arXiv subjects

Zaijun Chen

Publications and source records attributed to Zaijun Chen.

At least 19 recordsLinked to original sources

arXiv2026

Ultra-broadband integrated optical parametric amplifier for quantum sensing

Although thin-film lithium niobate (TFLN) facilitates efficient signal generation and nonlinear and quantum interactions, the realization of optical parametric amplification (OPA) that can provide simultaneous ultra-broadband and high-gain operation in an integrated chip continues to pose a challenge. Here we demonstrate continuous-wave-pumped OPA in an X-cut MgO-doped, dispersion-engineered, and adaptively-poled TFLN waveguide of 1.6 cm length, achieving a flat-top profile covering a 450 nm-wide optical wavelength window, corresponding to a 3-dB gain bandwidth of about 56 THz. Among reported TFLN platforms, our device exhibits the broadest 3-dB gain bandwidth. The same device can be pumped either directly at visible wavelengths through second-order x(2) interactions or in the telecom band through cascaded x(2) processes, the latter eliminating the need for a high-power visible pump laser. We achieve maximum gains of 8.87 +- 0.39 dB and 10.79 +- 0.43 dB at on-chip pump powers of 74 mW and 170 mW for the direct and cascaded schemes, respectively. We further directly probe OPA gain across the 1650-1900 nm wavelength range, where experimental gain measurements have remained scarce. With a normalized on-chip gain of 1.21 dB per W per mm, our device sets a new benchmark among reported cascaded x(2) nonlinear processes. This work advances the realization of integrated optical parametric amplifiers, offering high efficiency, robust gain, ultra-broadband bandwidth, and continuous-wave operation, thereby enabling new capabilities for next-generation quantum sensing and photonic systems.

physics.optics↗

arXiv2026

Few-cycle electro-optic light on thin-film lithium niobate

The twin fields of ultrafast optics and nonlinear photonics enable applications ranging from attosecond science [1, 2] and ultrafast electronics [3] to molecular spectroscopy [4, 5], nonlinear optics [6-8], quantum nanophotonics [9] and precision metrology [10]. However, bringing these capabilities-including ultrashort pulse generation, dispersion control, and strong nonlinear interactions-together within a scalable photonic integrated platform requires exceptional performance and cooperation between components while preserving sufficient optical power across the circuit. Here we demonstrate an integrated multi-functional nonlinear photonic system that transforms continuous-wave (CW) light into high-peak-power femtosecond pulses and harnesses them for pulse-driven nonlinear optics on thin-film lithium niobate (TFLN). Microwave-driven electro-optic (EO) broadening followed by integrated dispersive compression generates 230-fs Fourier-transform-limited pulses with energies up to 3.3 pJ at 30.7 GHz, representing orders of magnitude higher pulse energy than previous integrated pulse synthesis at comparable repetition rates [11]. In a 0.3-meter dispersion-engineered TFLN waveguide, soliton dynamics compress the pulses to 35 fs (6.7 optical cycles), accompanied by coherent spectral broadening exceeding 330 nm. In a fully-monolithic architecture, EO synthesis, dispersive compression and a high-Q nonlinear resonator are integrated on a single TFLN chip, enabling resonantly-enhanced pulse pumping and coherent spectral broadening at pulse energies as low as 400 fJ. By unifying microwave-controlled pulse synthesis and pulse-driven nonlinear interactions, our work establishes a direct path from CW excitation to few-cycle nonlinear optics on chip, with opportunities spanning microwave photonics [12], optical frequency synthesis and metrology, and mid-infrared and terahertz generation [13, 14].

