Photonic Architecture Research Articles

Quantum logical controlled-NOT gate in a lithium niobate-on-insulator photonic quantum walk

The two-qubit controlled-NOT gate is one of the central entangling operations in quantum information technology. The controlled-NOT gate for single photon qubits is normally realized as a network of five individual beamsplitters on six optical modes. Quantum walks (QWs) are an alternative photonic architecture involving arrays of coupled waveguides, which have been successful for investigating condensed matter physics, however, have not yet been applied to quantum logical operations. Here, we engineer the tight-binding Hamiltonian of an array of lithium niobate-on-insulator waveguides to experimentally demonstrate the two-qubit controlled-NOT gate in a QW. We measure the two-qubit transfer matrix with 0.938 ± 0.003 fidelity, and we use the gate to generate entangled qubits with 0.945 ± 0.002 fidelity by preparing the control photon in a superposition state. Our results highlight a new application for QWs that use a compact multi-mode interaction region to realize large multi-component quantum circuits.

Integrate and scale: a source of spectrally separable photon pairs

Integrated photonics is a powerful contender in the race for a fault-tolerant quantum computer, claiming to be a platform capable of scaling to the necessary number of qubits. This necessitates the use of high-quality quantum states, which we create here using an all-around high-performing photon source on an integrated photonics platform. We use a photonic molecule architecture and broadband directional couplers to protect against fabrication tolerances and ensure reliable operation. As a result, we simultaneously measure a spectral purity of 99.1±0.1%, a pair generation rate of 4.4±0.1MHzmW−2, and an intrinsic source heralding efficiency of 94.0±2.9%. We also see a maximum coincidence-to-accidental ratio of 1644±263. We claim over an order of magnitude improvement in the trivariate trade-off among source heralding efficiency, purity, and brightness. Future implementations of the source could achieve in excess of 99% purity and heralding efficiency using the lowest reported propagation losses.

Physical Layer-Aware Digital-Analog Co-Design for Photonic Convolution Neural Network

Photonic convolution neural network (CNN) has shown tremendous capability to provide Tera-level operations/sec (OPS) for streaming object detection with ultra-low energy consumption. However, due to the nature of analog photonic computing which is constrained by accumulated noise from various sources, its physical operating precision is significantly lower than that of electronic artificial intelligence (AI) accelerators powered by tensor processing unit (TPU) and graphic processing unit (GPU). In this paper, we propose a robust digital-analog co-designed photonic CNN using error learning and Kirsch edge enhancement. Experimental results show that, nearly 96.1% recognition accuracy, compared with 34.2% in baseline photonic CNN, can be achieved at global 2-bit precision through physical error learning in dense layer and edge enhancement by photonic Kirsch operator. The proposed photonic CNN architecture is promising to provide 10 Tera-OPS scale computing speed deployed by the state of art PAM-4 optical modules with high energy efficiency.

Integrated Microwave Photonics Coherent Processor for Massive-MIMO Systems in Wireless Communications

Massive-MIMO systems can achieve high capacities and data rates by increasing the number of operational antennas in the base station. As more antenna elements are introduced, the complexity of the signal processing operations increases accordingly and current electronic processors struggle to reach those requirements. To overcome these hurdles, we propose a novel theory to process the RF signals in the optical domain. We experimentally demonstrate the capabilities of theory using a non-integrated Dual Parallel Modulator as a 2×2 optical matrix multiplier, recovering satisfactorily previously mixed RF signals modulated following the standards in 5 G communications. Finally, we propose an integrated photonic architecture based on a core of Mach Zehnder Interferometers to physically process the information. Together with the optical core, we proposed the integration of RF modulators, microwave photonic filters and high-speed photodetectors. We present simulations on the expected performance of the circuit and analyze the impact of fabrication errors.

Multiplexing in photonics as a resource for optical ternary content-addressable memory functionality

Abstract In this paper, we combine a Content-Addressable Memory (CAM) encoding scheme previously proposed for analog electronic CAMs (E-CAMs) with optical multiplexing techniques to create two new photonic CAM architectures—wavelength-division multiplexing (WDM) optical ternary CAM (O-TCAM) and time-division multiplexing (TDM) O-TCAM. As an example, we show how these two O-TCAM schemes can be implemented by performing minor modifications in microring-based silicon photonic (SiPh) circuits originally optimized for exascale interconnects. Here, our SiPh O-TCAM designs include not only the actual search engine, but also the transmitter circuits. For the first time, we experimentally demonstrate O-TCAM functionality in SiPh up to ∼ 4 Gbps ${\sim} 4\,\,\text{Gbps}$ and we prove in simulation feasibility for speeds up to 10 Gbps, 10 times faster than typical E-TCAMs at the expense of higher energy consumption per symbol of our O-TCAM Search Engine circuits than the corresponding E-TCAMs. Finally, we identify which hardware and architecture modifications are required to improve the O-CAM’s energy efficiency towards the level of E-CAMs.

