Photonic Architecture Research Articles

Electrical excitation and charge-state conversion of silicon vacancy color centers in single-crystal diamond membranes

The silicon-vacancy (SiV) color center in diamond has recently emerged as a promising qubit for quantum photonics. However, the electrical control and excitation of the SiV centers are challenging due to the low density of free carriers in doped diamond. Here, we realize electrical excitation of SiV centers in a single-crystal diamond membrane, which is promising for scalable photonic architectures. We further demonstrate electrical switching of the charge states of the SiV centers by applying a forward bias voltage to the fabricated diamond-membrane devices and identify the position of the SiV−/SiV0 charge transition level. Our findings provide a perspective toward electrical triggering of color centers in diamond and accelerate the development of scalable quantum nanophotonic technologies.

Photonic crystal light trapping: Beyond 30% conversion efficiency for silicon photovoltaics

The power conversion efficiency of single-junction silicon solar cells has increased only by 1.5% despite extensive efforts over the past two decades. The current world-record efficiencies of silicon solar cells, within the 25%–26.7% range, fall well below the thermodynamic limit of 32.3%. We review the recent progress in photonic crystal light-trapping architectures poised to achieve 28%–31% conversion efficiency in flexible 3–20 μm-thick, single-junction crystalline-silicon solar cells. These photonic crystals utilize wave-interference based light-trapping, enabling solar absorption well beyond the Lambertian limit in the 300–1200 nm wavelength range. Using experimentally feasible doping profiles, carrier lifetimes, surface recombination velocities, and established Auger recombination losses, we review considerations leading to the prediction of 31% efficiency in a 15 μm-thick silicon photonic crystal cell with interdigitated back-contacts. This is beyond the conversion efficiency of any single-material photovoltaic device of any thickness.

Metalloporphyrins into mesoporous photonic crystals: towards molecularly-tuned photonic sensing devices

Metalloporphyrins are molecules capable of optically sensing targets due to their specific reactivity, and mesoporous photonic architectures can act as environment-responsive materials. In this work, we used a metalloporphyrin, MnIII meso-tetra(N-methyl-4-pyridyl) porphyrin (MP), to add a recognition functionality to a mesoporous oxide photonic crystal (MOPC) in order to develop a versatile and portable sensing device for volatile amines exploiting both the open MOPC pore network and the high affinity of volatile amino analytes to the MP. The hybrid material MP@MOPC was prepared by immersion of the MOPC, synthesized from mesoporous silica and titania layers, in an MP solution. The system was characterized by UV–vis absorption and FTIR spectroscopies, and SEM microscopy. Sensing of small molecules was tested using ethylenediamine (EDA) as a volatile amine model, showing an absorbance increase in the MOPC spectral window with promising analytical parameters. The hybrid material presents enhanced sensing capabilities compared to the MP or MOPC components alone. A prototype device was built, aiming for a future design of simple, low-cost equipment, with a light emitting diode and a light-dependent resistor, which includes the MP@MOPC sensor.

Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on Chip

We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.

Near infrared organic photodetectors based on enhanced charge transfer state absorption by photonic architectures

Solution processed organic photodetectors with a nanostructured active layer in the shape of a photonic crystal exhibit an improved NIR response, below the band gap of the active layer materials, that can be tuned by varying the lattice parameter.

Large-Scale Spatiotemporal Photonic Reservoir Computer for Image Classification

We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits from the MNIST database. Our experiment exploits off-the-shelf optical and electronic components to currently achieve a network size of 16,384 nodes. Both network types are designed within the reservoir computing paradigm with randomly weighted input and hidden layers. Using various feature extraction techniques (e.g., histograms of oriented gradients, zoning, and Gabor filters) and a simple training procedure consisting of linear regression and winner-takes-all decision strategy, we demonstrate numerically and experimentally that a feedforward network allows for classification error rate of 1%, which is at the state-of-the-art for experimental implementations and remains competitive with more advanced algorithmic approaches. We also investigate recurrent networks in numerical simulations by explicitly activating the temporal dynamics, and predict a performance improvement over the feedforward configuration.

Ultra-wideband signal acquisition by use of channel-interleaved photonic analog-to-digital converter under the assistance of dilated fully convolutional network

We demonstrate a photonic architecture to enable the separation of ultra-wideband signals. The architecture consists of a channel-interleaved photonic analog-to-digital converter (PADC) and a dilated fully convolutional network (DFCN). The aim of the PADC is to perform ultra-wideband signal acquisition, which introduces the mixing of signals between different frequency bands. To alleviate the interference among wideband signals, the DFCN is applied to reconstruct the waveform of the target signal from the ultra-wideband mixed signals in the time domain. The channel-interleaved PADC provides a wide spectrum reception capability. Relying on the DFCN reconstruction algorithm, the ultra-wideband signals, which are originally mixed up, are effectively separated. Additionally, experimental results show that the DFCN reconstruction algorithm improves the average bit error rate by nearly three orders of magnitude compared with that without the algorithm.