physics.optics↗

arXiv2026

18-dB on-chip vacuum squeezing in an adaptively poled lithium niobate waveguide

Quantum squeezed states of light can enhance measurement sensitivity beyond classical limits and enable quantum information processing, but scalable low-loss sources remain challenging. We demonstrate continuous-wave quantum squeezing on a chip, achieving 18 dB of squeezing and 20 dB of anti-squeezing at 1570 nm in a 1.6-cm traveling-wave adaptively poled thin-film lithium niobate waveguide. A distributed model independently determines facet losses, phase noise, and nonlinear interaction strength without prior assumptions, enabling rigorous inference of on-chip performance. We estimate a 95% confidence interval of [-18.96, -17.25] dB squeezing and [19.96, 21.35] dB anti-squeezing. These values represent the highest squeezing reported for any integrated photonic platform and the first assumption-free statistical validation of integrated squeezing performance. Our results establish thin-film lithium niobate as a high-performance, scalable platform for continuous-variable quantum sensing, communications, and photonic computing.

physics.optics↗

arXiv2026

Homodyne Photonic Tensor Processor exceeds 1,000-TOPS

High-performance computing underpins modern artificial intelligence (AI), enabling foundation models, real-time inference and perception in autonomous systems, and data-intensive scientific simulations. Recent advances in quantization techniques utilizing low-precision computation without degrading model accuracy, create new opportunities for analog photonic computing characterized by ultra-high clock rates and low energy consumption. Here we propose and demonstrate a coherent homodyne integrated circuit capable of general matrix multiplication (GEMM) with aggregate throughput that exceeds 1,000 TOPS (tera-operations per second), enabled by massive on-chip optical fanout and parallelism. By leveraging time multiplexing, the required modulator count is reduced from O($N^2$) to O(N), allowing dense integration of record-scale 256 $\times$ 256 homodyne units (each <0.0064 $mm^2$) within a single reticle. We employ wafer-scale fabricated 64 thin-film lithium niobate (TFLN) transmitters (each over 40-GHz bandwidth with propagation loss of 0.2 dB/cm) to encode data and chip-to-chip coupled to Si/SiN computing circuits (64 channels). Our system achieves up to 7-bit computational accuracy across 8 $\times$ 8 parallel channels at record computing clockrate 120 Gbaud/s, and 6-bit statistical accuracy across 256 $\times$ 100 channels at 20-128 Gbaud/s, representing a total throughput of 1,000-6,000 TOPS. Massive parallelism amortizes the optoelectronic (OE) conversion to allow 330-TOPS/W efficiency using foundry-available packaging technology. The system throughput is benchmarked with Qwen2.5-0.5 billion parameter models that generate accurate tokens. High throughput and energy efficiency establish a near-term pathway toward light-based accelerators for large-scale training and low-latency inference from datacenters to edges, accelerating new models toward artificial general intelligence.

cs.ET↗

arXiv2026

Quantum squeezing in an all-resonant periodically poled lithium niobate microresonator

Quantum noise limits the sensitivity of optical measurements, but squeezed states of light enable quantum-enhanced metrology, sensing, and information processing. Most on-chip squeezed-light sources rely on Kerr ($\chi^{(3)}$) nonlinearities, remain limited by pump power and excess loss constraints. Quadratic ($\chi^{(2)}$) platforms instead provide stronger parametric interactions, lower pump power requirements, and greater spectral engineering flexibility. Here, we demonstrate strong, broadband squeezed-light generation on a thin-film lithium niobate (TFLN) photonic chip using a dual-resonant optical parametric amplifier implemented in a single periodically poled LN (PPLN) microresonator. Near-full-depth domain inversion is achieved simultaneously with highly over-coupled resonances, exhibiting escape efficiencies exceeding 90% and intrinsic quality factors above 2.5 million in a 0.6 mm$^2$ X-cut TF-PPLN resonator, enabling efficient squeezing at 1587 nm when pumped at 793.5 nm. Operating in the continuous-wave regime, we directly measure -0.81 dB of squeezing below the shot-noise limit with a pump power of 27 mW, together with +4.29 dB of anti-squeezing. From these measurements, we infer an on-chip squeezing level of -7.52 dB $\pm$ 0.22 dB (95% confidence interval: [-7.96,-7.10] dB), and an on-chip anti-squeezing level of +9.62 dB $\pm$ 0.25 dB. We demonstrate single-mode squeezing at degeneracy with a squeezed-light spectrum exceeding 10.3 THz. This work reports the highest squeezing ratio among integrated $\chi^{(2)}$ cavity platforms and the first quasi-phase matched, fully resonant $\chi^{(2)}$ cavity squeezer on chip, establishing a scalable route to fully integrated power-efficient squeezed-light sources for quantum-enhanced sensing and metrology.

