arXiv subjects

Yidong Huang

Publications and source records attributed to Yidong Huang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model

Subwavelength photonic structures and metamaterials provide revolutionary approaches for controlling light. The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices. However, most of the existing inverse design methods cannot realize direct mapping from optical properties to photonic structures but instead rely on forward simulation methods to perform iterative optimization. In this work, we exploit the powerful generative abilities of artificial intelligence (AI) and propose a practical inverse design method based on latent diffusion models. Our method maps directly the optical properties to structures without the requirement of forward simulation and iterative optimization. Here, the given optical properties can work as "prompts" and guide the constructed model to correctly "draw" the required photonic structures. Experiments show that our direct mapping-based inverse design method can generate subwavelength photonic structures at high fidelity while following the given optical properties. This may change the method used for optical design and greatly accelerate the research on new photonic devices.

physics.optics↗

arXiv2024

SUANPAN: Scalable Photonic Linear Vector Machine

Photonic linear operation is a promising approach to handle the extensive vector multiplications in artificial intelligence techniques due to the natural bosonic parallelism and high-speed information transmission of photonics. Although it is believed that maximizing the interaction of the light beams is necessary to fully utilize the parallelism and tremendous efforts have been made in past decades, the achieved dimensionality of vector-matrix multiplication is very limited due to the difficulty of scaling up a tightly interconnected or highly coupled optical system. Additionally, there is still a lack of a universal photonic computing architecture that can be readily merged with existing computing system to meet the computing power demand of AI techniques. Here, we propose a programmable and reconfigurable photonic linear vector machine to perform only the inner product of two vectors, formed by a series of independent basic computing units, while each unit is just one pair of light-emitter and photodetector. Since there is no interaction among light beams inside, extreme scalability could be achieved by simply duplicating the independent basic computing unit while there is no requirement of large-scale analog-to-digital converter and digital-to-analog converter arrays. Our architecture is inspired by the traditional Chinese Suanpan or abacus and thus is denoted as photonic SUANPAN. As a proof of principle, SUANPAN architecture is implemented with an 8*8 vertical cavity surface emission laser array and an 8*8 MoTe2 two-dimensional material photodetector array. We believe that our proposed photonic SUANPAN is capable of serving as a fundamental linear vector machine that can be readily merged with existing electronic digital computing system and is potential to enhance the computing power for future various AI applications.

physics.optics↗

arXiv2024

DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences

Recent advancements in foundation models (FMs) have unlocked new prospects in autonomous driving, yet the experimental settings of these studies are preliminary, over-simplified, and fail to capture the complexity of real-world driving scenarios in human environments. It remains under-explored whether FM agents can handle long-horizon navigation tasks with free-from dialogue and deal with unexpected situations caused by environmental dynamics or task changes. To explore the capabilities and boundaries of FMs faced with the challenges above, we introduce DriVLMe, a video-language-model-based agent to facilitate natural and effective communication between humans and autonomous vehicles that perceive the environment and navigate. We develop DriVLMe from both embodied experiences in a simulated environment and social experiences from real human dialogue. While DriVLMe demonstrates competitive performance in both open-loop benchmarks and closed-loop human studies, we reveal several limitations and challenges, including unacceptable inference time, imbalanced training data, limited visual understanding, challenges with multi-turn interactions, simplified language generation from robotic experiences, and difficulties in handling on-the-fly unexpected situations like environmental dynamics and task changes.

cs.CV↗

arXiv2024

Low-Voltage Electron Emission by Graphene-hBN-graphene Heterostructure

Scanning Electron Microscopes (SEM) with low energy electron sources (accelerating voltage of less than 1000V) have important application requirements in many application scenarios. Tunneling junction can potentially achieve low-voltage and planar-type electron sources with good emission current density. However, further lower the extracting voltage while ensure the emission current density remains challenging. In this paper, we report a low-voltage planar-type electron source based on graphene-hBN-graphene heterostructures (GBGH) under a really low out-plane extracting voltage. The external electric field strength applied to the electron sources is only 4 times 10^4V/m and the accelerating voltage as low as 20V is realized. Steady electron emission of over 1nA and operating duration of several hours is observed from the GBGH with size of 59.29um^2 in our experiments, and thus the maximum emission current density reaches 7mA/cm^2. Great electrical contacts, extremely low thickness, and excellent layer properties of two-dimensional (2D) materials lead to easy-fabrication and miniature on-chip electron sources, which would significantly contribute to the development of next-generation free electron devices.

