Photonic Processing Research Articles

Probabilistic photonic computing with chaotic light

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as “point estimates” that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.

Nonvolatile silicon photonic MEMS switch based on centrally clamped stepped bistable mechanical beams

High-performance photonic switches are essential for large-scale optical routing for AI large models and the Internet of Things. Realizing nonvolatility can further reduce power consumption and expand application scenarios. We propose a nonvolatile 2×2 silicon photonic micro-electromechanical system (MEMS) switch compatible with standard silicon photonic foundry processes. The switch employs an electrostatic comb actuator to change the air gap of the compact horizontal adiabatic coupler and achieves nonvolatility with centrally clamped stepped bistable mechanical beams. The photonic switch features a 10s μs-scale switching speed and a 10s fJ-scale simulated switching energy within a 100 μm×100 μm footprint, with ≤12 V driving voltages. This 2×2 switch can be used in a variety of topologies for large-scale photonic switches, and its nonvolatility can potentially support future photonic field programmable gate array designs.

Performance and Characterization of Additively Manufactured BST Varactor Enhanced by Photonic Thermal Processing

The demand for reconfigurable devices for emerging RF and microwave applications has been growing in recent years, with additive manufacturing and photonic thermal treatment presenting new possibilities to supplement conventional fabrication processes to meet this demand. In this paper, we present the realization and analysis of barium–strontium–titanate-(Ba0.5Sr0.5TiO3)-based ferroelectric variable capacitors (varactors), which are additively deposited on top of conventionally fabricated interdigitated capacitors and enhanced by photonic thermal processing. The ferroelectric solution with suspended BST nanoparticles is deposited on the device using an ambient spray pyrolysis method and is sintered at low temperatures using photonic thermal processing by leveraging the high surface-to-volume ratio of the BST nanoparticles. The deposited film is qualitatively characterized using SEM imaging and XRD measurements, while the varactor devices are quantitatively characterized by using high-frequency RF measurements from 300 MHz to 10 GHz under an applied DC bias voltage ranging from 0 V to 50 V. We observe a maximum tunability of 60.6% at 1 GHz under an applied electric field of 25 kV/mm (25 V/μm). These results show promise for the implementation of photonic thermal processing and additive manufacturing as a means to integrate reconfigurable ferroelectric varactors in flexible electronics or tightly packaged on-chip applications, where a limited thermal budget hinders the conventional thermal processing.

Protocol for nearly deterministic parity projection on two photonic qubits

Photonic parity projection plays an important role in photonic quantum information processing. Nondestructive parity projections normally require high-fidelity controlled-Z gates between photonic and matter qubits, which can be experimentally demanding. In this paper, we propose a nearly deterministic parity projection protocol on two photonic qubits which only requires stable matter-photon controlled-phase gates. We also demonstrate that our protocol can tolerate moderate Gaussian phase errors in the controlled-phase gates as well as Pauli errors on the matter qubits. The fact that our protocol does not require perfect controlled-Z gates makes it more amenable to experimental implementation. Although we focus on photonic qubits, our protocol can be applied to any physical system or circuit with imperfect controlled-Z gates. Our protocol also provides a new optimization space for parity projection operations on various physical platforms, which is potentially beneficial for achieving high-fidelity parity projection operations. Published by the American Physical Society 2024

ΦPU – A Photonic Processing Unit for Heterogeneous Optical Networks

ΦPU – A Photonic Processing Unit for Heterogeneous Optical Networks

Spatially varying nanophotonic neural networks.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.

