arXiv subjects

Xurong Li

Publications and source records attributed to Xurong Li.

9 recordsLinked to original sources

arXiv2026

A fully integrated dispersion-managed femtosecond mode-locked laser

Femtosecond lasers underpin applications ranging from material processing to corneal surgery, while their regular pulse trains form optical frequency combs that have revolutionized timekeeping, spectroscopy, and metrology. On-chip optical frequency combs, such as Kerr microcombs, have enabled high-repetition-rate applications in optical communications and microwave photonics. However, integrated chip-scale sources operating at low repetition rates (100 MHz to 1 GHz), crucial for high peak intensities, remain elusive, as existing devices typically operate well beyond 10 GHz. Here, we demonstrate a self-starting, photonic integrated mode-locked laser based on a dispersion-managed architecture that accesses this regime. The laser combines erbium-implanted silicon nitride gain waveguides, integrated chirped Bragg gratings, and a semiconductor saturable absorber mirror to generate optical pulses with repetition rates from 0.5 to 1.2 GHz, pulse durations as short as 300 fs, and mode-locking thresholds down to 27.3 mW. The output forms a passively stable optical frequency comb with a comb-line drift below 1% of the repetition rate, surpassing the stability of commercial fiber lasers by two orders of magnitude. Leveraging this ultra-low threshold, we achieve complete hybrid integration by co-packaging the laser with a telecom-grade 980-nm III-V pump diode chip inside a compact photonic module. The resulting electrical-in/optical-out module delivers turnkey, stable mode-locked pulses, providing a compact, low-power, and vibration-insensitive foundry-compatible platform for field-deployable optical metrology and precision sensing.

physics.optics↗

arXiv2025

High-pulse-energy integrated mode-locked lasers based on a Mamyshev oscillator

Ultrafast lasers have unlocked numerous advances across science and technology: they enable corneal surgery, reveal chemical reaction dynamics, and underpin optical atomic clocks. Over the past decades, extensive efforts have been devoted to developing photonic integrated circuit-based mode-locked lasers that are compact, scalable, and compatible with further on-chip functionalities. Yet, existing implementations fall short of pulse energies required for their subsequent uses in nonlinear applications. In this work, we demonstrate the first mode-locked laser that overcomes this limitation in low-loss erbium-doped silicon nitride photonic integrated circuits. The laser is based on the Mamyshev oscillator architecture, which employs alternating spectral filtering and self-phase modulation for mode-locking. It delivers a 176 MHz stream of pulses with nanojoule energy, comparable to fiber lasers and surpassing previous photonic integrated sources by more than two orders of magnitude. The output pulses exhibit excellent coherence, can be linearly compressed to 147 fs and directly drive a 1.5-octave-spanning supercontinuum in an integrated waveguide. Our work establishes a new generation of high-pulse-energy photonic integrated mode-locked lasers and paves the way for their widespread adoption.

physics.optics↗

arXiv2025

Copper-impurity-free photonic integrated circuits enable deterministic soliton microcombs

Chip-scale optical frequency combs based on microresonators (microcombs) enable GHz-THz repetition rates, broad bandwidth, compactness, and compatibility with wafer-scale manufacturing. Silicon nitride photonic integrated circuits have become a leading platform due to their low loss, broad transparency, lithographic dispersion control, and commercial 200-mm-wafer foundry access. They have enabled system-level applications in optical communications, LiDAR, frequency synthesis, low-noise microwave generation, and convolutional processing. However, real-world deployment is hindered by the challenge of deterministic soliton microcomb generation, primarily due to thermal instabilities. Although techniques like pulsed pumping, fast scanning, and auxiliary lasers help mitigate these effects, they often add complexity or reduce soliton stability. In this work, we overcome thermal limitations and demonstrate deterministic soliton generation in silicon nitride photonic circuits. We trace the thermal effects to copper impurities within waveguides, originating from residual contaminants in CMOS-grade silicon wafers that are gettered into silicon nitride during fabrication. By developing effective copper removal techniques, we significantly reduce thermal instabilities. This enables soliton generation with arbitrary or slow laser scanning, removing a key barrier to microcomb deployment. Our approach is compatible with front-end-of-line foundry processing, paving the way for broader adoption of soliton microcomb technologies.

