arXiv subjects

Hualiang Zhang

Publications and source records attributed to Hualiang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

A Self-Evolving Agentic Framework for Metasurface Inverse Design

Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering. We present a self-evolving agentic framework that lowers this barrier by coupling a coding agent, explicit human-readable skill files, and a deterministic physics-based evaluator. Rather than updating model weights, it revises the skill files from solver-grounded feedback, while the base model and differentiable solver, which provides the physics simulation and gradients, stay fixed. On a multi-type benchmark, skill evolution raises same-type task success from 38\% to 74\%, the fraction of physical criteria met from 0.51 to 0.87, and reduces average attempts from 4.10 to 2.30. On two new-type families, success holds near ceiling on one (0.92 to 0.90) and rises from 0.20 to 0.90 on the other. Skill evolution offers a practical path toward autonomous and accessible inverse-design workflows.

cs.AI↗

arXiv2026

MCP-Enabled LLM for Meta-optics Inverse Design: Leveraging Differentiable Solver without LLM Expertise

Automatic differentiation (AD) enables powerful metasurface inverse design but requires extensive theoretical and programming expertise. We present a Model Context Protocol (MCP) assisted framework that allows researchers to conduct inverse design with differentiable solvers through large language models (LLMs). Since LLMs inherently lack knowledge of specialized solvers, our proposed solution provides dynamic access to verified code templates and comprehensive documentation through dedicated servers. The LLM autonomously accesses these resources to generate complete inverse design codes without prescribed coordination rules. Evaluation on the Huygens meta-atom design task with the differentiable TorchRDIT solver shows that while both natural language and structured prompting strategies achieve high success rates, structured prompting significantly outperforms in design quality, workflow efficiency, computational cost, and error reduction. The minimalist server design, using only 5 APIs, demonstrates how MCP makes sophisticated computational tools accessible to researchers without programming expertise, offering a generalizable integration solution for other scientific tasks.

physics.comp-ph↗

arXiv2024

Wide Field of View Large Aperture Meta-Doublet Eyepiece

Wide field of view and light weight optics are critical for advanced eyewear, with applications in augmented/virtual reality and night vision. Conventional refractive lenses are often stacked to correct aberrations at wide field of view, leading to limited performance and increased size and weight. In particular, simultaneously achieving wide field of view and large aperture for light collection is desirable but challenging to realize in a compact form-factor. Here, we demonstrate a wide field of view (greater than 60$^\circ$) meta-optic doublet eyepiece with an entrance aperture of 2.1 cm. At the design wavelength of 633 nm, the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece system. This meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye display and night vision systems.

physics.optics↗

arXiv2023

High-fidelity achromatic metalens imaging via deep neural network

Meta-optics are attracting intensive interest as alternatives to traditional optical systems comprising multiple lenses and diffractive elements. Among applications, single metalens imaging is highly attractive due to the potential for achieving significant size reduction and simplified design. However, single metalenses exhibit severe chromatic aberration arising from material dispersion and the nature of singlet optics, making them unsuitable for full-color imaging requiring achromatic performance. In this work, we propose and validate a deep learning-based single metalens imaging system to overcome chromatic aberration in varied scenarios. The developed deep learning networks computationally reconstruct raw imaging captures through reliably refocusing red, green and blue channels to eliminate chromatic aberration and enhance resolution without altering the metalens hardware. The networks demonstrate consistent enhancement across different aperture sizes and focusing distances. Images outside the training set and real-world photos were also successfully reconstructed. Our approach provides a new means to achieve achromatic metalenses without complex engineering, enabling practical and simplified implementation to overcome inherent limitations of meta-optics.

physics.optics↗

arXiv2023

A Scalable, Fast and Programmable Neural Decoder for Fault-Tolerant Quantum Computation Using Surface Codes