Integrated electro-optic control of biphoton generation using hybrid photonics

Parametric sources in quantum optics usually require some form of spectro-temporal control for conditional generation of high-purity single-photon states, but their properties have not yet been optimized using integrated microchips. Using external short-pulse lasers and separate devices for pump preparation and for photon generation, as is traditional, incurs many impediments such as reduced performance, increased loss, high cost, and limited scalability. To overcome these limitations, here we demonstrate a circuit including high-bandwidth, high-extinction ratio electro-optic modulators for pump-pulse preparation fully integrated with high-quality factor (Q) microresonators for efficient parametric nonlinearity, together with seamlessly interconnecting waveguides. The microchip uses different optical materials on a common platform and a multi-layer integrated photonic architecture. Using this, we control the joint spectrum of room-temperature biphoton generation for the first time on a single integrated microchip, and demonstrate that the theoretical purity bound can be achieved.

Event-driven adaptive optical neural network.

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

Compact chirped fiber Bragg gratings for single-photon generation from quantum dots

A scalable source of single photons is a key constituent of an efficient quantum photonic architecture. To realize this, it is beneficial to have an ensemble of quantum emitters that can be collectively excited with high efficiency. Semiconductor quantum dots hold great potential in this context due to their excellent photophysical properties. Spectral variability of quantum dots is commonly regarded as a drawback introduced by the fabrication method. However, this is beneficial to realize a frequency-multiplexed single-photon platform. Chirped pulse excitation, relying on the so-called adiabatic rapid passage, is the most efficient scheme to excite a quantum dot ensemble due to its immunity to individual quantum dot parameters. Yet, the existing methods of generating chirped laser pulses to excite a quantum emitter are bulky, lossy, and mechanically unstable, which severely hampers the prospects of a quantum dot photon source. Here, we present a compact, robust, and high-efficiency alternative for chirped pulse excitation of solid-state quantum emitters. Our simple plug-and-play module consists of chirped fiber Bragg gratings, fabricated via femtosecond inscription, to provide high values of dispersion in the near-infrared spectral range, where the quantum dots emit. We characterize and benchmark the performance of our method via chirped excitation of a GaAs quantum dot, establishing high-fidelity single-photon generation. Our highly versatile chirping module coupled to a photon source is a significant milestone toward realizing practical quantum photonic devices.

Surpassing the nonlinear conversion efficiency of soliton microcombs

Laser frequency combs are enabling some of the most exciting scientific endeavours in the twenty-first century, ranging from the development of optical clocks to the calibration of the astronomical spectrographs used for discovering Earth-like exoplanets. Dissipative Kerr solitons generated in microresonators currently offer the prospect of attaining frequency combs in miniaturized systems by capitalizing on advances in photonic integration. Most of the applications based on soliton microcombs rely on tuning a continuous-wave laser into a longitudinal mode of a microresonator engineered to display anomalous dispersion. In this configuration, however, nonlinear physics precludes one from attaining dissipative Kerr solitons with high power conversion efficiency, with typical comb powers amounting to ~1% of the available laser power. Here we demonstrate that this fundamental limitation can be overcome by inducing a controllable frequency shift to a selected cavity resonance. Experimentally, we realize this shift using two linearly coupled anomalous-dispersion microresonators, resulting in a coherent dissipative Kerr soliton with a conversion efficiency exceeding 50% and excellent line spacing stability. We describe the soliton dynamics in this configuration and find vastly modified characteristics. By optimizing the microcomb power available on-chip, these results facilitate the practical implementation of a scalable integrated photonic architecture for energy-efficient applications.

Experimental realization of convolution processing in photonic synthetic frequency dimensions.

Convolution is an essential operation in signal and image processing and consumes most of the computing power in convolutional neural networks. Photonic convolution has the promise of addressing computational bottlenecks and outperforming electronic implementations. Performing photonic convolution in the synthetic frequency dimension, which harnesses the dynamics of light in the spectral degrees of freedom for photons, can lead to highly compact devices. Here, we experimentally realize convolution operations in the synthetic frequency dimension. Using a modulated ring resonator, we synthesize arbitrary convolution kernels using a predetermined modulation waveform with high accuracy. We demonstrate the convolution computation between input frequency combs and synthesized kernels. We also introduce the idea of an additive offset to broaden the kinds of kernels that can be implemented experimentally when the modulation strength is limited. Our work demonstrate the use of synthetic frequency dimension to efficiently encode data and implement computation tasks, leading to a compact and scalable photonic computation architecture.

