Artificial neural networks (ANNs) are able to distill the hierarchical aspects of raw data. They are central to machine learning functions including speech and pattern recognition, medical diagnosis, playing board games, computer vision, and many other areas [1-7]. Optical neural networks (ONNs) in particular can significantly increase the computing speed of ANNs in order to overcome the intrinsic bandwidth bottleneck of electronics. Convolutional neural networks (CNNs), are inspired by biological systems such as the visual cortex, and are a powerful approach to greatly reduce the parametric network complexity in order to enhance the accuracy of the predictions of the system. In this paper, we demonstrate a universal optical convolutional accelerator that can be used in conjunction with both electronic and optical neural networks. It operates beyond 10 Tera-OPS (TOPS - operations per second) and produces convolutions of extremely large scale images of 250,000 pixels in size with a resolution of 8-bits. It generates 10 convolutions simultaneously in parallel, with 10 different kernels. This processing simultaneously — enough for facial image recognition. After demonstrating this, we then use the exact hardware to form a convolutional neural network consisting of a convolutional front-end followed by a deep optical neural network fully connected layer, together forming a CNN with ten neurons at the output. We successfully perform the recognition of all 10 hand written digits, each consisting of 900 pixel handwritten digit images. We achieve an accuracy of 88% which is very close to the theoretical accuracy of 90%. We use an approach that exploits the simultaneous multiplexing, or interleaving, within the time, space and wavelength dimensions, using an optical frequency comb supplied by an integrated Kerr micro-comb source. We compare the performance of different optical neural networks, explicitly showing that our approach is intrinsically scalable in both size and speed, up to the PetaOPs per second (POPs) regime in speed and to well over 24,000 synapses in size. We perform theoretical evaluation of the scaled system performance and show that it is trainable to much more complex networks for real-world demanding applications including real-time video recognition and autonomous unmanned vehicle control.

We experimentally demonstrate enhanced spectral broadening of femtosecond optical pulses after propagation through silicon-on-insulator (SOI) nanowire waveguides integrated with two-dimensional (2D) graphene oxide (GO) films. Owing to the strong mode overlap between the SOI nanowires and the GO films with a high Kerr nonlinearity, the self-phase modulation (SPM) process in the hybrid waveguides is significantly enhanced, resulting in greatly improved spectral broadening of the femtosecond optical pulses. A solution-based, transfer-free coating method is used to integrate GO films onto the SOI nanowires with precise control of the film thickness. Detailed SPM measurements using femtosecond optical pulses are carried out, achieving a broadening factor of up to ~4.3 for a device with 0.4-mm-long, 2 layers of GO. By fitting the experimental results with theory, we obtain an improvement in the waveguide nonlinear parameter by a factor of ~3.5 and the effective nonlinear figure of merit (FOM) by a factor of ~3.8, relative to the uncoated waveguide. Finally, we discuss the influence of GO film length on the spectral broadening and compare the nonlinear optical performance of different integrated waveguides coated with GO films. These results confirm the improved nonlinear optical performance for silicon devices integrated with 2D GO films.

Digital signal processing has become central to many fields, from coherent optical telecommunications where it is used to compensate signal impairments, to video image processing. Image processing in particular is important for observational astronomy, medical diagnosis, autonomous driving, big data and particularly artificial intelligence. Digital signal processing is mainly performed electronically, but new applications, particularly those involving real time video image processing, are creating unprecedented demand for ultrahigh performance, including bandwidth and reduced energy consumption. Here, we demonstrate a photonic digital signal processor operating at 18 Terabits/s and use it to process multiple simultaneous video signals in real-time. The system processes 400,000 video signals concurrently, performing 34 functions simultaneously that are key to object edge detection, edge enhancement and motion blur. As compared with spatial-light devices used for image processing, our system is not only ultra-high speed but highly reconfigurable and programable, able to perform many different functions without any change to the physical hardware. Our approach, based on an integrated Kerr soliton crystal microcomb, opens up new avenues for ultrafast robotic vision and machine learning.