Evidence of the Quantum Optical Nature of High-Harmonic Generation

High-harmonic generation is a light up-conversion process occurring in a strong laser field, leading to coherent bursts of extreme ultrashort broadband radiation [Lewenstein , Phys. Rev. A , 2117 (1994)]. As a new perspective, we propose that ultrafast strong-field electronic or photonic processes such as high-harmonic generation can potentially generate nonclassical states of light well before the decoherence of the system occurs [Gorlach , Nat. Commun. , 4598 (2020); Stammer ., Phys. Rev. Lett. , 123603 (2022)]. This could address fundamental challenges in quantum technology such as scalability, decoherence, or the generation of massively entangled states [Lewenstein , Luca Argenti Michael Chini, 27 (2024)]. Here, we report experimental evidence of the nonclassical nature of the harmonic emission in several semiconductors excited by a femtosecond infrared laser. By investigating single- and double-beam intensity cross-correlation [Loudon, Rep. Prog. Phys. , 913 (1980)], we measure characteristic nonclassical features in the single-photon statistics. We observe two-mode squeezing in the generated harmonic radiation, which depends on the laser intensity that governs the transition from super-Poissonian to Poissonian photon statistics. The measured violation of the Cauchy-Schwarz inequality realizes a direct test of multipartite entanglement in high-harmonic generation [Wasak, Phys. Rev. A , 033616 (2014)]. This result is supported by the theory of multimodal detection and the Hamiltonian from which the effective squeezing modes of the harmonics can be derived [Gonoskov , Phys. Rev. B , 125110 (2024); Christ New J. Phys. , 033027 (2011)]. With this work, we show experimentally that high-harmonic generation is a new quantum bosonic platform that intrinsically produces nonclassical states of light with unique features such as multipartite broadband entanglement or multimode squeezing. The source operates at room temperature, using standard semiconductors and a standard commercial fiber laser, opening up new routes for the quantum industry, such as optical quantum computing, communication, and imaging. Published by the American Physical Society 2024

Scalable parallel photonic processing unit for various neural network accelerations

In recent years, integrated optical processing units (IOPUs) have demonstrated advantages in energy efficiency and computational speed for neural network inference applications. However, limited by optical integration technology, the practicality and versatility of IOPU face serious challenges. In this work, a scalable parallel photonic processing unit (SPPU) for various neural network accelerations based on high-speed phase modulation is proposed and implemented on a silicon-on-insulator platform, which supports parallel processing and can switch between multiple computational paradigms simply and without latency to infer different neural network structures, enabling to maximize the utility of on-chip components. The SPPU adopts a scalable and process-friendly architecture design, with a preeminent photonic-core energy efficiency of 0.83 TOPS/W, two to ten times higher than existing integrated solutions. In the proof-of-concept experiment, a convolutional neural network (CNN), a residual CNN, and a recurrent neural network (RNN) are all implemented on our photonic processor to handle multiple tasks of handwritten digit classification, signal modulation format recognition, and review emotion recognition. The SPPU achieves multi-task parallel processing capability, serving as a promising and attractive research route to maximize the utility of on-chip components under the constraints of integrated technology, which helps to make IOPU more practical and universal.

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing

AbstractProcessing information in the optical domain promises advantages in both speed and energy efficiency over existing digital hardware for a variety of emerging applications in artificial intelligence and machine learning. A typical approach to photonic processing is to multiply a rapidly changing optical input vector with a matrix of fixed optical weights. However, encoding these weights on-chip using an array of photonic memory cells is currently limited by a wide range of material- and device-level issues, such as the programming speed, extinction ratio and endurance, among others. Here we propose a new approach to encoding optical weights for in-memory photonic computing using magneto-optic memory cells comprising heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. We show that leveraging the non-reciprocal phase shift in such magneto-optic materials offers several key advantages over existing architectures, providing a fast (1 ns), efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing.

Monte Carlo dose calculation for photon and electron radiotherapy on dynamically deforming anatomy.

