A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency.

Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates.

Real-time photonic blind interference cancellation.

mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration.

Fully-integrated photonic tensor core for image convolutions.

Convolutions are one of the most critical signal and image processing operations. From spectral analysis to computer vision, convolutional filtering is often related to spatial information processing involving neighbourhood operations. As convolution operations are based around the product of two functions, vectors or matrices, dot products play a key role in the performance of such operations; for example, advanced image processing techniques require fast, dense matrix multiplications that typically take more than 90% of the computational capacity dedicated to solving convolutional neural networks.

Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation.

The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability.

Sub-Nyquist optical pulse sampling for photonic blind source separation.

We propose and experimentally demonstrate an optical pulse sampling method for photonic blind source separation. The photonic system processes and separates wideband signals based on the statistical information of the mixed signals, and thus the sampling frequency can be orders of magnitude lower than the bandwidth of the signals. The ultra-fast optical pulses collect samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals.

Design automation of photonic resonator weights.

Neuromorphic photonic processors based on resonator weight banks are an emerging candidate technology for enabling modern artificial intelligence (AI) in high speed analog systems. These purpose-built analog devices implement vector multiplications with the physics of resonator devices, offering efficiency, latency, and throughput advantages over equivalent electronic circuits. Along with these advantages, however, often come the difficult challenges of compensation for fabrication variations and environmental disturbances.

Blind source separation with integrated photonics and reduced dimensional statistics.

Microwave communications have witnessed an incipient proliferation of multi-antenna and opportunistic technologies in the wake of an ever-growing demand for spectrum resources, while facing increasingly difficult network management over widespread channel interference and heterogeneous wireless broadcasting. Radio frequency (RF) blind source separation (BSS) is a powerful technique for demixing mixtures of unknown signals with minimal assumptions, but relies on frequency dependent RF electronics and prior knowledge of the target frequency band. We propose photonic BSS with unparalleled frequency agility supported by the tremendous bandwidths of photonic channels and devices.

Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics.

We experimentally demonstrate all-optical reconfigurable nonlinear activation functions in a cavity-loaded Mach-Zehnder interferometer device on a silicon photonics platform, via the free-carrier dispersion effect. Our device is programmable to generate various nonlinear activation functions, including sigmoid, radial-basis, clamped rectified linear unit, and softplus, with tunable thresholds. We simulate benchmark tasks such as XOR and MNIST handwritten digit classifications with experimentally measured activation functions and obtain accuracies of 100% and 94%, respectively.

Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing.

Lateral bipolar junction transistor on a silicon photonics platform.

Integration of active electronics into photonic systems is necessary for large-scale photonic integration. While heterogeneous integration leverages high-performance electronics, a monolithic scheme can coexist by aiding the electronic processing, improving overall efficiency. We report a lateral bipolar junction transistor on a commercial silicon photonics foundry process.

High-speed all-optical thresholding via carrier lifetime tunability.

We theoretically study the effect of free-carrier lifetime on processing speed and strength of nonlinearity, pertaining to our all-optical thresholder. We find that optimal device performance necessitates tuning lifetime while optimizing for both speed and nonlinearity. We also experimentally demonstrate device processing speed improvement from 400 Mbps to 2.

Photonic independent component analysis using an on-chip microring weight bank.

Independent component analysis (ICA) is a general-purpose technique for analyzing multi-dimensional data to reveal the underlying hidden factors that are maximally independent from each other. We report the first photonic ICA on mixtures of unknown signals by employing an on-chip microring (MRR) weight bank. The MRR weight bank performs so-called weighted addition (i.

Accelerated secure key distribution based on localized and asymmetric fiber interferometers.

We propose and experimentally demonstrate an approach to generate and distribute secret keys over optical fiber communication infrastructure. Mach-Zehnder interferometers (MZIs) are adopted for key generation by transferring the environmental noise to random optical signals. A novel combination of wideband optical noise and an asymmetric MZI structure enables the secret keys to be securely transmitted and exchanged over public fiber links without being detected.

Photonic principal component analysis using an on-chip microring weight bank.