As one of the representatives of emerging metallic transition metal dichalcogenides, niobium ditelluride (NbTe2) has attracted intensive interest recently due to its distorted lattice structure and unique physical properties. Here, we report on the ultrafast carrier dynamics in NbTe2 measured using time-resolved pump-probe transient reflection spectroscopy. A thickness-dependent carrier relaxation time is observed, exhibiting a clear increase in the fast and slow carrier decay rates for thin NbTe2 flakes. In addition, pump power dependent measurements indicate that the carrier relaxation rates are power-independent, with the peak amplitude of the transient reflectivity increasing linearly with pump power. Isotropic relaxation dynamics in NbTe2 is also verified by performing polarization-resolved pump-probe measurements. These results provide an insight into the light-matter interactions and charge carrier dynamics in NbTe2 and will pave the way for its applications to photonic and optoelectronic devices.

We experimentally demonstrate enhanced self-phase modulation (SPM) in silicon nitride (Si3N4) waveguides integrated with 2D graphene oxide (GO) films. GO films are integrated onto Si3N4 waveguides using a solution-based, transfer-free coating method that enables precise control of the film thickness. Detailed SPM measurements are carried out using both picosecond and femtosecond optical pulses. Owing to the high Kerr nonlinearity of GO, the hybrid waveguides show significantly improved spectral broadening compared to the uncoated waveguide, achieving a broadening factor of up to ~3.4 for a device with 2 layers of GO. By fitting the experimental results with theory, we obtain an improvement in the waveguide nonlinear parameter by a factor of up to 18.4 and a Kerr coefficient (n2) of GO that is about 5 orders of magnitude higher than Si3N4. Finally, we provide a theoretical analysis for the influence of GO film length, coating position, and its saturable absorption on the SPM performance. These results verify the effectiveness of on-chip integrating 2D GO films to enhance the nonlinear optical performance of Si3N4 devices.

Enhanced supercontinuum generation (SCG) is experimentally demonstrated in integrated silicon nitride (Si3N4) waveguides incorporating highly nonlinear graphene oxide (GO) in the form of two-dimensional (2D) films. On-chip integration of the 2D GO films with precise control of their thickness is realized by using a transfer-free and layer-by-layer coating method. The control of the film length and coating position is achieved via window opening in the upper silica cladding of the photonic integrated chips. Detailed SCG measurements are performed using the fabricated devices with different waveguide geometries and GO film thicknesses, and the results are compared with devices without GO. Significantly improved spectral broadening of ultrashort optical pulses with ultrahigh peaks powers exceeding 1000 W is observed for the hybrid devices, achieving up to 2.4 times improvement in the spectral bandwidth relative to devices without GO. Theoretical analyses for the influence of GO film thickness, coating length, coating position, and waveguide geometry are also provided by fitting the experimental results with theory, showing that there is still significant room for further improvement. This work opens up a promising new avenue towards improving the SCG performance of photonic integrated devices by incorporating functional 2D materials.

We demonstrate a radio frequency (RF) phase-encoded signal generator as well as a user-defined RF arbitrary waveform generator (AWG) based on a soliton crystal micro-comb generated by an integrated MRR with a free spectral range of ~49 GHz. Owing to the soliton crystal’s robust and stable generation as well as the high intrinsic efficiency, RF phase-encoded signal generators and AWGs with simple operation and fast reconfiguration are realized. The soliton crystal micro-comb provides 60 wavelengths for RF phase-encoded signal generators, achieving a phase encoding speed of 5.95 Gb/s and a high pulse compression ratio of 29.6. Over 80 wavelengths are employed for the AWGs, achieving tunable square waveforms with a duty cycle ratio ranging from 10% to 90%, sawtooth waveforms with tunable slope ratios from 0.2 to 1, and symmetric concave quadratic chirp waveforms. Our system has great potential to achieve RF and microwave photonic signal generation and processing with low cost and footprint.