Dose calculation in radiotherapy aims to accurately estimate and assess the dose distribution of a treatment plan. Monte Carlo (MC) dose calculation is considered the gold standard owing to its ability to accurately simulate particle transport in inhomogeneous media. However, uncertainties such as the patient's dynamically deforming anatomy can still lead to differences between the delivered and planned dose distribution. Development and validation of a deformable voxel geometry for MC dose calculations (DefVoxMC) to account for dynamic deformation in the dose calculation process of photon- and electron-based radiotherapy treatment plans for clinically motivated cases. DefVoxMC relies on the subdivision of a regular voxel geometry into dodecahedrons. It allows shifting the dodecahedrons' corner points according to the deformation in the patient's anatomy using deformation vector fields (DVF). DefVoxMC is integrated into the Swiss Monte Carlo Plan (SMCP) to allow the MC dose calculation of photon- and electron-based treatment plans on the deformable voxel geometry. DefVoxMC is validated in two steps. A compression test and a Fano test are performed in silico. Delta4 (for photon beams) and EBT4 film measurements in a cubic PMMA phantom (for electron beams) are performed on a TrueBeam in Developer Mode for clinically motivated treatment plans. During these measurements, table motion is used to mimic rigid dynamic patient motion. The measured and calculated dose distributions are compared using gamma passing rate (GPR) (3% / 2mm (global), 10% threshold). DefVoxMC is used to study the impact of patient-recorded breathing motion on the dose distribution for clinically motivated lung and breast cases, each prescribed 50Gy to 50% of the target volume. A volumetric modulated arc therapy (VMAT) and an arc mixed-beam radiotherapy (Arc-MBRT) plan are created for the lung and breast case, respectively. For the dose calculation, the dynamic deformation of the patient's anatomy is described by DVFs obtained from deformable image registration of the different phases of 4DCTs. The resulting dose distributions are compared to the ones of the static situation using dose-volume histograms and dose differences. DefVoxMC is successfully integrated into the SMCP to enable the MC dose calculation of photon- and electron-based treatments on a dynamically deforming patient anatomy. The compression and the Fano test agree within 1.0% and 0.1% with the expected result, respectively. Delta4 and EBT4 film measurements agree with the calculated dose by a GPR>95%. For the clinically motivated cases, breathing motion resulted in areas with a dose increase of up to 26.9Gy (lung) and up to 7.6Gy (breast) compared to the static situation. The largest dose differences are observed in high-dose-gradient regions perpendicular to the beam plane, consequently decreasing the planning target volume coverage (V95%) by 4.2% for the lung case and 2.0% for the breast case. A novel method for MC dose calculation for photon- and electron-based treatments on dynamically deforming anatomy is successfully developed and validated. Applying DefVoxMC to clinically motivated cases, we found that breathing motion has non-negligible impact on the dosimetric plan quality.

Deep subwavelength slotted photonic crystal nanobeam in a monolithic silicon photonics foundry.

We report the design and experimental realization of a deep subwavelength-engineered slotted photonic crystal fabricated using a commercial monolithic silicon photonics process with a minimum feature size near 40 nm. The deep subwavelength design includes a corrugated, slotted unit cell shape that leverages electromagnetic interface conditions to localize optical energy in low refractive index regions, achieving a four-fold enhancement of the electric field energy compared to an equivalent slotted photonic crystal without the nanoscale corrugations. This demonstration establishes a basis for future study of commercially fabricated, subwavelength-engineered photonic structures where intense light-matter interaction and manipulation of optical properties on-chip is critical, including biosensing and optical trapping applications.

Efficient mass manufacturing of high-density, ultra-low-loss Si3N4 photonic integrated circuits

Silicon nitride (Si3N4) photonic integrated circuits (PICs) offer significant advantages over traditional silicon photonics, including low loss and superior power handling at optical communication wavelength bands. To facilitate high-density integration and effective nonlinearity, the use of thick, stoichiometric Si3N4 films is crucial. However, when using low-pressure chemical vapor deposition (LPCVD) to achieve high optical material transparency, Si3N4 films exhibit large tensile stress on the order of GPa, leading to wafer cracking that challenges mass production. Methods for crack prevention are therefore essential. The photonic Damascene process has addressed this issue, attaining record low-loss Si3N4 PICs, but it lacks control of the waveguide height, leading to large random variations of waveguide dispersion and unpredictable spectrum responses of critical functional devices such as optical couplers. Conversely, subtractive processes achieve better dimension control but rely on techniques unsuitable for large-scale production. To date, an outstanding challenge is to attain both lithographic precision and ultra-low loss in high-confinement Si3N4 PICs that are compatible with large-scale foundry manufacturing. Here, we present a single-step deposited, DUV-based subtractive method for producing wafer-scale ultra-low-loss Si3N4 PICs that harmonize these necessities. By employing deep etching of densely distributed, interconnected trenches into the substrate, we effectively mitigate the tensile stress in the Si3N4 layer, enabling direct deposition of thick films without cracking and substantially prolonged storage duration. A secondary ion mass spectrometry (SIMS) analysis reveals that these deep trenches simultaneously serve as gettering centers for metal impurities, in particular copper, thereby reducing the absorption loss in Si3N4 waveguides. Lastly, we identify ultraviolet (UV)-radiation-induced damage that can be remedied through a rapid thermal annealing. Collectively, we develop ultra-low-loss Si3N4 microresonators and 0.5-m-long spiral waveguides with losses down to 1.4 dB/m at 1550 nm with high production yield. This work addresses the long-standing challenges toward scalable and cost-effective production of tightly confined, low-loss Si3N4 PICs as used for quantum photonics, large-scale linear and nonlinear photonics, photonic computing, and narrow-linewidth lasers.

