arXiv subjects

Wenshan Cai

Publications and source records attributed to Wenshan Cai.

At least 19 recordsLinked to original sources

arXiv2026

Full-Wave Harmonic Balance Framework for Dispersive Time-Varying Photonic Structures

Time-varying photonic structures redistribute electromagnetic energy among Floquet harmonics, enabling frequency conversion, nonreciprocity, parametric gain, and dynamic wavefront control. Accurate modeling of realistic platforms remains challenging when temporal modulation occurs in strongly dispersive materials, because each harmonic experiences a distinct material response while remaining coupled to all others through the modulation. This work introduces a full-wave harmonic-balance framework for dispersive time-varying photonic structures. The formulation solves the steady-state Floquet response in the frequency domain by representing modulated components as induced secondary sources: volumetric polarization densities for bulk media and surface current densities for conductive sheets. Material dispersion and radiation operators are evaluated at each harmonic frequency, whereas temporal modulation enters as off-diagonal convolutional coupling in Floquet space. The framework is validated for a parametrically pumped rolled graphene cylinder, where numerical results agree with an analytical Floquet scattering solution and resolve harmonic-specific scattering, absorption, and near fields. It is further applied to an ITO-based epsilon-near-zero (ENZ) space-time metasurface, enabling shape optimization of a reflective device that converts an optical input into sidebands and redirects them into selected spatial diffraction orders. The approach establishes a general platform for modeling and designing dispersive active photonic structures with harmonic-resolved field, power, absorption, and diffraction observables.

physics.optics↗

arXiv2026

Topologically enhanced optical helicity density in the thermal near field of twisted bilayer van der Waals materials

Twisted van der Waals (vdW) bilayers can support tunable surface/hyperbolic phonon polariton (S/HPhP) depending on the interlayer twist angle. S/HPhPs can be thermally excited and significantly modify the thermal near field. A photonic topological phase transition occurs at a critical twist angle where the polariton dispersion switches from hyperbolic to elliptical. Because the twist angle governs the polariton modes, it is intrinsically linked to the optical helicity density (OHD) of the near-field thermal emission. In this work, a relationship between the OHD of near-field emission and the twist angle of bilayer twisted vdW materials is discovered and investigated. To evaluate the OHD, a 3$\times$3 coherence matrix method is obtained from the fluctuation-dissipation theorem (FDT), which provides a complete description of the thermal electromagnetic field of the twisted bilayer, and a formalism for OHD based on the polarization matrix is employed. The topological transition angle (TTA) is determined by calculating the polariton dispersion relation of the vdW bilayer at different twist angles. A strong correlation between OHD and TTA is observed, which can be attributed to polariton canalization and confined group velocity, leading to enhanced polariton directionality. This study provides new insights into the analysis of angular momentum in near-field thermal radiation from twisted vdW structures.

physics.optics↗

arXiv2026

MxDiffusion: A Physics-Aware Maxwells Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design

We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm.

physics.optics↗

arXiv2025

Nonreciprocal Negative Refraction Enabled by Photonic Time Crystals

We propose and theoretically demonstrate nonreciprocal negative refraction enabled by time-varying photonic structures. By engineering temporal modulations at the interfaces of hyperbolic media, we achieve isolation between forward and backward beams while preserving the hallmark property of negative refraction. Two complementary approaches are developed: in the optical regime, a multilayer AZO/ZnO hyperbolic slab is sandwiched between permittivity-modulated dielectric layers (3D time crystals); in the microwave regime, a wire medium is sandwiched between time-modulated resistive metasurfaces (2D time crystals). Both designs exploit Floquet harmonic expansions and are validated with a customized harmonic-balance finite-element solver. We report isolation exceeding 46 dB in the optical device and ~11 dB in the microwave counterpart. This work introduces a general framework for nonreciprocal negative refraction across frequency regimes, expanding the design space of time-varying metasurfaces and photonic time crystals.

physics.optics↗

arXiv2025

A van der Waals material exhibiting room temperature broken inversion symmetry with ferroelectricity