physics.app-ph↗

arXiv2024

16-channel Photonic Solver for Optimization Problems on a Silicon Chip

In this article, we proposed a programmable 16-channel photonic solver for quadratic unconstrained binary optimization (QUBO) problems. The solver is based on a hybrid optoelectronic scheme including a photonic chip and the corresponding electronic driving circuit. The photonic chip is fabricated on silicon on insulator (SOI) substrate and integrates high-speed electro-optic modulators, thermo-optic phase shifters and photodetectors to conduct the 16-dimensional optical vector-matrix multiplication (OVMM). Due to the parallel and low latency propagation of lightwave, the calculation of the QUBO cost function can be accelerated. Besides, the electronic processor is employed to run the heuristic algorithm to search the optimal solution. In the experiment, two 16-dimensional randomly generated QUBO problems are solved with high successful probabilities. To our knowledge, it is the largest scale of programmable and on-chip photonic solver ever reported. Moreover, the computing speed of the OVMM on photonic chip is ~2 TFLOP/s. It shows the potential of fast solving such optimization problems with integrated photonic systems.

cs.ET↗

arXiv2024

Electrically switchable $2^N$-channel wave-front control with N cascaded polarization-dependent metasurfaces

Metasurfaces with tunable functionalities are greatly desired for modern optical system and various applications. To increase the operating channels of polarization-multiplexed metasurfaces, we proposed a structure of N cascaded dual-channel metasurfaces to achieve 2^N electrically switchable functional channels without intrinsic noise or cross-talk. As proof of principles, we have implemented a 3-layer setup to achieve 8 channels. In success, we have demonstrated two typical functionalities of vortex beam generation with switchable topological charge of l=-3 ~ +4 or l=-1~ -8, and beam steering with the deflecting direction switchable in an 8*1 line or a 4*2 grid. We believe that our proposal would provide a practical way to significantly increase the scalability and extend the functionality of polarization-multiplexed metasurfaces, which are potential for the applications of LiDAR, glasses-free 3D display, OAM (de)multiplexing, and varifocal meta-lens.

physics.optics↗

arXiv2024

On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase encoding and Intensity Detection

The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that can solve arbitrary large-scale Ising problems with fast speed remains challenging. In this work, we have proposed and demonstrated the Phase Encoding and Intensity Detection Ising Annealer (PEIDIA) capable of solving arbitrary Ising problems on demand. The PEIDIA employs the heuristic algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation by encoding the Ising spins on the phase term of the optical field and performing intensity detection during the solving process. As a proof of principle, several 20 and 30-spin Ising problems have been solved with high ground state probability (>0.97/0.85 for the 20/30-spin Ising model).

cs.ET↗

arXiv2024

A Programmable and Reconfigurable Photonic Simulator for Classical XY Models

In this work, we proposed and experimentally demonstrated a photonic simulator for XY models, which is a typical kind of classical spin models. By encoding the XY spins on the phase term of the input light field, the corresponding XY Hamiltonian could be performed on the output light intensities. The simulator is mainly based on a programmable and reconfigurable optical vector-matrix multiplication system, which can map arbitrary XY models within the dimensionality limit. Here, we demonstrated the Berezinskii-Kosterlitz-Thouless transition in a two-dimensional XY model, in which the expectation values of some observables are calculated and consistent with the theory. Besides, we performed the ground state search of two 25-spin XY models with different spin connections and coupling strengths. Our proposal paves a new way to investigate the XY spin system.