Near-field strong coupling and entanglement of quantum emitters for room-temperature quantum technologies

In recent years, quantum nanophotonics has forged a rich nexus of nanotechnology with photonic quantum information processing, offering remarkable prospects for advancing quantum technologies beyond their current technical limits in terms of physical compactness, energy efficiency, operation speed, temperature robustness and scalability. In this perspective, we highlight a number of recent studies that reveal the especially compelling potential of nanoplasmonic cavity quantum electrodynamics for driving quantum technologies down to nanoscale spatial and ultrafast temporal regimes, whilst elevating them to ambient temperatures. Our perspective encompasses innovative proposals for quantum plasmonic biosensing, driving ultrafast single-photon emission and achieving near-field multipartite entanglement in the strong coupling regime, with a notable emphasis on the use of industry-grade devices. We conclude with an outlook emphasizing how the bespoke characteristics and functionalities of plasmonic devices are shaping contemporary research directives in ultrafast and room-temperature quantum nanotechnologies.

Photonic neuromorphic accelerators for event-based imaging flow cytometry

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The event-based camera is capable of capturing 1 Gevents/sec, where events correspond to pixel contrast changes, similar to the retina’s ganglion cell function. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains using a purely analogue version of the convolutional operation. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.1 m/sec. Classification accuracy, using only lightweight digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer at a rate of 3 × 106 images per second, we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network. These results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications.

Multifunctional Microwave Photonic Processor With Sideband-Dependent Filtering and Phase Shifting Based on Broadband Optical Source and Hybrid Spectrum Slicer

Multifunctional Microwave Photonic Processor With Sideband-Dependent Filtering and Phase Shifting Based on Broadband Optical Source and Hybrid Spectrum Slicer

Efficient photon-pair generation in layer-poled lithium niobate nanophotonic waveguides

Integrated photon-pair sources are crucial for scalable photonic quantum systems. Thin-film lithium niobate is a promising platform for on-chip photon-pair generation through spontaneous parametric down-conversion (SPDC). However, the device implementation faces practical challenges. Periodically poled lithium niobate (PPLN), despite enabling flexible quasi-phase matching, suffers from poor fabrication reliability and device repeatability, while conventional modal phase matching (MPM) methods yield limited efficiencies due to inadequate mode overlaps. Here, we introduce a layer-poled lithium niobate (LPLN) nanophotonic waveguide for efficient photon-pair generation. It leverages layer-wise polarity inversion through electrical poling to break spatial symmetry and significantly enhance nonlinear interactions for MPM, achieving a notable normalized second-harmonic generation (SHG) conversion efficiency of 4615% W−1cm−2. Through a cascaded SHG and SPDC process, we demonstrate photon-pair generation with a normalized brightness of 3.1 × 106 Hz nm−1 mW−2 in a 3.3 mm long LPLN waveguide, surpassing existing on-chip sources under similar operating configurations. Crucially, our LPLN waveguides offer enhanced fabrication reliability and reduced sensitivity to geometric variations and temperature fluctuations compared to PPLN devices. We expect LPLN to become a promising solution for on-chip nonlinear wavelength conversion and non-classical light generation, with immediate applications in quantum communication, networking, and on-chip photonic quantum information processing.

Modular quantum-to-quantum Bernoulli factory in an integrated photonic processor

Modular quantum-to-quantum Bernoulli factory in an integrated photonic processor

Steric Engineering of HOF Hierarchical Branched Homostructures for 3D Photonic Logic Processing

Steric Engineering of HOF Hierarchical Branched Homostructures for 3D Photonic Logic Processing

Reconfigurable integrated photonic processor for NP-complete problems

Reconfigurable integrated photonic processor for NP-complete problems