Silicon‐Based Photonic Architectures from Hierarchically Porous Carbon Opals

AbstractSilicon‐based materials are needed in cutting‐edge technological fields, for which hierarchical porosity and photonic properties help improve performance. In this work, the versatility of several fabrication routes that combine silicon infiltration by chemical vapor deposition (CVD), reactive ion etching (RIE), and carbon calcination, which produce a palette of novel silicon‐based material architectures, is demonstrated. Design strategies are discussed and the main features of the processing steps are addressed to provide a variety of new silicon‐based materials with high morphological versatility and photonic quality. Three‐dimensional hollow carbon opals (HCO) permit control of the porosity of subsequent architectures through the initial silicon infiltration, while open or closed surfaces are achieved primarily as a function of RIE conditions. The composition of derived structures depends on the sequence in which the processes are applied. As a result, pure Si and hybrid C–Si inverse structures can be produced with open or closed spheres in the top layer and with an optional passivation layer. Remarkably, by properly choosing the Si infiltration parameters and the calcination/RIE procedure, final structures are achieved with double‐shell spheres.

Human action recognition with a large-scale brain-inspired photonic computer

The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e.g. brain-computer interface and surveillance. Deep learning has shown remarkable results recently, but can be found hard to use in practice, as its training requires large datasets and special purpose, energy-consuming hardware. In this work, we propose a scalable photonic neuro-inspired architecture based on the reservoir computing paradigm, capable of recognising video-based human actions with state-of-the-art accuracy. Our experimental optical setup comprises off-the-shelf components, and implements a large parallel recurrent neural network that is easy to train and can be scaled up to hundreds of thousands of nodes. This work paves the way towards simply reconfigurable and energy-efficient photonic information processing systems for real-time video processing.

Photonic integrated circuits with bound states in the continuum

Waves that are perfectly confined in the continuous spectrum of radiating waves without interaction with them are known as bound states in the continuum (BICs). Despite recent discoveries of BICs in nanophotonics, full routing and control of BICs have not yet been explored. Here, we experimentally demonstrate BICs in a fundamentally new photonic architecture by patterning a low-refractive-index material on a high-refractive-index substrate, where dissipation to the substrate continuum is eliminated by engineering the geometric parameters. Pivotal BIC-based photonic components are demonstrated, including waveguides, microcavities, directional couplers, and modulators. Therefore, this work presents the critical step of photonic integrated circuits with BICs, and enables the exploration of new single-crystal materials on an integrated photonic platform without the fabrication challenges of patterning the single-crystal materials. The demonstrated lithium niobate platform will facilitate development of functional photonic integrated circuits for optical communications, nonlinear optics at the single photon level, as well as scalable photonic quantum information processors.

Silver ants-inspired flexible photonic architectures with improved transparency and heat radiation for photovoltaic devices

Silver ants forage at extremely high environmental temperatures, the hairs of which play a crucial role in reducing the body temperature via enhanced optical reflection and radiative heat dissipation as a passive daytime radiative cooler. Inspired by the hierarchical feature structure of the hairs, we fabricated the flexible photonic architectures (FPA) on polydimethylsiloxane (PDMS). With some modification to the feature structure, the FPA-PDMS exhibited not only a large emittance in the mid-infrared spectral range, comparable with that of state-of-the-art thermal emitters, but also the record-high transmittance and haze in the visible and near-infrared range, which would be particularly appealing to photovoltaic devices for maximizing sunlight absorption and minimizing parasitic heat. The ease of application of the FPA-PDMS was demonstrated in both emerging perovskite and mature commercial crystalline silicon solar cells, where the FPA-PDMS was electrostatically attached to their glass superstrates or covers and the improvements in photovoltaic characteristics as a result of enhanced light harvesting were confirmed through laboratory measurements. Though the synergetic effects of the improved transparency and heat radiation brought by the FPA-PDMS need to be characterized outdoors in the future, these findings pave the way for suppressing optical and thermal losses simultaneously in opto-electric-thermal coupled devices through learning sophistically evolved photonic architectures from nature.