Reduced rank photonic computing accelerator

Use of artificial intelligence for tasks such as image classification and speech recognition has started to form an integral part of our lives. Facilitation of such tasks requires processing a huge amount of data, at times in real time, which has resulted in a computation bottleneck. Photonic cores promise ultra-fast convolutional processing by employing broadband optical links to perform parallelized matrix–vector multiplications (MVMs). Yet the scalability of photonic MVMs is limited by the footprint of the system and energy required for programming the weights, which scale with the matrix dimensionality (M×N). One approach is to reduce the number of hardware matrix weights required, which would allow for less aggressive scaling of the hardware. In this paper, we propose and experimentally demonstrate precisely such a hardware photonic architecture with reduced rank of operation, significantly improving on scalability and decreasing the system complexity. We employ the reduced photonic matrix with reconfigurable optical weights in image processing tasks where we demonstrate the ability to achieve edge detection and classification with 33% reduction in the conventional 3×3 kernel matrix and with no detectable loss of accuracy. While our demonstration is in photonics, this architecture can be universally adapted to MVM engines, and offers the potential for fast, scalable computations at a lower programming cost.

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Low-loss microwave photonic switching for satellite communication.

For traditional switching architecture, packet switching performs fine granularity data packet forwarding, but its digital signal processing (DSP) has high power consumption (PC). All-optical switching provides rapid exchange of wavelength resources, which has coarse granularity. In scenarios where the PC is limited, such as broadband satcom, a switching architecture with lower PC and finer granularity than optical switching would be useful. In this paper, we propose a novel, to the best of our knowledge, low-loss microwave photonic switching architecture that can exchange subband signals across beams and frequency bands. The switching process is realized by exchanging optical carriers instead of payload signals, which does not degrade the signal power, guaranteeing the signal-to-noise ratio (SNR). We conducted a proof-of-concept experiment of 2 × 2 switching with two 1.2-GBaud quadrature phase-shift keying (QPSK) signals; an error vector magnitude (EVM) of or less than 13.87% is realized after forwarding. The proposed system has the advantages of low PC, high SNR, and fine granularity, and is very promising for flexible forwarding in future satcom systems.

Photonic Binary Convolutional Neural Network Based on Microring Resonator Array

We propose the photonic architecture based on microring resonator (MRR) arrays for binary convolutional neural networks (BCNNs) accelerated computing. The MRR crossbar unit is used for computing weight {−1, 1} and the single MRR is for input {0, 1}. The computing parallelism is improved through wavelength division multiplexing. The photonic BCNN achieves 97.29% classification accuracy on the MNIST test set which is only 1.94% lower than the accuracy of the 32-bit neural network, and saves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32\times $ </tex-math></inline-formula> at memory usage. We analyze effects of input and weight encoding errors on the photonic BCNN. When the input or weight error rate is less than 0.01%, the test accuracy remains unchanged. We evaluate the performance of the photonic BCNN architecture considering optical loss, inter-channel crosstalk, operation frequency and device power consumption. The energy efficiency of the designed photonic BCNN architecture is 1.72 pJ/MAC, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.80\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$61.32\times $ </tex-math></inline-formula> better than the 8-bit and 16-bit architecture respectively. The photonic BCNN is promising to be used for edge computing.

Multimode squeezing in soliton crystal microcombs

Soliton microcombs are self-organized pulses of light sustained in driven Kerr microresonators, intensively studied for applications in integrated photonic technologies and for their rich nonlinear dynamics. In this work, we theoretically study the collective dynamics of the quantum fluctuations of soliton microcombs. We find that the mean field of a dissipative Kerr soliton crystal is accompanied by pulses of squeezed multimode vacuum and derives its operational stability from the strong detuning of the below-threshold parametric process. We present a photonic architecture that enables independent control of the above- and below-threshold states and achieves a high degree of squeezing (&gt;15dB) in the output waveguide with realistic losses. Our work elucidates the quantum dynamics of formation and annihilation in dissipative Kerr soliton systems, and establishes a pathway for the realization of a practical integrated source of multimode squeezed light.

Enhanced Light Scattering Using a Two-Dimensional Quasicrystal-Decorated 3D-Printed Nature-Inspired Bio-photonic Architecture

A number of strategies have been exploited so far to trap photons inside living cells to obtain high-contrast imaging. Also, launching light inside biological materials is technically challenging. Using photon confinement in a three-dimensional (3D)-printed biomimetic architecture in the presence of a localized surface plasmon resonance (LSPR) promoter can overcome some of these issues. This work compares optical confinement in natural and 3D-printed photonic architectures, namely, fish scale, in the presence of atomically thin Al70Co10Fe5Ni10Cu5 quasicrystals (QCs). Due to their wideband LSPR response, the QCs work as photon scattering hotspots. The architecture acts as an additive source of excitation for the two-dimensional (2D) QCs via total internal reflection (TIR). The computational analysis describes the surface plasmon-based scattering property of 2D QCs. The 3D-printed fish scale’s image contrast with the 2D Al70Co10Fe5Ni10Cu5 QC has been compared with other 2D materials (graphene, h-BN, and MoS2) and outperforms them. The present study conceptually presents a new approach for obtaining high-quality imaging of biological imaging, even using high-energy photons.

