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.

With compact footprint, low energy consumption, high scalability, and mass producibility, chip-scale integrated devices are an indispensable part of modern technological change and development. Recent advances intwo-dimensional (2D) layered materials with their unique structures and distinctive properties have motivated their on-chip integration, yielding a variety of functional devices with superior performance and new features. To realize integrated devices incorporating 2D materials, it requires a diverse range of device fabrication techniques, which are of fundamental importance to achieve good performance and high reproducibility. This paper reviews the state-of-art fabrication techniques for the on-chip integration of 2D materials. First, an overview of the material properties and on-chip applications of 2D materials is provided. Second, different approaches used for integrating 2D materials on chips are comprehensively reviewed, which are categorized into material synthesis, on-chip transfer, film patterning, and property tuning / modification. Third, the methods for integrating 2D van der Waals heterostructures are also discussed and summarized. Finally, the current challenges and future perspectives are highlighted.

We theoretically investigate and optimize the performance of four-wave mixing (FWM) in microring resonators (MRRs) integrated with two-dimensional (2D) layered graphene oxide (GO) films. Owing to the interaction between the MRRs and the highly nonlinear GO films as well as to the resonant enhancement effect, the FWM efficiency in GO-coated MRRs can be significantly improved. Based on previous experiments, we perform detailed analysis for the influence of the GO film parameters and MRR coupling strength on the FWM conversion efficiency (CE) of the hybrid MRRs. By optimizing the device parameters to balance the trade-off between the Kerr nonlinearity and loss, we achieve a high CE enhancement of ~18.6 dB relative to the uncoated MRR, which is ~8.3 dB higher than previous experimental results. The influence of photo-thermal changes in the GO films as well as variations in the MRR parameters such as the ring radius and waveguide dispersion on the FWM performance is also discussed. These results highlight the significantly improved FWM performance that can be achieved in MRRs incorporating GO films and provide a guide for optimizing their FWM performance.

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications.

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. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.

Soliton crystal micro-combs are powerful tools as sources of multiple wavelength channels for radio frequency (RF) signal processing. They offer a compact device footprint, large numbers of wavelengths, very high versatility, and wide Nyquist bandwidths. Here, we demonstrate integral order RF signal processing functions based on a soliton crystal micro-comb, including a Hilbert transformer and first- to third-order differentiators. We compare and contrast results achieved and the tradeoffs involved with varying comb spacing, tap design methods, as well as shaping methods.

We propose and theoretically investigate integrated photonic filters based on coupled Sagnac loop reflectors (SLRs) formed by a self-coupled wire waveguide. By tailoring coherent mode interference in the device, three different filter functions are achieved, including Fano-like resonances, wavelength interleaving, and varied resonance mode splitting. For each function, the impact of device structural parameters is analyzed to facilitate optimized performance. Our results theoretically verify the proposed device as a compact multi-functional integrated photonic filter for flexible spectral shaping.

We demonstrate an RF photonic fractional Hilbert transformer based on an integrated Kerr micro-comb source featuring a record low free spectral range of 49 GHz. By programming and shaping the comb lines according to calculated tap weights for up to 39 wavelengths across the C-band, we achieve tunable bandwidths ranging from 1.2 to 15.3 GHz as well as variable center frequencies from baseband to 9.5 GHz, for both standard integral and arbitrary fractional orders. We experimentally characterize the RF amplitude and phase response of the tunable bandpass and lowpass Hilbert transformers with 90 and 45-degree phase shifts. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a powerful way to implement standard and fractional order Hilbert transformers with broad and variable bandwidths and center frequencies, with high reconfigurability and greatly reduced size and complexity.

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.