physics.optics↗

arXiv2026

Quantization-aware Photonic Homodyne computing for Accelerated Artificial Intelligence and Scientific Simulation

Modern problems in high-performance computing, ranging from training and inferencing deep learning models in computer vision and language models to simulating complex physical systems with nonlinearly-coupled equations, require exponential growth of computational resources. Photonic analog systems are emerging with solutions of intrinsic parallelism, high bandwidth, and low propagation loss. However, their application has been hindered by the low analog accuracy due to the electro-optic distortion, material nonlinearities, and signal-to-noise ratios. Here we overcome this barrier with a quantization-aware digital-photonic mixed-precision framework across chiplets for accelerated AI processing and physical simulation. Using Lithium Niobate photonics with channel equalization techniques, we demonstrate linear multiplication (9-bit amplitude-phase decoupling) in homodyne optical logics with 6-bit precision at the clock rate of 128 giga-symbol-per-second (128 GS/s), enabling AI processing with 6 ns latency. Codesign hardware-algorithms, including iterative solvers, sparse-dense quantization, and bit-sliced matrix multiplication, explore photonic amplitude and phase coherence for complex-valued, physics-inspired computation. In electromagnetic problems, our approach yields 12-bit solutions for partial differential equations (PDEs) in scattering problems that would conventionally require up to 32-bit and often even 64-bit precision. These results preserve digital-level fidelity while leveraging the high-speed low-energy photonic hardware, establishing a pathway toward general-purpose optical acceleration for generative artificial intelligence, real-time robotics, and accurate simulation for climate challenges and biological discoveries.

cs.ET↗

arXiv2026

Suspended thin-film lithium niobate modulator for broadband mid-infrared light modulation and frequency comb generation

The mid-infrared (MIR) spectral regime is central to applications including remote sensing, precision spectroscopy, higher harmonic generation, and free-space optical communication. However, coherent and broadband MIR modulation remains challenging owing to high optical loss, limited bandwidth, and large drive voltages in existing platforms. Here, we overcome these challenges by deploying a suspended thin-film lithium-niobate (TFLN) based electro-optic (EO) platform co-designed with high-performance traveling-wave microwave (MW) electrodes. We demonstrate a record-low Vpi,DC of 2.3 to 4.3 V over a broadband MIR bandwidth from 2.4 to 3.6 um, and a 2.7 dB EO bandwidth of 40 GHz (extracted 3 dB bandwidth of 50 GHz), yielding a figure of merit of 17.4 GHz/V, more than an order of magnitude higher than the state of the art. We demonstrate, for the first time, high-frequency Vpi,MW of 4.5 to 6.5 V in the 25 to 35 GHz range, and frequency-agile MIR EO frequency comb generation with a 10 dB optical bandwidth over 0.8 THz using a suspended phase modulator of 4 cm active modulation length. We further validate the platform in a free-space optical communication link. Our results establish a monolithic MIR photonic platform capable of powerful EO modulation and spectral synthesis, and represent a significant step toward reconfigurable MIR sensing and communication systems on chip.

physics.optics↗

arXiv2026

Uncooled low-noise thin-film optomechanical resonator for thermal sensing on lithium niobate