Since the initial synthesis of van der Waals two-dimensional indium selenide was first documented in 1957, five distinct polymorphs and their corresponding polytypes have been identified. In this study, we report a unique phase of indium selenide via Scanning Transmission Electron Microscopy (STEM) analysis in the synthesized large-area films -- which we have named the $β^\text{p}$ phase. The quintuple layers of the $β^\text{p}$ phase, characterized by a unique zigzag atomic configuration with unequal indium-selenium bond lengths from the middle selenium atom, are distinct from any other previously reported phase of indium selenide. Cross-sectional STEM analysis has revealed that the $β^\text{p}$ layers exhibit intralayer shifting. We found that indium selenide films with $β^\text{p}$ layers display electric-field-induced switchable polarization characteristic of ferroelectric materials, suggesting the breaking of the inversion symmetry. Experimental observations of nonlinear optical phenomena -- Second Harmonic Generation (SHG) responses further support this conclusion. This study reports a $β^\text{p}$ phase of indium selenide showing ferroelectricity over large areas at room temperature in a low-dimensional limit.

cond-mat.mtrl-sci↗

arXiv2025

Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization

Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optimal device, and v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems.

physics.optics↗

arXiv2024

Ion-Assisted Nanoscale Material Engineering in Atomic Layers

Achieving deterministic control over the properties of low-dimensional materials with nanoscale precision is a long-sought goal. Mastering this capability has a transformative impact on the design of multifunctional electrical and optical devices. Here, we present an ion-assisted synthetic technique that enables precise control over the material composition and energy landscape of two-dimensional (2D) atomic crystals. Our method transforms binary transition metal dichalcogenides (TMDs), like MoSe$_2$, into ternary MoS$_{2α}$Se$_{2(1-α})$ alloys with systematically adjustable compositions, $α$. By piecewise assembly of the lateral, compositionally modulated MoS$_{2α}$Se$_{2(1-α)}$ segments within 2D atomic layers, we present a synthetic pathway towards the realization of multi-compositional designer materials. Our technique enables the fabrication of complex structures with arbitrary boundaries, dimensions as small as 30 nm, and fully customizable energy landscapes. Our optical characterizations further showcase the potential for implementing tailored optoelectronics in these engineered 2D crystals.

cond-mat.mtrl-sci↗

arXiv2023

Design of multifunctional color routers with Kerker switching using generative adversarial networks

To achieve optoelectronic devices with high resolution and efficiency, there is a pressing need for optical structural units that possess an ultrasmall footprint yet exhibit strong controllability in both the frequency and spatial domains. For dielectric nanoparticles, the overlap of electric and magnetic dipole moments can scatter light completely forward or backward, which is called Kerker theory. This effect can expand to any multipoles and any directions, re-named as generalized Kerker effect, and realize controllable light manipulation at full space and full spectrum using well-designed dielectric structures. However, the complex situations of multipole couplings make it difficult to achieve structural design. Here, generative artificial intelligence (AI) is utilized to facilitate multi-objective-oriented structural design, wherein we leverage the concept of "combined spectra" that consider both spectra and direction ratios as labels. The proposed generative adversarial network (GAN) is named as DDGAN (double-discriminator GAN) which discriminates both images and spectral labels. Using trained networks, we achieve the simultaneous design for scattering color and directivities, RGB color routers, as well as narrowband light routers. Notably, all generated structures possess a footprint less than 600x600 nm indicating their potential applications in optoelectronic devices with ultrahigh resolution.

physics.optics↗

arXiv2023

Optical Synthesis of Transient Chirality in Achiral Plasmonic Metasurfaces

As much as chiral metasurfaces are significant in stereochemistry and polarization control, tunable chiroptical response is important for their dynamic counterparts. A single metasurface device with invertible chiral states can selectively harness or manipulate both handedness of circularly polarized light upon demand, where in fact chiral inversion in molecules is an active research field. Tactics for chirality switching can be classified into geometry modification and refractive index tuning. However, these generally confront slow modulation speed or restrained refractive index tuning effects in the visible regime with forbidden 'true' inversion. Here, we reconfigure the 'optical' geometry through inhomogeneous spatiotemporal distribution of hot carriers as a breakthrough, transforming a plasmonic achiral metasurface into an ultrafast transient chiral medium with near-perfectly-invertible handedness in the visible. The photoinduced chirality relaxes through the fast spatial diffusion process of electron temperature compared to electron-phonon relaxation, empowering hot-carrier-based devices to be particularly suitable for ultrafast chiroptics.

physics.optics↗

arXiv2022

Polarimetric analysis of thermal emission from both reciprocal and nonreciprocal materials using fluctuational electrodynamics