physics.optics↗

arXiv2024

CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation

Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an intuitive interface for consistent image-to-image (I2I) translation. Various methods have been explored to address this issue, including mask-based methods, attention-based methods, and image-conditioning. However, it remains a critical challenge to enable unpaired I2I translation with pre-trained DMs while maintaining satisfying consistency. This paper introduces Cyclenet, a novel but simple method that incorporates cycle consistency into DMs to regularize image manipulation. We validate Cyclenet on unpaired I2I tasks of different granularities. Besides the scene and object level translation, we additionally contribute a multi-domain I2I translation dataset to study the physical state changes of objects. Our empirical studies show that Cyclenet is superior in translation consistency and quality, and can generate high-quality images for out-of-domain distributions with a simple change of the textual prompt. Cyclenet is a practical framework, which is robust even with very limited training data (around 2k) and requires minimal computational resources (1 GPU) to train. Project homepage: https://cyclenetweb.github.io/

cs.CV↗

arXiv2024

Hyperbolic photonic topological insulators

Topological photonics provides a new degree of freedom to robustly control electromagnetic fields. To date, most of established topological states in photonics have been employed in Euclidean space. Motivated by unique properties of hyperbolic lattices, which are regular tessellations in non-Euclidean space with a constant negative curvature, the boundarydominated hyperbolic topological states have been proposed. However, limited by highly crowded boundary resonators and complicated site couplings, the hyperbolic topological insulator has only been experimentally constructed in electric circuits. How to achieve hyperbolic photonic topological insulators is still an open question. Here, we report the experimental realization of hyperbolic photonic topological insulators using coupled ring resonators on silicon chips. Boundary-dominated one-way edge states with pseudospindependent propagation directions have been observed. Furthermore, the robustness of edge states in hyperbolic photonic topological insulators is also verified. Our findings have potential applications in the field of designing high-efficient topological photonic devices with enhanced boundary responses.

physics.optics↗

arXiv2024

Reconfigurable entanglement distribution network based on pump management of spontaneous four-wave mixing source

Leveraging the unique properties of quantum entanglement, quantum entanglement distribution networks support multiple quantum information applications and are essential to the development of quantum networks. However, its practical implementation poses significant challenges to network scalability and flexibility. In this work, we propose a novel reconfigurable entanglement distribution network based on tunable multi-pump excitation of a spontaneous four-wave mixing (SFWM) source and a time-sharing method. We characterize the two-photon correlation under different pump conditions to demonstrate the effect of pump degenerate and pump non-degenerate SFWM processes on the two-photon correlation, and its tunability. Then as a benchmark application, a 10-user fully-connected quantum key distribution (QKD) network is established in a time-sharing way with triple pump lights. Each user receives one frequency channel thus it shows a linear scaling between the number of frequency channels and the user number in despite of the network topology. Our results thus provide a promising networking scheme for large-scale entanglement distribution networks owing to its scalability, functionality, and reconfigurability.

quant-ph↗

arXiv2023

Inversion-Free Image Editing with Natural Language

Despite recent advances in inversion-based editing, text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency with accuracy; 3) the lack of compatibility with efficient consistency sampling methods used in consistency models. To address the above issues, we start by asking ourselves if the inversion process can be eliminated for editing. We show that when the initial sample is known, a special variance schedule reduces the denoising step to the same form as the multi-step consistency sampling. We name this Denoising Diffusion Consistent Model (DDCM), and note that it implies a virtual inversion strategy without explicit inversion in sampling. We further unify the attention control mechanisms in a tuning-free framework for text-guided editing. Combining them, we present inversion-free editing (InfEdit), which allows for consistent and faithful editing for both rigid and non-rigid semantic changes, catering to intricate modifications without compromising on the image's integrity and explicit inversion. Through extensive experiments, InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40), demonstrating the potential for real-time applications. Project Page: https://sled-group.github.io/InfEdit/

cs.CV↗

arXiv2023

Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments

Linear combination is a potent data fusion method in information retrieval tasks, thanks to its ability to adjust weights for diverse scenarios. However, achieving optimal weight training has traditionally required manual relevance judgments on a large percentage of documents, a labor-intensive and expensive process. In this study, we investigate the feasibility of obtaining near-optimal weights using a mere 20\%-50\% of relevant documents. Through experiments on four TREC datasets, we find that weights trained with multiple linear regression using this reduced set closely rival those obtained with TREC's official "qrels." Our findings unlock the potential for more efficient and affordable data fusion, empowering researchers and practitioners to reap its full benefits with significantly less effort.

