arXiv subjects

Zhipei Sun

Publications and source records attributed to Zhipei Sun.

At least 19 recordsLinked to original sources

arXiv2026

Multidimensional Light Detection with Symmetry-engineered Heterojunctions

Miniaturised multidimensional light detection, encompassing full-Stokes polarimetry and spectroscopy in ultracompact footprints, is attracting growing interest for its potential to capture a comprehensive set of light properties in portable platforms. Although significant progress has been made in miniaturised schemes for independent polarisation and spectral detection, achieving simultaneous high-dimensional light detection remains an outstanding challenge that limits their development toward full integration. We overcome this limitation by breaking the rotational and inversion symmetries in a symmetry-engineered van der Waals heterojunction to realise an ultracompact multidimensional photodetector. The dual symmetry breaking gives rise to non-trivial quantum geometric and topological features, enabling simultaneous broadband polarisation- and spectrum-resolved light detection, in contrast to previous van der Waals material-based devices, which could detect only one of these modalities. Our device, with an effective area of only 10 micron x 10 micron, reconstructs full-Stokes polarisation with overall root-mean-square errors below 0.05 and resolves spectral peaks separated by 0.4 nm, capabilities not previously achieved in single-pixel detectors. By unifying high-fidelity polarimetry and sub-nanometre spectroscopy in a single electrically tunable junction, our work eliminates the need for cascaded detection architectures and establishes a foundation for multidimensional detector arrays for integrated photonics, quantum information processing, and precision imaging.

physics.optics↗

arXiv2026

Artificial Intelligence in a Photonic Temporal Processor

Optical neural networks (ONNs) promise high-throughput and energy-efficient artificial intelligence, yet essentially all implementations so far encode information across space either in free-space arrays or in integrated waveguide meshes, tying the number of neurons to the number of physical components and fixes the routing topology at fabrication. Here we show that moving the computation into time decouples computational dimension from hardware dimension. Exploiting space-time duality, we implement optical diffraction and interference entirely in time domain, using thin-film lithium niobate modulators as time lenses and temporal masks, with chromatic dispersion providing the coupling between successive temporal neurons. We experimentally verify high-order, complex-valued matrix-matrix multiplications using just a single optical input/output port, scaling the computational dimensions far beyond the channel count. By incorporating optical feedback, we extend this platform into versatile neural networks, where the network layers, neuron numbers, and synaptic connections are fully programmable and in-situ trainable. Our temporal diffractive neural networks are successfully validated on various classification benchmarks, alongside image and video generation tasks. Notably, using this platform we demonstrate an all-analogue generative pipeline in which the latent variable is drawn directly from amplified spontaneous emission, so that no digital sampling or electronic modulation appears anywhere in the generative path. Furthermore, high-resolution images and videos are generated at high frame rates, outperforming state-of-the-art modulator-refresh-limited optical generative systems. These results establish a unified photonic temporal computing framework, providing a scalable and deployable pathway toward next-generation machine intelligence.

physics.optics↗

arXiv2026

A unified resource-pool architecture for high-dimensional direct-detection optical communication

Increasing optical communication capacity without proportionally increasing receiver complexity remains a key challenge for direct-detection links. Conventional systems typically assign wavelength, polarization and intensity to fixed, separately recovered functions, so that alphabet expansion is accompanied by additional demultiplexing, polarization handling, receiver branches and electronic processing. Here we introduce a unified resource-pool architecture for high-dimensional direct-detection optical communication, in which wavelength, polarization and intensity are jointly organized as a composite optical symbol space and recovered through optical-domain joint projection rather than dimension-by-dimension separation. The receiver is implemented with an integrated disordered photonic processor that transforms each composite optical state into a reproducible multi-output electrical fingerprint for single-shot direct recovery. In a dual-wavelength transmission experiment, the system resolves 4096 composite symbols, corresponding to 12 bits per symbol slot, with a bit error rate of 4.25e-4 after 10 km standard-fiber transmission. Additional experiments demonstrate dense polarization alphabets, wavelength-indexed state-space expansion and high-launch-power operation over hollow-core fiber. These results establish disorder-enabled joint projection in an integrated photonic processor as a route to hardware-efficient high-dimensional direct-detection communication beyond conventional dimension-partitioned receiver architecture.

physics.optics↗

arXiv2026

Towards compact high-frequency nonreciprocal devices using nanoplasma-switched time-varying metasurfaces

Time-modulated systems have received growing interest in recent years. They allow us to tailor effects, such as frequency conversion, single-direction propagation, etc. For the microwave band, semiconductor elements, such as varactors, are usually used as time-modulated elements but their modulation frequency has been limited to the few-gigahertz range. Recent advances in nanoplasma switches, i.e., two-state electronic switches based on a gas discharge in a nanometer-scale gap, provide a new potential for developing time-modulated systems with high operating frequencies. Here, we develop an analytical framework based on the time-Floquet method for the design of nonreciprocal time-modulated devices based on two-state time-modulated elements, for instance, nanoplasma-based switches. A practical example of a microwave isolator operating at 100~GHz frequency is developed and studied both analytically and using full-wave simulations. A potential realization in a parallel-plate waveguide is also simulated numerically.