Photonic principal component analysis (PCA) enables high-performance dimensionality reduction in wideband analog systems. In this paper, we report a photonic PCA approach using an on-chip microring (MRR) weight bank to perform weighted addition operations on correlated wavelength-division multiplexed (WDM) inputs. We are able to configure the MRR weight bank with record-high accuracy and precision, and generate multi-channel correlated input signals in a controllable manner.

Neuromorphic photonics with electro-absorption modulators.

Photonic neural networks benefit from both the high-channel capacity and the wave nature of light acting as an effective weighting mechanism through linear optics. Incorporating a nonlinear activation function by using active integrated photonic components allows neural networks with multiple layers to be built monolithically, eliminating the need for energy and latency costs due to external conversion. Interferometer-based modulators, while popular in communications, have been shown to require more area than absorption-based modulators, resulting in a reduced neural network density.

Feedback control for microring weight banks.

Microring weight banks present novel opportunities for reconfigurable, high-performance analog signal processing in photonics. Controlling microring filter response is a challenge due to fabrication variations and thermal sensitivity. Prior work showed continuous weight control of multiple wavelength-division multiplexed signals in a bank of microrings based on calibration and feedforward control.

Simultaneous excitatory and inhibitory dynamics in an excitable laser.

Neocortical systems encode information in electrochemical spike timings, not just mean firing rates. Learning and memory in networks of spiking neurons is achieved by the precise timing of action potentials that induces synaptic strengthening (with excitation) or weakening (with inhibition). Inhibition should be incorporated into brain-inspired spike processing in the optical domain to enhance its information-processing capability.

Neuromorphic photonic networks using silicon photonic weight banks.

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks.

Passively synchronized Q-switched and mode-locked dual-band Tm:ZBLAN fiber lasers using a common graphene saturable absorber.

Dual-band fiber lasers are emerging as a promising technology to penetrate new industrial and medical applications from their dual-band properties, in addition to providing compactness and environmental robustness from the waveguide structure. Here, we demonstrate the use of a common graphene saturable absorber and a single gain medium (Tm:ZBLAN fiber) to implement (1) a dual-band fiber ring laser with synchronized Q-switched pulses at wavelengths of 1480 nm and 1840 nm, and (2) a dual-band fiber linear laser with synchronized mode-locked pulses at wavelengths of 1480 nm and 1845 nm. Q-switched operation at 1480 nm and 1840 nm is achieved with a synchronized repetition rate from 20 kHz to 40.

Multi-channel control for microring weight banks.

We demonstrate 4-channel, 2GHz weighted addition in a silicon microring filter bank. Accurate analog weight control becomes more difficult with increasing number of channels, N, as feedback approaches become impractical and brute force feedforward approaches take O(2N) calibration measurements in the presence of inter-channel dependence. We introduce model-based calibration techniques for thermal cross-talk and cross-gain saturation, which result in a scalable O(N) calibration routine and 3.

Spike processing with a graphene excitable laser.

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing.

Gigabit Ethernet signal transmission using asynchronous optical code division multiple access.

We propose and experimentally demonstrate a novel architecture for interfacing and transmitting a Gigabit Ethernet (GbE) signal using asynchronous incoherent optical code division multiple access (OCDMA). This is the first such asynchronous incoherent OCDMA system carrying GbE data being demonstrated to be working among multi-users where each user is operating with an independent clock/data rate and is granted random access to the network. Three major components, the GbE interface, the OCDMA transmitter, and the OCDMA receiver are discussed in detail.

Excitable laser processing network node in hybrid silicon: analysis and simulation.

The combination of ultrafast laser dynamics and dense on-chip multiwavelength networking could potentially address new domains of real-time signal processing that require both speed and complexity. We present a physically realistic optoelectronic simulation model of a circuit for dynamical laser neural networks and verify its behavior. We describe the physics, dynamics, and parasitics of one network node, which includes a bank of filters, a photodetector, and excitable laser.

Demonstration of WDM weighted addition for principal component analysis.

We consider an optical technique for performing tunable weighted addition using wavelength-division multiplexed (WDM) inputs, the enabling function of a recently proposed photonic spike processing architecture [J. Lightwave Technol., 32 (2014)].