AI) systems in the daily life greatly increases the amount of data generated and processed. In addition to the large computational power required, the hardware needs to be compact and energy efficient. One promising approach to fulfill those requirements is phase-change material based photonic neuromorphic computing that enables in-memory computation and a high degree of parallelization. In the following, we present an optimized layout of a photonic tensor core (PTC) which is designed to perform real valued matrix vector multiplications and operates at telecommunication wavelengths. We deploy the well-studied phase-change material Ge2Sb2Te5(GST) as an optical attenuator to perform single positive valued multiplications. In order to generalize the multiplication to arbitrary real factors, we develop a novel symmetric multiplication unit which directly includes a reference-computation branch. The variable GST attenuator enables a modulation depth of 5 dB over a wavelength range of 100 nm with a wavelength dependency below 0.8 dB. The passive photonic circuit itself ensures equal coupling to the main-computation and reference-computation branch over the complete wavelength range. For the first time, we integrate wavelength multiplexers (MUX) together with a photonic crossbar array on-chip, paving the way towards fully integrated systems. The MUX are crucial for the PTC since they enable multiple computational channels in a single photonic crossbar array. We minimize the crosstalk between the channels by designing Bragg scattering based MUX. By cascading, we achieve an extinction ratio larger than 61 dB while the insertion loss is below 1 dB.

Graphical abstract:

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Hybrid quantum photonics combines classical photonics with quantum emitters in a postprocessing step. It facilitates to link ideal quantum light sources to optimized photonic platforms. Optical cavities enable to harness the Purcell-effect boosting the device efficiency. Here, we postprocess a free-standing, crossed-waveguide photonic crystal cavity based on Si3N4with SiV−center in nanodiamonds. We develop a routine that optimizes the overlap with the cavity electric field utilizing atomic force microscope (AFM) nanomanipulation to attain control of spatial and dipole alignment. Temperature tuning further gives access to the spectral emitter-cavity overlap. After a few optimization cycles, we resolve the fine-structure of individual SiV−centers and achieve a Purcell enhancement of more than 4 on individual optical transitions, meaning that four out of five spontaneously emitted photons are channeled into the photonic device. Our work opens up new avenues to construct efficient quantum photonic devices.

Recent developments in chip-based photonic quantum circuits have radically impacted quantum information processing. However, it is challenging for monolithic photonic platforms to meet the stringent demands of most quantum applications. Hybrid platforms combining different photonic technologies in a single functional unit have great potential to overcome the limitations of monolithic photonic circuits. Our Review summarizes the progress of hybrid quantum photonics integration, discusses important design considerations, including optical connectivity and operation conditions, and highlights several successful realizations of key physical resources for building a quantum teleporter. We conclude by discussing the roadmap for realizing future advanced large-scale hybrid devices, beyond the solid-state platform, which hold great potential for quantum information applications.

Harnessing tailored disorder for broadband light scattering enables high-resolution signal analysis in nanophotonic spectrometers with a small device footprint. Multiple scattering events in the disordered medium enhance the effective path length which leads to increased resolution. Here we demonstrate an on-chip random spectrometer cointegrated with superconducting single-photon detectors suitable for photon-scarce environments. We combine an efficient broadband fiber-to-chip coupling approach with a random scattering area and broadband transparent silicon nitride waveguides to operate the spectrometer in a diffusive regime. Superconducting nanowire single-photon detectors at each output waveguide are used to perform spectral-to-spatial mapping via the transmission matrix at the system, allowing us to reconstruct a given probe signal. We show operation over a wide spectral range with sensitivity down to powers of −111.5 dBm in the telecom band.