Quantum error-correcting codes (QECCs) can eliminate the negative effects of quantum noise, the major obstacle to the execution of quantum algorithms. However, realizing practical quantum error correction (QEC) requires resolving many challenges to implement a high-performance real-time decoding system. Many decoding algorithms have been proposed and optimized in the past few decades, of which neural network (NNs) based solutions have drawn an increasing amount of attention due to their high efficiency. Unfortunately, previous works on neural decoders are still at an early stage and have only relatively simple architectures, which makes them unsuitable for practical QEC. In this work, we propose a scalable, fast, and programmable neural decoding system to meet the requirements of FTQEC for rotated surface codes (RSC). Firstly, we propose a hardware-efficient NN decoding algorithm with relatively low complexity and high accuracy. Secondly, we develop a customized hardware decoder with architectural optimizations to reduce latency. Thirdly, our proposed programmable architecture boosts the scalability and flexibility of the decoder by maximizing parallelism. Fourthly, we build an FPGA-based decoding system with integrated control hardware for evaluation. Our $L=5$ ($L$ is the code distance) decoder achieves an extremely low decoding latency of 197 ns, and the $L=7$ configuration also requires only 1.136 $μ$s, both taking $2L$ rounds of syndrome measurements. The accuracy results of our system are close to minimum weight perfect matching (MWPM). Furthermore, our programmable architecture reduces hardware resource consumption by up to $3.0\times$ with only a small latency loss. We validated our approach in real-world scenarios by conducting a proof-of-concept benchmark with practical noise models, including one derived from experimental data gathered from physical hardware.

quant-ph↗

arXiv2023

A holistically 3D-printed flexible millimeter-wave Doppler radar: Towards fully printed high-frequency multilayer flexible hybrid electronics systems

Flexible hybrid electronics (FHE) is an emerging technology enabled through the integration of advanced semiconductor devices and 3D printing technology. It unlocks tremendous market potential by realizing low-cost flexible circuits and systems that can be conformally integrated into various applications. However, the operating frequencies of most reported FHE systems are relatively low. It is also worth to note that reported FHE systems have been limited to relatively simple design concept (since complex systems will impose challenges in aspects such as multilayer interconnections, printing materials, and bonding layers). Here, we report a fully 3D-printed flexible four-layer millimeter-wave Doppler radar (i.e., a millimeter-wave FHE system). The sensing performance and flexibility of the 3D-printed radar are characterized and validated by general field tests and bending tests, respectively. Our results demonstrate the feasibility of developing fully 3D-printed high-frequency multilayer FHE, which can be conformally integrated into irregular surfaces (e.g., vehicle bumpers) for applications such as vehicle radars and wearable electronics.

eess.SY↗

arXiv2021

Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits

Qubit initialization is a critical task in quantum computation and communication. Extensive efforts have been made to achieve this with high speed, efficiency and scalability. However, previous approaches have either been measurement-based and required fast feedback, suffered from crosstalk or required sophisticated calibration. Here, we report a fast and high-fidelity reset scheme, avoiding the issues above without any additional chip architecture. By modulating the flux through a transmon qubit, we realize a swap between the qubit and its readout resonator that suppresses the excited state population to 0.08% $\pm$ 0.08% within 34 ns (284 ns if photon depletion of the resonator is required). Furthermore, our approach (i) can achieve effective second excited state depletion, (ii) has negligible effects on neighbouring qubits, and (iii) offers a way to entangle the qubit with an itinerant single photon, useful in quantum communication applications.

quant-ph↗

arXiv2021

Wide field-of-view flat lens: an analytical formalism

Wide field-of-view (FOV) optics are widely used in various imaging, display, and sensing applications. While conventional wide FOV optics rely on cascading multiple elements to suppress coma and other aberrations, it has recently been demonstrated that diffraction-limited, near-180 degree FOV operation can be achieved with a single-piece flat fisheye lens designed via iterative numerical optimization [Nano Lett. 20, 7429(2020)]. Here we derive an analytical solution to enable computationally efficient design of flat wide FOV lenses based on metasurfaces or diffractive optical elements (DOEs). Leveraging this analytical approach, we further quantified trade-offs between optical performance and design parameters in wide FOV metalenses.