physics.optics↗

arXiv2024

Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor

Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization.

physics.optics↗

arXiv2023

Plasmonic photoconductive terahertz focal-plane array with pixel super-resolution

Imaging systems operating in the terahertz part of the electromagnetic spectrum are in great demand because of the distinct characteristics of terahertz waves in penetrating many optically-opaque materials and providing unique spectral signatures of various chemicals. However, the use of terahertz imagers in real-world applications has been limited by the slow speed, large size, high cost, and complexity of the existing imaging systems. These limitations are mainly imposed due to the lack of terahertz focal-plane arrays (THz-FPAs) that can directly provide the frequency-resolved and/or time-resolved spatial information of the imaged objects. Here, we report the first THz-FPA that can directly provide the spatial amplitude and phase distributions, along with the ultrafast temporal and spectral information of an imaged object. It consists of a two-dimensional array of ~0.3 million plasmonic photoconductive nanoantennas optimized to rapidly detect broadband terahertz radiation with a high signal-to-noise ratio. As the first proof-of-concept, we utilized the multispectral nature of the amplitude and phase data captured by these plasmonic nanoantennas to realize pixel super-resolution imaging of objects. We successfully imaged and super-resolved etched patterns in a silicon substrate and reconstructed both the shape and depth of these structures with an effective number of pixels that exceeds 1-kilo pixels. By eliminating the need for raster scanning and spatial terahertz modulation, our THz-FPA offers more than a 1000-fold increase in the imaging speed compared to the state-of-the-art. Beyond this proof-of-concept super-resolution demonstration, the unique capabilities enabled by our plasmonic photoconductive THz-FPA offer transformative advances in a broad range of applications that use hyperspectral and three-dimensional terahertz images of objects for a wide range of applications.

physics.optics↗

arXiv2023

Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array

Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this purpose. Here we introduce an optoelectronic nonlinear filter array that can address this emerging need. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than what is achievable in most optical nonlinear materials. For a proof-of-concept demonstration, we fabricated a 10,000-pixel array of optoelectronic neurons, each serving as a nonlinear filter, and experimentally demonstrated an intelligent imaging system that uses the nonlinear response to instantly reduce input glares while retaining the weaker-intensity objects within the field of view of a cellphone camera. This intelligent glare-reduction capability is important for various imaging applications, including autonomous driving, machine vision, and security cameras. Beyond imaging and sensing, this optoelectronic neuron array, with its rapid nonlinear modulation for processing incoherent broadband light, might also find applications in optical computing, where nonlinear activation functions that can work under ambient light conditions are highly sought.

physics.optics↗

arXiv2023

Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor

Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. However, existing terahertz inspection systems are restricted in their throughput and accuracy (especially for detecting small features) due to their limited speed and resolution. Furthermore, machine vision-based continuous sensing systems that use large-pixel-count imaging are generally bottlenecked due to their digital storage, data transmission and image processing requirements. Here, we report a diffractive processor that rapidly detects hidden defects/objects within a target sample using a single-pixel spectroscopic terahertz detector, without scanning the sample or forming/processing its image. This terahertz processor consists of passive diffractive layers that are optimized using deep learning to modify the spectrum of the terahertz radiation according to the absence/presence of hidden structures or defects. After its fabrication, the resulting diffractive processor all-optically probes the structural information of the sample volume and outputs a spectrum that directly indicates the presence or absence of hidden structures, not visible from outside. As a proof-of-concept, we trained a diffractive terahertz processor to sense hidden defects (including subwavelength features) inside test samples, and evaluated its performance by analyzing the detection sensitivity as a function of the size and position of the unknown defects. We validated its feasibility using a single-pixel terahertz time-domain spectroscopy setup and 3D-printed diffractive layers, successfully detecting hidden defects using pulsed terahertz illumination. This technique will be valuable for various applications, e.g., security screening, biomedical sensing, quality control, anti-counterfeiting measures and cultural heritage protection.

physics.optics↗

arXiv2021

Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks

3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the design of optical networks in the form of multiple diffractive layers that are trained using deep learning to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which are used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique machine vision framework merges the power of deep learning with the spatial and spectral processing capabilities of diffractive networks, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive optical networks.

cs.CV↗

arXiv2019

Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.

cs.CV↗