Integrated photonic devices operating via optical nonlinearities offer a powerful solution for all-optical information processing, yielding processing speeds that are well beyond that of electronic processing as well as providing the added benefits of compact footprint, high stability, high scalability, and small power consumption. The increasing demand for high-performance nonlinear integrated photonic devices has facilitated the hybrid integration of novel materials to address the limitations of existing integrated photonic platforms, such as strong nonlinear optical absorption or an inadequate optical nonlinearity. Recently, graphene oxide (GO), with its large optical nonlinearity, high flexibility in altering its properties, and facile fabrication processes, has attracted significant attention, enabling many hybrid nonlinear integrated photonic devices with improved performance and novel capabilities. This paper reviews the applications of GO to nonlinear integrated photonics. First, an overview of GO’s optical properties and the fabrication technologies needed for its on-chip integration is provided. Next, the state-of-the-art GO nonlinear integrated photonic devices are reviewed, together with comparisons of the nonlinear optical performance of different integrated platforms incorporating GO. Finally, the challenges and perspectives of this field are discussed.

We experimentally demonstrate bandwidth-tunable RF photonic Hilbert transformer based on an integrated Kerr micro-comb source. The micro-comb is generated by an integrated micro-ring resonator with a free spectral range of 48.9 GHz, yielding 75 micro-comb lines in the telecom C-band. By programming and shaping the generated comb lines according to calculated tap weights, we demonstrate high-speed Hilbert transform functions with tunable bandwidths ranging from 1.2 GHz to 15.3 GHz, switchable center frequencies from baseband to 9.5 GHz, and arbitrary fractional orders. The experimental results show good agreement with theory and confirm the effectiveness of our approach.

We propose and theoretically investigate integrated photonic filters based on two coupled Sagnac loop reflectors (SLRs) formed by a self-coupled optical waveguide. Recently we investigated integrated photonic filters based on cascaded SLRs and coupled SLRs. Here, we advance this field by presenting a unique approach of using coupled SLRs formed by a self-coupled optical waveguide. This enables us to achieve high performance filter functions including Fano-like resonances and wavelength interleaving with a simpler design and a higher fabrication tolerance by tailoring coherent mode interference in the device. Our design takes into account the device fabrication issues as well as the requirements for practical applications. As a guide for practical device fabrication, an analysis of the impact of the structural parameters and fabrication tolerance on each filter function is also provided. The Fano-like resonances show a low insertion loss (IL) of 1.1 dB, a high extinction ratio of 30.2 dB, and a high slope rate (SR) of 747.64 dB/nm. The combination of low IL and high SR promises this device for Fano resonance applications. Our device also can achieve wavelength de-interleaving function with high fabrication tolerance which is attractive for optical interleavers that need a flat-top symmetric filter shape. Optical interleavers and de-interleavers are core elements for signal multiplexing and demultiplexing in wavelength division multiplexing optical communication systems. Versatile spectral responses with a simple design, compact device footprint, and high fabrication tolerance make this approach highly promising for flexible response shaping in a wide variety of applications.

We report enhanced nonlinear optics in nanowires, waveguides, and ring resonators by introducing layered two-dimensional (2D) graphene oxide (GO) films through experimental demonstration. The GO films are integrated on silicon-on-insulator nanowires (SOI), high index doped silica glass, and silicon nitride (SiN) waveguides and microring resonators (MRRs), to demonstrate an improved optical nonlinearity including Kerr nonlinearity and four-wave mixing (FWM). By using a large-area, transfer-free, layer-by-layer GO coating method with photolithography and lift-off processes, we integrate GO films on these complementary metal-oxide-semiconductor (CMOS)-compatible devices. For SOI nanowires, significant spectral broadening of optical pulses in GO-coated SOI nanowires induced by self-phase modulation (SPM) is observed, achieving a high spectral broadening factor of 4.34 for a device with a patterned film including 10 layers of GO. A significant enhancement in the nonlinear figure of merit (FOM) for silicon nanowires by a factor of 20 is also achieved, resulting in a FOM > 5. For Hydex and SiN waveguides, enhanced FWM in the GO-coated waveguides is achieved, where conversion efficiency (CE) enhancements of up to 6.9 dB and 9.1 dB relative to the uncoated waveguides. For MRRs, an increase of up to ~10.3 dB in the FWM CE is achieved due to the resonant enhancement effect. These results reveal the strong potential of GO films to improve the nonlinear optics of nanowires, waveguides, and ring resonators.

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. We show that this approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.