Optomechanical transduction harnesses the interaction between optical fields and mechanical motion to achieve sensitive measurement of weak mechanical quantities with inherently low noise. Lithium niobate combines low optical loss, strong piezoelectricity, high intrinsic fQ_m factor, and low thermal conductivity, making it promising for exploring optomechanical platforms targeting thermal sensing applications. Here, we developed an integrated optomechanical platform on thin-film lithium niobate with precisely engineered optical, mechanical, and thermal fields within a compact 40 {\mu}m by 40 {\mu}m footprint. The platform integrates suspended microring resonators with ultrathin central membranes, reducing mechanical stiffness and effective mass while maintaining a high optical factor Q_o of 1e6 and mechanical quality factor Q_m of 1117, which increases to 5.1e4 after oscillation. The design suppresses thermal dissipation into the silicon substrate and enhances thermal sensitivity, achieving a temperature coefficient of frequency of -124 ppm/K and a noise-equivalent power of 6.2 nW/sqrt(Hz) at 10 kHz at room temperature. This compact and scalable platform opens up new opportunities for high-sensitivity thermal sensing, supports heterogeneous integration with infrared absorbers for uncooled infrared detection, and enables fully integrated, all-optical on-chip readout, paving the way toward large-format, low-noise infrared sensing arrays.

physics.optics↗

arXiv2025

Fundamental Phase Noise in Thin Film Lithium Niobate Resonators

Fundamental phase noise in thin-film lithium niobate (TFLN) photonic integrated circuits is governed by thermal-charge-carrier-refractive (TCCR) dynamics arising from thermally driven carrier fluctuations. In contrast to the predominantly thermorefractive noise in silicon photonic platforms, TCCR noise represents a distinct mechanism that becomes critical for applications requiring high frequency stability and phase coherence, including optomechanical sensing, low-phase-noise microwave synthesis, and on-chip quantum squeezing. A quantitative understanding of the deterministic parameters that control TCCR noise is therefore essential for engineering the next generation of low-noise TFLN photonic systems. Here, we identify two dominant contributors to the TCCR noise in TFLN microresonators: material anisotropy and surface states. Material anisotropy results in increased noise for extraordinarily polarized optical modes and leads to a geometry dependent phase noise. Surface-state effects manifest as increased noise in higher-order transverse modes as well as more than 120-fold higher noise in suspended microresonators. Finally, we demonstrate that post-fabrication annealing -- widely used to reduce defect densities and recover crystal quality -- suppresses frequency noise by a factor of 8.2 in cladded microresonators. Together, these results establish a practical pathway for noise engineering in TFLN integrated photonic devices and accelerate their deployment in next-generation precision photonic systems.

physics.optics↗

arXiv2025

High Clockrate Free-space Optical In-Memory Computing

The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing and remote robotics. However, the deployment of deep neural networks (DNNs) in edge devices is hindered by the lack of energy-efficient scalable computing hardware. Here, we introduce a fanout spatial time-of-flight optical neural network (FAST-ONN) that calculates billions of convolutions per second with ultralow latency and power consumption. This is enabled by the combination of high-speed dense arrays of vertical-cavity surface-emitting lasers (VCSELs) for input modulation with spatial light modulators of high pixel counts for in-memory weighting. In a three-dimensional optical system, parallel differential readout allows signed weight values accurate inference in a single shot. The performance is benchmarked with feature extraction in You-Only-Look-Once (YOLO) for convolution at 100 million frames per second (MFPS), and in-system backward propagation training with photonic reprogrammability. The VCSEL transmitters are implementable in any free-space optical computing systems to improve the clockrate to over gigahertz. The high scalability in device counts and channel parallelism enables a new avenue to scale up free space computing hardware.

cs.ET↗

arXiv2025

Single-Shot Matrix-Matrix Multiplication Optical Tensor Processor for Deep Learning

The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promising solution, offering benefits in bandwidth and energy consumption. However, existing ONN processors exhibit limited computational parallelism, and while certain architectures achieve high parallelism, they encounter serious scaling roadblocks for large-scale implementation. This restricts the throughput, latency, and energy efficiency advantages of ONN processors. Here, we introduce a spatial-wavelength-temporal hyper-multiplexed ONN processor that supports high data dimensionality, high computing parallelism and is feasible for large-scale implementation, and in a single time step, a three-dimensional matrix-matrix multiplication (MMM) optical tensor processor is demonstrated. Our hardware accelerates convolutional neural networks (CNNs) and deep neural networks (DNNs) through parallel matrix multiplication. We demonstrate benchmark image recognition using a CNN and a subsequently fully connected DNN in the optical domain. The network works with 292,616 weight parameters under ultra-low optical energy of 20 attojoules (aJ) per multiply and accumulate (MAC) at 96.4% classification accuracy. The system supports broad spectral and spatial bandwidths and is capable for large-scale demonstration, paving the way for highly efficient large-scale optical computing for next-generation deep learning.