Advances in artificial intelligence have greatly increased demand for data-intensive computing. Integrated photonics is a promising approach to meet this demand in big-data processing due to its potential for wide bandwidth, high speed, low latency, and low-energy computing. Photonic computing using phase-change materials combines the benefits of integrated photonics and co-located data storage, which of late has evolved rapidly as an emerging area of interest. In spite of rapid advances of demonstrations in this field on both silicon and silicon nitride platforms, a clear pathway towards choosing between the two has been lacking. In this paper, we systematically evaluate and compare computation performance of phase-change photonics on a silicon platform and a silicon nitride platform. Our experimental results show that while silicon platforms are superior to silicon nitride in terms of potential for integration, modulation speed, and device footprint, they require trade-offs in terms of energy efficiency. We then successfully demonstrate single-pulse modulation using phase-change optical memory on silicon photonic waveguides and demonstrate efficient programming, memory retention, and readout of $ \gt {4}$>4 bits of data per cell. Our approach paves the way for in-memory computing on the silicon photonic platform.

A lithography-free photonic processor through dynamic control of optical gain distributions is demonstrated, allowing reconfigurable photonic neural networks and more efficient signal processing, and showing great promise in easing data traffic as well as accelerating information processing speeds.

Photonic integrated circuits require efficient interfaces to fiber optic components for applications in optical communication, computing, and sensing. Current optical interconnects rely on edge or grating couplers with limitations in terms of alignment tolerances, efficiency, and bandwidth, respectively. Here, we present a scalable coupling concept that allows fiber-to-chip coupling to the fundamental transverse electric and transverse magnetic modes based on three-dimensional nanostructures exploiting total internal reflection. We demonstrate close to octave-spanning highly efficient coupling to nanophotonic waveguides in the visible wavelength range. Our coupling scheme can be adjusted for other wavelength regimes and fiber mode-field diameters.

Abstract

Integrated nanophotonics is an emerging field with high potential for quantum technology applications such as quantum sensing or quantum networks. A desired photonics platform is Si3N4due to low-photon loss and well-established fabrication techniques. However, quantum optics applications are not yet established. Here, we investigate an approach toward Si3N4-based quantum photonics utilizing a crossed waveguide, pump–probe design. The platform enables efficient, on-chip excitation, strong background suppression, and at the same time, efficient coupling to the mode of a high-Qphotonic crystal cavity. The freestanding photonic crystal cavities reach high Q-factors up to 47 × 103. To test our platform, we positioned an ensemble of negatively charged nitrogen vacancy centers located in a nanodiamond within the interaction zone of the photonic crystal cavity. We quantify the efficiency of the coupling with the βλ-factor reaching values as large as 0.71. We further demonstrate on-chip excitation of the quantum emitter with strong suppression (∼20 dB) of the background fluorescence. Our results unfold the potential to utilize negatively charged nitrogen vacancy centers in nanodiamonds and Si3N4platforms as an efficient, on-chip spin-photon interface in quantum photonics experiments.

The Stark effect provides a powerful method to shift the spectra of molecules, atoms, and electronic transitions in general, becoming one of the simplest and most straightforward ways to tune the frequency of quantum emitters by means of a static electric field. At the same time, in order to reduce the emitter sensitivity to charge noise, inversion symmetric systems are typically designed, providing a stable emission frequency with a quadratic-only dependence on the applied field. However, such nonlinear behavior might be reflected in correlations between the tuning ability and unwanted spectral fluctuations. Here, we provide experimental evidence of this trend using molecular quantum emitters in the solid state cooled down to liquid helium temperatures. We finally combine the electric field generated by electrodes, which is parallel to the molecule’s induced dipole, with optically excite long-lived charge states acting in the perpendicular direction. Based on the anisotropy of the molecule’s polarizability, our two-dimensional control of the local electric field allows us not only to tune the emitter’s frequency but also to sensibly suppress the spectral instabilities associated with field fluctuations.

Optically storing and addressing data on photonic chips is of particular interest as such capability would eliminate optoelectronic conversion losses in data centers. It would also enable on-chip non-von Neumann photonic computing by allowing multinary data storage with high fidelity. Here, we demonstrate such an optically addressed, multilevel memory capable of storing up to 34 nonvolatile reliable and repeatable levels (over 5 bits) using the phase change material Ge_2Sb_2Te_5 integrated on a photonic waveguide. Crucially, we demonstrate for the first time, to the best of our knowledge, a technique that allows us to program the device with a single pulse regardless of the previous state of the material, providing an order of magnitude improvement over previous demonstrations in terms of both time and energy consumption. We also investigate the influence of write-and-erase pulse parameters on the single-pulse recrystallization, amorphization, and readout error in our multilevel memory, thus tailoring pulse properties for optimum performance. Our work represents a significant step in the development of photonic memories and their potential for novel integrated photonic applications.