Coherent thermal emission for a given polarization has been observed in many metamaterials with micro/nanostructures. A complete description of the thermal emission requires the full characterization of the spectral angular emissivity for all polarization states. Emissivity is typically obtained based on the equivalence between the absorptivity and emissivity according to Kirchhoff's law; however, such relation may be invalid for nonreciprocal media. More general approaches without the constrain of optical reciprocity are necessary when dealing with magneto-optical materials and magnetic Weyl semimetals. Here, a polarimetric analysis of thermal emission is carried out based on fluctuational electrodynamics. The Stokes parameters are obtained using coherency matrix for a multilayered system with anisotropic media, including nonreciprocal materials. The results demonstrate that thermal emission may be circularly or linearly polarized in different directions and frequencies. The findings are consistent with the statements of the modified Kirchhoff's law provided by several groups in recent years, and therefore, justify the appropriateness of both the direct and indirect methods. This study will help the design of desired thermal emitters for energy harvesting and thermal control.

physics.optics↗

arXiv2021

Electrically driven programmable phase-change meta-switch reaching 80% efficiency

Despite recent advances in active metaoptics, wide dynamic range combined with high-speed reconfigurable solutions is still elusive. Phase-change materials (PCMs) offer a compelling platform for metasurface optical elements, owing to the large index contrast and fast yet stable phase transition properties. Here, we experimentally demonstrate an in situ electrically-driven reprogrammable metasurface by harnessing the unique properties of a phase-change chalcogenide alloy, Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST), in order to realize fast, non-volatile, reversible, multilevel, and pronounced optical modulation in the near-infrared spectral range. Co-optimized through a multiphysics analysis, we integrate an efficient heterostructure resistive microheater that indirectly heats and transforms the embedded GST film without compromising the optical performance of the metasurface even after several reversible phase transitions. A hybrid plasmonic-PCM meta-switch with a record electrical modulation of the reflectance over eleven-fold (an absolute reflectance contrast reaching 80%), unprecedented quasi-continuous spectral tuning over 250 nm, and switching speed that can potentially reach a few kHz is presented. Our work represents a significant step towards the development of fully integrable dynamic metasurfaces and their potential for beamforming applications.

physics.optics↗

arXiv2021

Fast Non-line-of-sight Imaging with Two-step Deep Remapping

Conventional imaging only records photons directly sent from the object to the detector, while non-line-of-sight (NLOS) imaging takes the indirect light into account. Most NLOS solutions employ a transient scanning process, followed by a physical based algorithm to reconstruct the NLOS scenes. However, the transient detection requires sophisticated apparatus, with long scanning time and low robustness to ambient environment, and the reconstruction algorithms are typically time-consuming and computationally expensive. Here we propose a new NLOS solution to address the above defects, with innovations on both equipment and algorithm. We apply inexpensive commercial Lidar for detection, with much higher scanning speed and better compatibility to real-world imaging. Our reconstruction framework is deep learning based, with a generative two-step remapping strategy to guarantee high reconstruction fidelity. The overall detection and reconstruction process allows for millisecond responses, with reconstruction precision of millimeter level. We have experimentally tested the proposed solution on both synthetic and real objects, and further demonstrated our method to be applicable to full-color NLOS imaging.

cs.CV↗

arXiv2020

Dynamic hybrid metasurfaces

Efficient hybrid plasmonic-photonic metasurfaces that simultaneously take advantage of the potential of both pure metallic and all-dielectric nanoantennas are identified as an emerging technology in flat optics. Nevertheless, post-fabrication tunable hybrid metasurfaces are still elusive. Here, we present a reconfigurable hybrid metasurface platform by incorporating the phase-change material Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST) into metal-dielectric meta-atoms for active and non-volatile tuning of properties of light. We systematically design a reduced-dimension meta-atom, which selectively controls the fundamental hybrid plasmonic-photonic resonances of the metasurface via the dynamic change of optical constants of GST without compromising the scattering efficiency. As a proof-of-concept, we experimentally demonstrate miniaturized tunable metasurfaces that control the amplitude and phase of incident light necessary for high-contrast optical switching and anomalous to specular beam deflection, respectively. Finally, we leverage a deep learning-based approach to present an intuitive low-dimensional visualization of the enhanced range of response reconfiguration enabled by the addition of GST. Our findings further substantiate dynamically tunable hybrid metasurfaces as promising candidates for the development of small-footprint energy harvesting, imaging, and optical signal processing devices.