physics.optics↗

arXiv2024

Photorefractive and pyroelectric photonic memory and long-term stability in thin-film lithium niobate microresonators

The stability of the integrated photonic circuits is of critical importance for many applications that require high frequency precision or robust operation over time, such as optomechanical sensing, frequency conversion, optical communication, and quantum optics. Photonic memory is useful for low-energy optical computing and interconnects. Thin film lithium niobate (TFLN), as an emerging photonic platform, exhibits complex material properties including pyroelectric (PE) and photorefractive (PR) effects which could lead to intra-device drift and excess noise under different environmental or operating conditions as well as be utilized for building photonic memory. However, the long-term stability and memory effect of its optical properties has not been explored. In this paper, we discovered a long-lived change of optical refractive index as a result of light excitation and temporal temperature variation using Z-cut TFLN microresonators and reveal a strong dependence of the instability with the crystal orientation of the thin film form. The recovery time are measured to be over 10 hours. Leveraging the photonic memory with a long relaxation time, we realize optical trimming of the cavity resonance frequencies. Our result offers insights towards understanding the fundamental noise properties and dynamic behavior of the integrated TFLN material and devices.

physics.optics↗

arXiv2024

Hypermultiplexed Integrated-Photonics-based Tensor Optical Processor

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we demonstrate a hypermultiplexed integratedphotonics-based tensor optical processor (HITOP) that can perform trillions of operations per second (TOPS) at the energy efficiency of 40 TOPS/W. Space-time-wavelength three-dimensional (3D) optical parallelism enables O($N^{2}$) operations per clock-cycle using O($N$) modulator devices. The system is built with wafer-fabricated III/V micron-scale lasers and high-speed thin-film Lithium-Niobate electro-optics for encoding at 10s femtojoule/symbol. Lasing threshold incorporates analog inline rectifier (ReLu) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clockrates, energy-efficient processing and programmability unlocks the potential of light for large-scale AI accelerators in applications ranging from training of large AI models to real-time decision making in edge deployment.

cs.ET↗

arXiv2023

Near-ultraviolet photon-counting dual-comb spectroscopy

Ultraviolet spectroscopy provides unique insights into the structure of matter with applications ranging from fundamental tests to photochemistry in the earth's atmosphere and astronomical observations from space telescopes. At longer wavelengths, dual-comb spectroscopy with two interfering laser frequency combs has evolved into a powerful technique that can offer simultaneously a broad spectral range and very high resolution. Here we demonstrate a photon-counting approach that can extend the unique advantages of this method into ultraviolet regions where nonlinear frequency-conversion tends to be very inefficient. Our spectrometer, based on two frequency combs of slightly different repetition frequencies, provides broad span, high resolution, frequency calibration within the accuracy of an atomic clock, and overall consistency of the spectra. We demonstrate a signal-to-noise ratio at the quantum limit and optimal use of the measurement time, provided by the multiplex recording of all spectral data on a single photo-counter. Our initial experiments are performed in the near-ultraviolet and in the visible spectral ranges with alkali-atom vapor, with a power per comb line as low as a femtowatt. This crucial step towards precision broadband spectroscopy at short wavelengths clears the path to extreme-ultraviolet dual-comb spectroscopy and, more generally, generates a new realm of applications for diagnostics at photon level, as encountered e.g., when driving single atoms or molecules.