Multi-spectral gate-triggered heterogeneous photonic neuro-transistors for power-efficient brain-inspired neuromorphic computing

For the realization of low-power consumption brain-inspired neuromorphic computing devices which mimic the biological neuronal information processing methodology, the development of photonic transistors capable of synaptic behaviors and neuronal computation have attracted lots of interests. Here, metal-chalcogenide (MC)/metal-oxide (MO) heterogeneous photonic neuro-transistors capable of multi-spectrum triggered synaptic responses and corresponding neuronal computation were developed for an intelligent and energy efficient neuromorphic device. The photonic transistor architecture including a solution-processed broadband photo-active heterogeneous channel and electronic modulatory terminal enable to establish power-saved multi-level writing/reading processing. The multi-spectral gate-triggerings and their synaptic responses were emulated via the broadband absorbing MC/MO heterogeneous semiconducting structure and its defective hetero-interface, which can be fine-tuned by varying photo-spectrum of applied spikes and controlling of interfacial traps in-between, respectively. More importantly, the multi-spectrum triggered heterogeneous photonic neuro-transistors can facilitate wider dynamic and more intelligent neuronal computation such as multi-level dendritic summation and fire behaviors, logic-computation, and associated learning beyond conventional simple synaptic-level photonic devices. The results reported here argue that the multi-spectral activated heterogeneous photonic neuro-transistor outperforms current state-of-neuro-devices, provide a facile and generic route to achieve high-density and energy efficient neuromorphic system.

Beyond 30% Conversion Efficiency in Silicon Solar Cells: A Numerical Demonstration

We demonstrate through precise numerical simulations the possibility of flexible, thin-film solar cells, consisting of crystalline silicon, to achieve power conversion efficiency of 31%. Our optimized photonic crystal architecture consists of a 15 μm thick cell patterned with inverted micro-pyramids with lattice spacing comparable to the wavelength of near-infrared light, enabling strong wave-interference based light trapping and absorption. Unlike previous photonic crystal designs, photogenerated charge carrier flow is guided to a grid of interdigitated back contacts with optimized geometry to minimize Auger recombination losses due to lateral current flow. Front and back surface fields provided by optimized Gaussian doping profiles are shown to play a vital role in enhancing surface passivation. We carefully delineate the drop in power conversion efficiency when surface recombination velocities exceed 100 cm/s and the doping profiles deviate from prescribed values. These results are obtained by exact numerical simulation of Maxwell’s wave equations for light propagation throughout the cell architecture and a state-of-the-art model for charge carrier transport and Auger recombination.

Energy Efficiency Analysis of Comb Source Carrier-Injection Ring-Based Silicon Photonic Link

Current electronic interconnections in high performance computing (HPC) systems are reaching their limit in supporting high data traffic demands. Dense wavelength-division multiplexed (DWDM) links have gained interest as they can potentially alleviate these interconnect bandwidth demands while also lowering the cost and energy consumption compared to traditional electronic links. In this article we present an analysis of a ring-based DWDM silicon photonic (SiP) link architecture with a comb laser source and p-i-n photodetectors. Specifically, we consider microring resonators (MRRs) with narrow bus waveguides and carrier-injection ring modulators. We propose a new method to select the optimal comb source setting to minimize the laser power consumption at a particular data rate. Additionally, we leverage power penalty models supported by measurements to estimate the effective received optical power at the receiver input of each of the DWDM channels which yields a bit error rate (BER) of $\text{10}^{-{\text{12}}}$ or lower. We show that the analyzed comb source has the lowest power consumption per channel for 24 consecutive lines. For these comb settings, the maximum channel data rate of non-return to zero on-off keying (NRZ-OOK) signals is 22 Gbps, and the minimum energy consumption is 3.28 $\frac{\text{pJ}}{\text{bit}}$ .

Casimir-Lifshitz Force Based Optical Resonators.

We theoretically investigate the building of optical resonators based on the levitation properties of thin films subjected to strong repulsive Casimir-Lifshitz forces when immersed in an adequate medium and confronted with a planar substrate. We propose a design in which cavities supporting high Q-factor optical modes at visible frequencies can be achieved by means of combining commonly found materials, such as silicon oxide, polystyrene or gold, with glycerol as a mediating medium. We use the balance between flotation and repulsive Casimir-Lifshitz forces in the system to accurately tune the optical cavity thickness and hence its modes. The effects of other forces, such as electrostatic, that may come into play are also considered. Our results constitute a proof of concept that may open the route to the design of photonic architectures in environments in which dispersion forces play a substantial role and could be of particular relevance for devising novel microfluidic optical resonators.