Bioinspired Photonic Microchip with Molecularly Imprinted Polymer for Single Recognition of c-Myc Protein in Predictive Medical Diagnostics.

Monitoring of trace c-Myc protein as the biomarker of ubiquitous cancers is critical in achieving predictive medical diagnostics. However, qualitative and quantitative detection of c-Myc protein with superior single selectivity and sensitivity is still challenging. Herein, a bioinspired photonic sensing microchip for single recognition of c-Myc protein is outlined with two synergistic aspects involving chemical and physical design criteria. Chemical design uses specific molecularly imprinted polymer (MIP) with exquisite complementarity in its chemical functions and spatially geometries to targeted c-Myc protein, leading to excellent sensitivity and selectivity for single identification. Physical design involves optical geometrical double-reflection polarization rotation and multilayer interference of the fabricated periodic photonic architecture inspired by Papilio palinurus butterfly wings to enhance the spectral diversity of reflectance. Therefore, a one-of-a-kind sensing platform integrated the advantages of MIP and bioinspired photonic structure is demonstrated to actualize distinctive signal conversion and amplification for qualitative and quantitative detection of trace c-Myc protein, accompanied with superior sensitivity (detection limit is 0.014μg mL-1 ), selectivity, stability, anti-interference ability as well as rapid response/recovery time. This sensor microchip uniquely ventures into the territory of functionally combining bioinspired photonic structure with MIP absorbers, proven promising for prevention or diagnosis of cancers in medical field. This article is protected by copyright. All rights reserved.

A Coherent Photonic Crossbar for Scalable Universal Linear Optics

We demonstrate a novel interferometric coherent photonic crossbar architecture (Xbar) that can realize any tensor operator and allows for total loss-induced fidelity restoration, offering at the same time significant dimension scalability credentials compared to respective state-of-the-art solutions. The proposed Xbar layout demarcates from the prevalent photonic schemes that rely on the implementation of intended matrices factorized via the Singular Value Decomposition (SVD); instead, it harnesses the power of interference within a novel design where each matrix element can be mapped by one-to-one correspondence to a single, designated Xbar node, bringing down the number of programming steps to only one. In this paper, we present the theoretical foundations of the Xbar, proving that its insertion losses do not scale with the node losses as opposed to the exponential scaling witnessed by the SVD based counterparts. This leads to a matrix design with significantly lower overall insertion losses and improved scalability potential compared to SVD-based schemes, allowing for the employment of alternative node technologies with lower energy consumption and higher operational speed credentials. Finally, we theoretically validate that the proposed Xbar architecture is the first linear operator that supports fidelity restoration, outperforming SVD schemes in loss- and phase-error fidelity performance.

Coupling of Photon Emitters in Monolayer WS2 with a Photonic Waveguide Based on Bound States in the Continuum

On-chip light sources are an essential component of scalable photonic integrated circuits (PICs), and coupling between light sources and waveguides has attracted a great deal of attention. Photonic waveguides based on bound states in the continuum (BICs) allow optical confinement in a low-refractive-index waveguide on a high-refractive-index substrate and thus can be employed for constructing PICs. In this work, we experimentally demonstrated that the photoluminescence (PL) from a monolayer of tungsten sulfide (WS2) could be coupled into a BIC waveguide on a lithium-niobate-on-insulator (LNOI) substrate. Using finite-difference time-domain simulations, we numerically obtained a coupling efficiency of ∼2.3% for an in-plane-oriented dipole and a near-zero loss at a wavelength of 620 nm. By breaking through the limits of 2D-material integration with conventional photonic architectures, our work offers a new perspective for light-matter coupling in monolithic PICs.

Minimum complexity integrated photonic architecture for delay-based reservoir computing.

Reservoir computing is an analog bio-inspired computation scheme for efficiently processing time-dependent signals, the photonic implementations of which promise a combination of massive parallel information processing, low power consumption, and high-speed operation. However, most of these implementations, especially for the case of time-delay reservoir computing, require extensive multi-dimensional parameter optimization to find the optimal combination of parameters for a given task. We propose a novel, largely passive integrated photonic TDRC scheme based on an asymmetric Mach-Zehnder interferometer in a self-feedback configuration, where the nonlinearity is provided by the photodetector, and with only one tunable parameter in the form of a phase shifting element that, as a result of our configuration, allows also to tune the feedback strength, consequently tuning the memory capacity in a lossless manner. Through numerical simulations, we show that the proposed scheme achieves good performance -when compared to other integrated photonic architectures- on the temporal bitwise XOR task and various time series prediction tasks, while greatly reducing hardware and operational complexity.