physics.optics↗

arXiv2020

Multifunctional Meta-Optic Systems: Inversely Designed with Artificial Intelligence

Flat optics foresees a new era of ultra-compact optical devices, where metasurfaces serve as the foundation. Conventional designs of metasurfaces start with a certain structure as the prototype, followed by an extensive parametric sweep to accommodate the requirements of phase and amplitude of the emerging light. Regardless of how computation-consuming the process is, a predefined structure can hardly realize the independent control over the polarization, frequency, and spatial channels, which hinders the potential of metasurfaces to be multifunctional. Besides, achieving complicated and multiple functions calls for designing a meta-optic system with multiple cascading layers of metasurfaces, which introduces super exponential complexity. In this work we present an artificial intelligence framework for designing multilayer meta-optic systems with multifunctional capabilities. We demonstrate examples of a polarization-multiplexed dual-functional beam generator, a second order differentiator for all-optical computation, and a space-polarization-wavelength multiplexed hologram. These examples are barely achievable by single-layer metasurfaces and unattainable by traditional design processes.

physics.app-ph↗

arXiv2019

A topological encoding method for data-driven photonics inverse design

Data-driven approaches have been proposed as effective strategies for the inverse design and optimization of photonic structures in recent years. In order to assist data-driven methods for the design of topology of photonic devices, we propose a topological encoding method that transforms photonic structures represented by binary images to a continuous sparse representation. This sparse representation can be utilized for dimensionality reduction and dataset generation, enabling effective analysis and optimization of photonic topologies with data-driven approaches. As a proof of principle, we leverage our encoding method for the design of two dimensional non-paraxial diffractive optical elements with various diffraction intensity distributions. We proved that our encoding method is able to assist machine-learning-based inverse design approach for accurate and global optimization.

physics.optics↗

arXiv2019

Compounding meta-atoms into meta-molecules with hybrid artificial intelligence techniques

Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, meta-molecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of meta-molecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multi-element systems impede an effective strategy for the design and optimization of meta-molecules. Here, we propose a hybrid artificial intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of meta-molecules in metasurfaces. The framework breaks the design of the meta-molecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. We leverage the proposed framework to design metallic meta-molecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner.

physics.optics↗

arXiv2019

A Hybrid Strategy for the Discovery and Design of Photonic Nanostructures

Designing complex physical systems, including photonic structures, is typically a tedious trial-and-error process that requires extensive simulations with iterative sweeps in multi-dimensional parameter space. To circumvent this conventional approach and substantially expedite the discovery and development of photonic structures, here we develop a framework leveraging both a deep generative model and a modified evolution strategy to automate the inverse design of engineered nanophotonic materials. The capacity of the proposed methodology is tested through the application to a case study, where metasurfaces in either continuous or discrete topologies are generated in response to customer-defined spectra at the input. Through a variational autoencoder, all potential patterns of unit nanostructures are encoded into a continuous latent space. An evolution strategy is applied to vectors in the latent space to identify an optimized vector whose nanostructure pattern fulfills the design objective. The evaluation shows that over 95% accuracy can be achieved for all the unit patterns of the nanostructure tested. Our scheme requires no prior knowledge of the geometry of the nanostructure, and, in principle, allows joint optimization of the dimensional parameters. As such, our work represents an efficient, on-demand, and automated approach for the inverse design of photonic structures with subwavelength features.

physics.optics↗

arXiv2019

Metasurfaces for near-eye augmented reality

Augmented reality (AR) has the potential to revolutionize the way in which information is presented by overlaying virtual information onto a person's direct view of their real-time surroundings. By placing the display on the surface of the eye, a contact lens display (CLD) provides a versatile solution for compact AR. However, an unaided human eye cannot visualize patterns on the CLD simply because of the limited accommodation of the eye. Here, we introduce a holographic display technology that casts virtual information directly to the retina so that the eye sees it while maintaining the visualization of the real-world intact. The key to our design is to introduce metasurfaces to create a phase distribution that projects virtual information in a pixel-by-pixel manner. Unlike conventional holographic techniques, our metasurface-based technique is able to display arbitrary patterns using a single passive hologram. With a small form-factor, the designed metasurface empowers near-eye AR excluding the need of extra optical elements, such as a spatial light modulator, for dynamic image control.

cs.GR↗