arXiv subjects

Xingyuan Xu

Publications and source records attributed to Xingyuan Xu.

At least 37 records · Page 2Linked to original sources

arXiv2022

Neuromorphic computing using wavelength-division multiplexing

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the potential to dramatically enhance the computing power and energy efficiency of mainstream electronic processors, due to their ultralarge bandwidths of up to 10s of terahertz together with their analog architecture that avoids the need for reading and writing data back and forth. Different multiplexing techniques have been employed to demonstrate ONNs, amongst which wavelength division multiplexing (WDM) techniques make sufficient use of the unique advantages of optics in terms of broad bandwidths. Here, we review recent advances in WDM based ONNs, focusing on methods that use integrated microcombs to implement ONNs. We present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second. The open challenges and limitations of ONNs that need to be addressed for future applications are also discussed.

physics.optics↗

arXiv2022

Phase retrieval of programmable photonic integrated circuits based on an on-chip fractional-delay reference path

Programmable photonic integrated circuits (PICs), offering diverse signal processing functions within a single chip, are promising solutions for applications ranging from optical communications to artificial intelligence. While the scale and complexity of programmable PICs is increasing, the characterization, and thus calibration, of them becomes increasingly challenging. Here we demonstrate a phase retrieval method for programmable PICs using an on-chip fractional-delay reference path. The impulse response of the chip can be uniquely and precisely identified from only the insertion loss using a standard complex Fourier transform. We demonstrate our approach experimentally with a 4-tap finite-impulse-response chip. The results match well with expectations and verifies our approach as effective for individually determining the taps' weights without the need for additional ports and photodiodes.

physics.optics↗

arXiv2022

Phase-encoded RF signal generation based on an integrated 49GHz micro-comb optical source

We demonstrate photonic RF phase encoding based on an integrated micro-comb source. By assembling single-cycle Gaussian pulse replicas using a transversal filtering structure, phase encoded waveforms can be generated by programming the weights of the wavelength channels. This approach eliminates the need for RF signal generators for RF carrier generation or arbitrary waveform generators for phase encoded signal generation. A large number of wavelengths of up to 60 were provided by the microcomb source, yielding a high pulse compression ratio of 30. Reconfigurable phase encoding rates ranging from 2 to 6 Gb/s were achieved by adjusting the length of each phase code. This work demonstrates the significant potentials of this microcomb-based approach to achieve high-speed RF photonic phase encoding with low cost and footprint.

physics.app-ph↗

arXiv2021

Optical Adaptive LMS Equalizer with an Opto-electronic Feedback Loop

We propose a photonic adaptive Least-Mean-Squares equalizer with an opto-electronic feedback loop to determine the updating of an optical Finite-Impulse-Response filter's weights, enabling dispersion compensation introduced by 30-km fiber based on our photonic integrated chip.

eess.SP↗

arXiv2021

Integral order photonic RF and microwave signal processors based on soliton crystal Kerr micro-combs

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.

physics.app-ph↗

arXiv2021

Frequency comb distillation for optical superchannel transmission

Optical frequency combs can potentially provide an efficient light source for multi-terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, it can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this, we demonstrate wideband noise reduction for comb lines using a high-Q microring resonator whose resonances align with the comb lines, providing tight optical filtering of multiple combs lines at the same time. By distilling an optical frequency comb in this way, we are able to reduce the required comb line OSNR when these lines are used in a coherent optical communications system. Through performance tests on a 19.45-GHz-spaced comb generating 71 lines, using 18 Gbaud, 64-QAM sub-channels at a spectral efficiency of 10.6 b/s/Hz, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side. This demonstration provides a method to enable low power optical frequency combs to be able to support high bandwidth and high-capacity communications.

physics.app-ph↗

arXiv2021

Highly tunable broadband RF photonic fractional Hilbert transformer based on a Kerr soliton crystal microcomb source

We demonstrate an RF photonic fractional Hilbert transformer based on an integrated Kerr micro-comb source featuring a record low free spectral range of 48.9 GHz, yielding 75 microcomb lines across the C-band. By programming and shaping the comb lines according to calculated tap weights, we demonstrate that the Hilbert transformer can achieve tunable bandwidths ranging from 1.2 to 15.3 GHz, switchable centre frequencies from baseband to 9.5 GHz, 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 shift. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a powerful way to implement the standard as well as fractional Hilbert transformers with broad and switchable processing bandwidths and centre frequencies, together with high reconfigurability and greatly reduced size and complexity.

physics.app-ph↗

arXiv2021

Photonic single perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical microcombs. This approach is programmable and scalable and is capable of reaching ultrahigh speeds. We demonstrate the basic building block ONNs, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or GigaOPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten digit recognition and cancer cell detection, achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off the shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

eess.SP↗

arXiv2021

Optical neuromorphic processing at Tera-OP/s speeds based on Kerr soliton crystal microcombs