Tunable photon statistics in parametrically amplified photonic molecules

The proposal for single-photon generation utilizing quantum interference of two coupled cavity modes with weak Kerr nonlinearity under resonant coherent driving has been successfully put forward in previous work [T. C. H. Liew and V. Savona, Phys. Rev. Lett. 104, 183601 (2010)]. Here we suggest an alternative scheme to explore the statistical properties of the photons and their control in a parametrically amplified photonic molecule that is composed of a pair of coupled cavities, both containing the degenerate optical parametric amplifier (DOPA), by means of extending the model of Liew and Savona. Due to the destructive quantum interference effect between different paths for two-photon excitation, the photons in both cavity modes can exhibit a strong antibunching effect when one of the two cavities is coherently driven by an external laser field. In order to intuitively understand the strong photon antibunching phenomenon occurring in our DOPA photonic molecule, the approximate analytical solutions of the system are provided via the Schr\odinger equation and further are compared with the full numerical solutions via the master equation. The in-depth results reveal that the two solutions are in good agreement. For the two cavity modes, we achieve the optimal antibunching conditions in terms of the nonlinear gain and the strength ratio between the two pump laser fields based on analytical calculations, gaining deep insight into the rich physics of photon statistics. We also show that the statistical properties of the photons can be well controlled by regulating the relative phase between the two pump laser fields and the tunneling rate between the two cavity modes. Furthermore, we address the second-order cross-correlation function between the two cavity modes and find that a strong intermodal antibunching can be efficiently generated. Our approach via the DOPA photonic molecule architecture can be useful to generate tunable single-photon sources or controlled photonic quantum gates.

Bright G-Quadruplex Nanostructures Functionalized with Porphyrin Lanterns.

The intricate arrangement of numerous and closely placed chromophores on nanoscale scaffolds can lead to key photonic applications ranging from optical waveguides and antennas to signal-enhanced fluorescent sensors. In this regard, the self-assembly of dye-appended DNA sequences into programmed photonic architectures is promising. However, the dense packing of dyes can result in not only compromised DNA assembly (leading to ill-defined structures and precipitates) but also to essentially nonfluorescent systems (due to π-π aggregation). Here, we introduce a two-step "tether and mask" strategy wherein large porphyrin dyes are first attached to short G-quadruplex-forming sequences and then reacted with per-O-methylated β-cyclodextrin (PMβCD) caps, to form supramolecular synthons featuring the porphyrin fluor fixed into a masked porphyrin lantern (PL) state, due to intramolecular host-guest interactions in water. The PL-DNA sequences can then be self-assembled into cyclic architectures or unprecedented G-wires tethered with hundreds of porphyrin dyes. Importantly, despite the closely arrayed PL units (∼2 nm), the dyes behave as bright chromophores (up to 180-fold brighter than the analogues lacking the PMβCD masks). Since other self-assembling scaffolds, dyes, and host molecules can be used in this modular approach, this work lays out a general strategy for the bottom-up aqueous self-assembly of bright nanomaterials containing densely packed dyes.

Deep-learning-powered photonic analog-to-digital conversion

Analog-to-digital converters (ADCs) must be high speed, broadband, and accurate for the development of modern information systems, such as radar, imaging, and communications systems; photonic technologies are regarded as promising technologies for realizing these advanced requirements. Here, we present a deep-learning-powered photonic ADC architecture that simultaneously exploits the advantages of electronics and photonics and overcomes the bottlenecks of the two technologies, thereby overcoming the ADC tradeoff among speed, bandwidth, and accuracy. Via supervised training, the adopted deep neural networks learn the patterns of photonic system defects and recover the distorted data, thereby maintaining the high quality of the electronic quantized data succinctly and adaptively. The numerical and experimental results demonstrate that the proposed architecture outperforms state-of-the-art ADCs with developable high throughput; hence, deep learning performs well in photonic ADC systems. We anticipate that the proposed architecture will inspire future high-performance photonic ADC design and provide opportunities for substantial performance enhancement for the next-generation information systems.

Nanosecond photonic switch architectures demonstrated in an all-digital monolithic platform.

We present an all-digital, fully programmable, nanosecond-scale photonic switch platform, monolithically integrating electronics for actuation, tuning, and power-monitoring alongside switching elements, resulting in a scalable, packageable solution for high-radix photonic switch fabrics. In this platform, we achieve record loss and extinction performances utilizing a 2×2 Mach-Zehnder switch (MZS) with 0.8dB of loss and 28dB extinction and a 2×2 nested MZS with 1.3dB of loss and 38dB extinction.

Production of photonic universal quantum gates enhanced by machine learning

We introduce photonic architectures for universal quantum computation. The first step is to produce a resource state which is a superposition of the first four Fock states with a probability $\geq 10^{-2}$, an increase by a factor of $10^4$ over standard sequential photon-subtraction techniques. The resource state is produced with near-perfect fidelity from a quantum gadget that uses displaced squeezed vacuum states, interferometers and photon-number resolving detectors. The parameters of this gadget are trained using machine learning algorithms for variational circuits. We discuss in detail various aspects of the non-Gaussian state preparation resulting from the numerical experiments. We then propose a notion of resource farms where these gadgets are stacked in parallel, to increase the success probability further. We find a trade-off between the success probability of the farm, the error tolerance, and the number of gadgets. Using the resource states in conventional gate teleportation techniques we can then implement weak tuneable cubic phase gates. The numerical tools that have been developed could potentially be useful for other applications in photonics as well.