Integration of superconducting nanowire single-photon detectors with nanophotonic waveguides is a key technological step that enables a broad range of classical and quantum technologies on chip-scale platforms. The excellent detection efficiency, timing and noise performance of these detectors have sparked growing interest over the last decade and have found use in diverse applications. Almost 10 years after the first waveguide-coupled superconducting detectors were proposed, here, we review the performance metrics of these devices, compare both superconducting and dielectric waveguide material systems and present prominent emerging applications.

Compact spectrometers based on disordered planar waveguides exhibit a rather high resolution with a relatively small footprint compared to conventional spectrometers. This is achieved by multiple scattering of light, which—if properly engineered—significantly enhances the effective optical path length. Here a design study of random spectrometers for TE- and TM-polarized light is presented that combines the results of Mie theory, multiple-scattering theory, and full electromagnetic simulations. It is shown that the performance of such random spectrometers depends on single-scattering quantities, notably on the overall scattering efficiency and the asymmetry parameter. Further, the study shows that a well-developed diffusive regime is not required in practice and that a standard integrated-optical layout is sufficient to obtain efficient devices even for rather weakly scattering systems consisting of low index inclusions in high-index matrices, such as pores in planar silicon-nitride-based waveguides. This allows for both significant reductions in footprint with acceptable losses in resolution and for device operation in the visible and near-infrared frequency range.

The combination of efficient single-photon generation, manipulation, and detection on a single chip poses a major challenge for quantum photonics and all-optical quantum computing. Among a multitude of detection technologies, waveguide-integrated superconducting nanowire single-photon detectors stand out as they promise near-unity detection efficiencies at outstanding timing accuracy and speed. Here, by exploiting the concept of critical coupling, we present the integration of a short nanowire into a two-dimensional double heterostructure photonic crystal cavity to realize an integrated single-photon detector with excellent performance metric. The complete detector characterization reveals on-chip detection efficiencies of almost 70% at telecom wavelengths, recovery times of 480 ps, and vanishingly low dark count rates. Our design paves the way for the implementation of compact on-chip detector arrays and time-multiplexed single-detector schemes.

Photonic quantum technologies allow quantum phenomena to be exploited in applications such as quantum cryptography, quantum simulation and quantum computation. A key requirement for practical devices is the scalable integration of single-photon sources, detectors and linear optical elements on a common platform. Nanophotonic circuits enable the realization of complex linear optical systems, while non-classical light can be measured with waveguide-integrated detectors. However, reproducible single-photon sources with high brightness and compatibility with photonic devices remain elusive for fully integrated systems. Here, we report the observation of antibunching in the light emitted from an electrically driven carbon nanotube embedded within a photonic quantum circuit. Non-classical light generated on chip is recorded under cryogenic conditions with waveguide-integrated superconducting single-photon detectors, without requiring optical filtering. Because exclusively scalable fabrication and deposition methods are used, our results establish carbon nanotubes as promising nanoscale single-photon emitters for hybrid quantum photonic devices.

Diamond offers superior material properties for realizing advanced integrated optical components. Particularly attractive is its wide transparency window which covers visible and infrared wavelengths. To take advantage of the potential for broadband waveguiding, diamond needs to be surrounded by broadband transparent cladding materials. Here we use diamond deposited onto aluminum nitride as a platform for integrated optics. We demonstrate essential photonic circuitry for coupling light into on‐chip waveguides and show transmission both in the near‐infrared and visible wavelength range. Our approach holds promise for functional diamond devices that cover spectral regions up to the mid‐infrared. Left: Scanning electron micrograph of a diamond waveguide with an overlay of the mode profile. Right: material stack consisting of polycrystalline diamond (PCD) on aluminum nitride (AlN) on silicon (Si) (not to scale).