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. 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 TeraOPS (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.

cs.ET↗

arXiv2021

Photonic Microwave and RF Channelizers using Kerr Micro-combs

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection.

physics.app-ph↗

arXiv2021

High bandwidth temporal RF photonic signal processing with Kerr micro-combs: integration, fractional differentiation and Hilbert transforms

Integrated Kerr micro-combs, a powerful source of many wavelengths for photonic RF and microwave signal processing, are particularly useful for transversal filter systems. They have many advantages including a compact footprint, high versatility, large numbers of wavelengths, and wide bandwidths. We review recent progress on photonic RF and microwave high bandwidth temporal signal processing based on Kerr micro-combs with spacings from 49-200GHz. We cover integral and fractional Hilbert transforms, differentiators as well as integrators. The potential of optical micro-combs for RF photonic applications in functionality and ability to realize integrated solutions is also discussed.

physics.app-ph↗

arXiv2021

Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection, achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off the shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

physics.app-ph↗

arXiv2021

Optical data transmission field trial @ 44Tb/s with a 49GHz Kerr soliton crystal microcomb

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.

physics.app-ph↗

arXiv2021

Broadband photonic RF channelizer with a Kerr soliton crystal micro-comb

We report a 92 channel RF channelizer based on a 48.9 GHz integrated micro-comb that operates via soliton crystals, together with a passive high-Q ring resonator that acts as a periodic filter with an optical 3dB bandwidth of 121.4 MHz. We obtain an instant RF bandwidth of 8.08 GHz and 17.55 GHz achieved through temperature tuning. These results represent a major advance to achieving fully integrated photonic RF spectrum channelizers with reduced low complexity, size, and high performance for digital-compatible signal detection and broadband analog signal processing.

physics.app-ph↗

arXiv2020

11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks

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. 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 TeraFLOPS (floating point 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.

cs.NE↗

arXiv2020

High performance integrated polarizers achieved by incorporating 2D layered graphene oxide films

Polarizers and polarization selective resonant cavities (e.g., ring resonators, gratings), are key components for applications to photography, coherent optical detection, polarization-division-multiplexing, optical sensing and liquid crystal displays. We demonstrate waveguide polarizers and polarization discriminating micro-ring resonators (MRRs) by integrating them with 2D graphene oxide (GO) layered thin films. We achieve precise control of the thickness, placement, and size of the films integrated onto photonic devices with a solution based, layer-by-layer transfer-free coating method combined with photolithography and lift-off. This overcomes limitations of layer transfer methods for 2D materials and is a significant advance to manufacturing integrated photonic devices incorporated with 2D materials. We measure the waveguide polarizer for different film thicknesses and lengths versus wavelength, polarization, and power, measuring a high polarization dependent loss (PDL) of ~ 53.8 dB. For GO-coated MRRs, we achieve an extinction ratio difference for TE/TM polarizations of 8.3-dB. We also present measurements of the linear optical properties of 2D layered GO films that yield the material loss anisotropy of the GO films and relative contribution of film loss anisotropy versus polarization-dependent mode overlap. Our results offer interesting physical insights into the transition of the layered GO films from 2D behaviour to quasi bulk like behavior and confirm the high performance of GO based integrated polarization selective devices.

physics.optics↗

arXiv2020

Reconfigurable photonic RF filters based on integrated Kerr frequency comb sources

We demonstrate two categories of photonic radio frequency (RF) filters based on integrated optical micro-combs. The first one is based on the transversal filtering structure and the second one is based on the channelization technique. The large number of wavelengths brought about by the microcomb results in a significantly increased RF spectral resolution and a large instantaneous bandwidth for the RF filters. For the RF transversal filter, we demonstrated Q factor enhancement, improved out-of-band rejection, tunable centre frequency, and reconfigurable filtering shapes. While a high resolution of 117 MHz, a large RF instantaneous bandwidth of 4.64 GHz, and programmable RF transfer functions including binary-coded notch filters and RF equalizing filters with reconfigurable slopes are demonstrated for the RF channelized filter. The microcomb-based approaches feature a potentially much smaller cost and footprint, and is promising for integrated photonic RF filters.

physics.app-ph↗

arXiv2020

Reconfigurable fractional microwave signal processor based on a microcomb

We propose and demonstrate reconfigurable fractional microwave signal processing based on an integrated Kerr optical microcomb. We achieve two forms of microwave signal processing functions, a fractional Hilbert transform as well as a fractional differentiator. For the Hilbert transform we demonstrate a phase shift of 45 degrees, half that of a full Hilbert transform, while for the differentiator we achieve square-root differentiation. For both, we achieve high resolution over a broad bandwidth of 17 GHz with a phase deviation of less than 5 per degree within the achieved passband. This performance in both the frequency and time domains demonstrates the versatility and power of micro-combs as a basis for high performance microwave signal processing.

physics.app-ph↗