physics.optics↗

arXiv2021

Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces

Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the near-field coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom placed in a large array with near-field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta-atom and its neighbors as input, and calculates its phase and amplitude in milliseconds. This approach can be applied to explain metasurfaces' performance deterioration caused by mutual coupling and further used to optimize their efficiencies once combined with optimization algorithms. To demonstrate the efficacy of this methodology, we obtain large improvements in efficiency for a beam deflector and a metalens over the conventional design approach. Moreover, we show the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, we envision that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs.

physics.optics↗

arXiv2020

Electrically Reconfigurable Nonvolatile Metasurface Using Low-Loss Optical Phase Change Material

Active metasurfaces promise reconfigurable optics with drastically improved compactness, ruggedness, manufacturability, and functionality compared to their traditional bulk counterparts. Optical phase change materials (O-PCMs) offer an appealing material solution for active metasurface devices with their large index contrast and nonvolatile switching characteristics. Here we report what we believe to be the first electrically reconfigurable nonvolatile metasurfaces based on O-PCMs. The O-PCM alloy used in the devices, Ge2Sb2Se4Te1 (GSST), uniquely combines giant non-volatile index modulation capability, broadband low optical loss, and a large reversible switching volume, enabling significantly enhanced light-matter interactions within the active O-PCM medium. Capitalizing on these favorable attributes, we demonstrated continuously tunable active metasurfaces with record half-octave spectral tuning range and large optical contrast of over 400%. We further prototyped a polarization-insensitive phase-gradient metasurface to realize dynamic optical beam steering.

physics.app-ph↗

arXiv2020

Multi-level Electro-thermal Switching of Optical Phase-Change Materials Using Graphene

Reconfigurable photonic systems featuring minimal power consumption are crucial for integrated optical devices in real-world technology. Current active devices available in foundries, however, use volatile methods to modulate light, requiring a constant supply of power and significant form factors. Essential aspects to overcoming these issues are the development of nonvolatile optical reconfiguration techniques which are compatible with on-chip integration with different photonic platforms and do not disrupt their optical performances. In this paper, a solution is demonstrated using an optoelectronic framework for nonvolatile tunable photonics that employs undoped-graphene microheaters to thermally and reversibly switch the optical phase-change material Ge$_2$Sb$_2$Se$_4$Te$_1$ (GSST). An in-situ Raman spectroscopy method is utilized to demonstrate, in real-time, reversible switching between four different levels of crystallinity. Moreover, a 3D computational model is developed to precisely interpret the switching characteristics, and to quantify the impact of current saturation on power dissipation, thermal diffusion, and switching speed. This model is used to inform the design of nonvolatile active photonic devices; namely, broadband Si$_3$N$_4$ integrated photonic circuits with small form-factor modulators and reconfigurable metasurfaces displaying 2$π$ phase coverage through neural-network-designed GSST meta-atoms. This framework will enable scalable, low-loss nonvolatile applications across a diverse range of photonics platforms.

physics.app-ph↗

arXiv2020

Multifunctional Metasurface Design with a Generative Adversarial Network

Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta-atom structures based on specific requirements (commonly formulated in terms of electromagnetic responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, we present a Generative Adversarial Networks (GAN) that can tackle this problem and generate meta-atom/metasurface designs to meet multifunctional design goals. Unlike conventional trial-and-error or iterative optimization design methods, this new methodology produces on-demand free-form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar electromagnetic responses but different physical features could provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. We demonstrate the network's ability to produce a variety of multifunctional metasurface designs by presenting a bifocal metalens, a polarization-multiplexed beam deflector, a polarization-multiplexed metalens and a polarization-independent metalens.

physics.optics↗

arXiv2020

A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations.