cs.IR↗

arXiv2023

Super-compact universal quantum logic gates with inversedesigned elements

Integrated quantum photonic circuit is a promising platform for the realization of quantum information processing in the future. To achieve the largescale quantum photonic circuits, the applied quantum logic gates should be as small as possible for the high-density integration on chips. Here, we report the implementation of super-compact universal quantum logic gates on silicon chips by the method of inverse design. In particular, the fabricated controlled-NOT gate and Hadamard gate are both nearly a vacuum wavelength, being the smallest optical quantum gates reported up to now. We further design the quantum circuit by cascading these fundamental gates to perform arbitrary quantum processing, where the corresponding size is about several orders smaller than that of previous quantum photonic circuits. Our study paves the way for the realization of largescale quantum photonic chips with integrated sources, and can possess important applications in the field of quantum information processes.

physics.optics↗

arXiv2023

Dynamic brain spectrum acquired by a real-time ultra-spectral imaging chip with reconfigurable metasurfaces

Spectral imaging paves way for various fields and particular in biomedical research. However, spectral imaging mainly depending on spatial or temporal scanning, cannot achieve high temporal, spatial and spectral resolution simultaneously. In this study, we demonstrated a silicon real-time ultra-spectral imaging chip based on reconfigurable metasurfaces, comprising of 155,216 (356$\times$436) image-adaptive micro-spectrometers with ultra-high center-wavelength accuracy of 0.04 nm and spectral resolution of 0.8 nm. It is employed for imaging brain hemodynamics, and the dynamic spectral absorption properties of deoxyhemoglobin and oxyhemoglobin in a rat barrel cortex were obtained, which enlighten the spectroscopy in vivo studies and other real-time applications.

physics.optics↗

arXiv2023

Metasurface-Based Free-Space Multi-port Beam Splitter with Arbitrary Power Ratio

A beam splitter (BS) is one of the most critical building blocks in optical systems. Despite various attempts of flat-type BSs to miniaturize the conventional cube BS reported, it remains a challenge to realize an ultrathin optical BS with multi-port output, non-uniform splitting ratio and steerable outgoing directions. Herein, we have demonstrated a free-space optical multi-port beam splitter (MPBS) based on a polarization-independent all-dielectric metasurface. By applying an optimized phase-pattern paradigm via a gradient-descent-based iterative algorithm to amorphous silicon (a-Si) metasurfaces, we have prepared a variety of MPBS samples with arbitrarily predetermined output port number (2~7), power ratio and spatial distribution of output beams. The experimental results reveal that the fabricated MPBSs could achieve high total splitting efficiency (TSE, above 74.7%) and beam-splitting fidelity (similarity, above 78.4%) within the bandwidth of 100 nm (1500~1600 nm). We envision that such MPBS could provide fabulous flexibility for optical integrated system and diverse applications.

physics.optics↗

arXiv2023

Deep-learning-based on-chip rapid spectral imaging with high spatial resolution

Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects. Conventional spectral cameras based on scanning methods suffer from low acquisition speed and large volume. On-chip computational spectral imaging based on metasurface filters provides a promising scheme for portable applications, but endures long computation time for point-by-point iterative spectral reconstruction and mosaic effect in the reconstructed spectral images. In this study, we demonstrated on-chip rapid spectral imaging eliminating the mosaic effect in the spectral image by deep-learning-based spectral data cube reconstruction. We experimentally achieved four orders of magnitude speed improvement than iterative spectral reconstruction and high fidelity of spectral reconstruction over 99% for a standard color board. In particular, we demonstrated video-rate spectral imaging for moving objects and outdoor driving scenes with good performance for recognizing metamerism, where the concolorous sky and white cars can be distinguished via their spectra, showing great potential for autonomous driving and other practical applications in the field of intelligent perception.

eess.IV↗

arXiv2022

Angle-insensitive spectral imaging based on topology-optimized plasmonic metasurfaces

On-chip spectral imaging based on engineered spectral modulation and computational spectral reconstruction provides a promising scheme for portable spectral cameras. However, the angle dependence of modulation units results in the angle sensitivity of spectral imaging, which limits its practical applications. Here, we proposed a design for angle-robust spectral recovery based on a group of topology-optimized plasmonic metasurface units under a 30° field-of-view, and demonstrate angle-insensitive on-chip spectral imaging in the wavelength range of 450 to 750 nm for average polarization. Furthermore, we experimentally verified the angle-insensitive spectral filtering effects of the fabricated metasurface units, and demonstrated angle-robust spectral reconstruction with a fidelity of 98%. Our approach expands the application scale of spectral imaging, and provides a guidance for designing other angle-robust devices.

physics.optics↗