physics.optics↗

arXiv2023

Entanglement-enhanced dual-comb spectroscopy

Dual-comb interferometry harnesses the interference of two laser frequency combs to provide unprecedented capability in spectroscopy applications. In the past decade, the state-of-the-art systems have reached a point where the signal-to-noise ratio per unit acquisition time is fundamentally limited by shot noise from vacuum fluctuations. To address the issue, we propose an entanglement-enhanced dual-comb spectroscopy protocol that leverages quantum resources to significantly improve the signal-to-noise ratio performance. To analyze the performance of real systems, we develop a quantum model of dual-comb spectroscopy that takes practical noises into consideration. Based on this model, we propose quantum combs with side-band entanglement around each comb lines to suppress the shot noise in heterodyne detection. Our results show significant quantum advantages in the uW to mW power range, making this technique particularly attractive for biological and chemical sensing applications. Furthermore, the quantum comb can be engineered using nonlinear optics and promises near-term experimentation.

quant-ph↗

arXiv2022

Deep Learning with Coherent VCSEL Neural Networks

Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain with high clock rates, parallelism and low-loss data transmission. However, to explore the potential of ONNs, it is necessary to investigate the full-system performance incorporating the major DNN elements, including matrix algebra and nonlinear activation. Existing challenges to ONNs are high energy consumption due to low electro-optic (EO) conversion efficiency, low compute density due to large device footprint and channel crosstalk, and long latency due to the lack of inline nonlinearity. Here we experimentally demonstrate an ONN system that simultaneously overcomes all these challenges. We exploit neuron encoding with volume-manufactured micron-scale vertical-cavity surface-emitting laser (VCSEL) transmitter arrays that exhibit high EO conversion (<5 attojoule/symbol with $V_\pi$=4 mV), high operation bandwidth (up to 25 GS/s), and compact footprint (<0.01 mm$^2$ per device). Photoelectric multiplication allows low-energy matrix operations at the shot-noise quantum limit. Homodyne detection-based nonlinearity enables nonlinear activation with instantaneous response. The full-system energy efficiency and compute density reach 7 femtojoules per operation (fJ/OP) and 25 TeraOP/(mm$^2\cdot$ s), both representing a >100-fold improvement over state-of-the-art digital computers, with substantially several more orders of magnitude for future improvement. Beyond neural network inference, its feature of rapid weight updating is crucial for training deep learning models. Our technique opens an avenue to large-scale optoelectronic processors to accelerate machine learning tasks from data centers to decentralized edge devices.

cs.ET↗

arXiv2022

RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement

Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this issue is the exponentially increasing demand for deep neural network (DNN) compute, including DNNs for signal processing. However, the slowing of Moore's Law due to the limitations of transistor-based electronics means that completely new paradigms for computing will be required to meet these increasing demands for advanced communications. Optical neural networks (ONNs) are promising DNN accelerators with ultra-low latency and energy consumption. Yet state-of-the-art ONNs struggle with scalability and implementing linear with in-line nonlinear operations. Here we introduce our multiplicative analog frequency transform ONN (MAFT-ONN) that encodes the data in the frequency domain, achieves matrix-vector products in a single shot using photoelectric multiplication, and uses a single electro-optic modulator for the nonlinear activation of all neurons in each layer. We experimentally demonstrate the first hardware accelerator that computes fully-analog deep learning on raw RF signals, performing single-shot modulation classification with 85% accuracy, where a 'majority vote' multi-measurement scheme can boost the accuracy to 95% within 5 consecutive measurements. In addition, we demonstrate frequency-domain finite impulse response (FIR) linear-time-invariant (LTI) operations, enabling a powerful combination of traditional and AI signal processing. We also demonstrate the scalability of our architecture by computing nearly 4 million fully-analog multiplies-and-accumulates for MNIST digit classification. Our latency estimation model shows that due to the Shannon capacity-limited analog data movement, MAFT-ONN is hundreds of times faster than traditional RF receivers operating at their theoretical peak performance.

cs.ET↗

arXiv2022

Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection. The simultaneous use of wavelength multiplexing, broadband modulation, and integration detection allows large neural networks to be run at the client by effectively pushing the energy and memory requirements back to the server. The performance and energy efficiency are fundamentally limited by crosstalk and detector noise, respectively. We derive analytic expressions for these limits and perform numerical simulations to verify these bounds.

cs.ET↗