physics.optics↗

arXiv2019

Reconfigurable all-dielectric metalens with diffraction limited performance

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency especially for non mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning covering the full 2$π$ range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling switching of metasurfaces between two arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for active meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 $μ$m wavelength. The metalens is constructed using Ge2Sb2Se4Te1 (GSST), an O-PCM with a large refractive index contrast ($Δ$ n > 1) and unique broadband low-loss characteristics in both amorphous and crystalline states. The reconfigurable metalens features focusing efficiencies above 20% at both states for linearly polarized light and a record large switching contrast ratio of 29.5 dB. We further validated aberration-free imaging using the metalens at both optical states, which represents the first experimental demonstration of a non-mechanical active metalens with diffraction-limited performance.

physics.optics↗

arXiv2019

Novel Balanced Single/Dual-band Bandpass Filters Based on Circular Patch Resonator

A new approach for designing the single-band and dual-band balanced bandpass filters (BPFs) based on the circular patch resonator is proposed in this work. By modifying the output balanced ports of the balanced BPFs to inhibit or transport TM31 mode of the circular patch resonator under the differential-mode (DM) excitation, a single-band or dual-band balanced BPF can realized. As the odd modes could be restrained under the common-mode (CM) excitation, the balanced BPFs could achieve high CM suppression in the DM passbands. Meanwhile, several perturbing ways are introduced to improve the performance of the balanced BPFs. Specifically, perturbation vias are introduced to disturb the electric field distribution to improve the CM suppression in the DM passbands, and slots are cut on the circular patch resonators to perturb the current distribution to modify the second DM passband of the dual-band balanced BPF. Furthermore, the proposed single-band and dual-band balanced BPFs are fabricated and characterized. Good agreement can be achieved between the simulated and measured results of the fabricated balanced BPFs, which validates the feasibility of the proposed design method.

eess.SY↗

arXiv2019

A single-layer panoramic metalens with > 170° diffraction-limited field of view

Wide-angle optical functionality is crucial for implementation of advanced imaging and image projection devices. Conventionally, wide-angle operation is attained by complicated assembly of multiple optical elements. Recent advances in nanophotonics have led to metasurface lenses or metalenses, a new class of ultra-thin planar lenses utilizing subwavelength nanoantennas to gain full control of the phase, amplitude, and/or polarization of light. Here we present a novel metalens design capable of performing diffraction-limited focusing and imaging over an unprecedented > 170 degree angular field of view (FOV). The lens is monolithically integrated on a one-piece flat substrate and involves only a single layer of metasurface that corrects third-order Seidel aberrations including coma, astigmatism, and field curvature. The metalens further features a planar focal plane, which enables considerably simplified system architectures for applications in imaging and projection. We fabricated the metalens using Huygens meta-atoms operating at 5.2 micron wavelength and experimentally demonstrated aberration-free focusing and imaging over the entire FOV. The design concept is generic and can be readily adapted to different meta-atom geometries and wavelength ranges to meet diverse application demands.

physics.optics↗

arXiv2019

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.

physics.optics↗

arXiv2018

Electrically Tunable and Reconfigurable Topological Edge State Lasers

We report an actively tunable topological edge mode laser in a one-dimensional Su-Schrieffer-Heeger (SSH) laser chain, where the SSH chain is realized in an electrically-injected Fabry-Perot (FP) laser chain. A non-Hermitian SSH model is developed to investigate the SSH laser chain with tunable active topological defect. We theoretically demonstrate topological edge mode phase transition in the SSH laser chain. The phase transition manifested in the tight binding SSH laser chain is observed experimentally and agreed well with the theoretical predictions. Finally, by electronically tuning the gain and loss, a lossy topological mode is obtained, and lasing is experimentally observed in the lossy time-reversal configuration. This work presents a versatile platform to investigate novel concepts, such as topological mode, for main stream photonic applications.

physics.optics↗