physics.optics↗

arXiv2026

Volumetric Optical Scattering Neural Networks

Optical neural networks offer a route to low-latency and energy-efficient inference by encoding computation in light propagation. However, most existing implementations rely on planar photonic circuits or discretely spaced diffractive layers, restricting volumetric integration and imposing stringent alignment requirements. Here we demonstrate a volumetric optical scattering neural network (OSNN) in which densely packed weak scatterers form a three-dimensional, locally connected optical computing medium. In contrast to fully connected diffractive architectures, the OSNN uses near-field scattering interactions, described under the first-Born approximation, to compress optical interconnections into a monolithic volume. We implement this concept using resilient inverse design and two-photon nanolithography, yielding OSNN devices with a volume of ~$3.8*10^{-4}mm^{3}$ and a record-breaking neuron density of $1.0*10^{9}/mm^{3}$. Experimentally, the fabricated classifier achieves $94.8\%$ blind-test accuracy on MNIST, while the imager performs optical compressed imaging with a $1-{\mu}m$ effective resolution and average FSIM values of $0.93$ on Fashion-MNIST and $0.91$ on VesselMNIST3D. OSNN paves the way for ultra-dense, ultra-compact, and efficient optical computing, creating a universal platform for embedded optical intelligence and promising widespread application in AI fields ranging from autonomous driving to medical diagnosis.

physics.optics↗

arXiv2026

Hamiltonian learning for spin-spiral moir\'e magnets from electronic magnetotransport

Two-dimensional noncollinear magnetic states, such as spin-spiral magnets, offer an excellent platform for investigating fundamental phenomena, with potential for advancing stray-field-free spintronics. However, detection and characterization of noncollinear magnetic states in two-dimensional systems remain challenging, motivating the development of alternative probing methods. Here, we present a methodology for extracting the spin-spiral $\mathbf{q}$ vector from lateral electronic transport measurements. Our approach leverages the magnetic field and bias dependence of the conductance to train a supervised machine learning algorithm, which enables us to extract the $\mathbf{q}$ vectors of arbitrary spin-spiral magnets. We demonstrate that this methodology is robust to the presence of impurities in the system and noise in the conductance data. Our findings show that the conductance pattern reveals a complex dependence on the $\mathbf{q}$ vector of the spin spiral, providing a new strategy to learn magnetic structures directly from transport experiments.

cond-mat.mes-hall↗

arXiv2026

On-chip Multimode Opto-electronic Neural Network

Opto-electronic computing combines the complementary strengths of photonics and electronics to deliver ultrahigh computational throughput with high energy efficiency. However, its practical deployment for real-world applications has been limited by architectures that rely on delicate wavelength management or phase-sensitive coherent detection. Here, we demonstrate the first multimode opto-electronic neural network (MOENN) on a silicon-on-insulator platform. By utilizing orthogonal waveguide eigenmodes as independent information carriers, our architecture achieves robust single-wavelength computation that is inherently immune to spectral crosstalk and phase noise. The fabricated MOENN chip monolithically integrates all functional components, including input encoders, programmable mode-division fan-in/-out units, and most importantly, the nonlinear multimode activation functions. We report the system's versatility through in-situ training via a genetic algorithm, successfully resolving the nonlinear decision boundaries of a two-class dataset and achieving 92.1% accuracy on the Iris classification benchmark. Furthermore, we reconfigure the MOENN into a one-dimensional convolutional neural network, attaining an accuracy of 90.7% on the electrocardiogram-based emotion recognition task. This work establishes a new opto-electronic computing paradigm of simple control and excellent robustness, providing a compelling path toward scalable, deployable photonic intelligence.

physics.optics↗

arXiv2026

Bio-Inspired Photonic Spectral Encoders

Compact spectrometers promise to revolutionize sensing applications, offering a unique pathway to laboratory-grade analysis within a miniaturized footprint. Central to their performance is the encoding strategy to unknown spectra, which determines the efficiency, accuracy, and adaptability of spectral reconstruction. However, the absence of a unified spectral encoding framework has hindered the realization of optimal, high-performance compact spectrometers. We propose a transformative approach: an information-theoretic framework grounded in bio-inspired Bayesian expected information gain that defines the first generic light encoder for computational spectrometers. By optimizing three fundamental attributes at the lowest level of physical hierarchy, (1) orthogonality, (2) completeness, and (3) sparsity, we establish a design paradigm that transcends conventional encoding hardware limitations. We validate this paradigm with the first generic encoder capable of dynamically reconfiguring its response matrices. Experiments show superior reconstruction fidelity across diverse spectral regimes, enabling tunable spectral encoding tailored to varied input features. An ultra-high resolution of 6 pm and a broad measurable bandwidth of 30 nm are experimentally validated. By bridging the gap between theoretical encoding principles and reconfigurable hardware, our framework defines a coherent basis for future advances in compact spectrometry.

physics.optics↗

arXiv2025

Controllable Superconductivity in Suspended van der Waals Materials

Tunable superconductors provide a versatile platform for advancing next-generation quantum technologies. Here, we demonstrate controllable superconductivity in suspended NbSe2 thin layers, achieved through local strain and thermal modulation of the superconducting state. Our results show that suspended NbSe2 structures enable strain modulation of the critical temperature by up to approximately 0.92 K (about 12.5% of the critical temperature) and allow the realization of gate-tunable superconducting critical currents. We further demonstrate configurable hysteretic transport characteristics exhibiting multistability and negative differential resistance, providing easily reconfigurable, spatially dependent superconducting states. These phenomena are well explained by calculations of electron-phonon coupling using density functional theory, together with time-dependent Ginzburg-Landau dynamics coupled to the thermal diffusion equation. Our work provides profound insight into strain and thermal modulation of van der Waals superconductors and opens new opportunities for tunable on-chip superconductor devices, integrated superconducting circuits, and quantum simulators.

cond-mat.supr-con↗

arXiv2025

Observation of tunable chiral spin textures with nonlinear optics

Chiral spin textures, such as spin spirals and skyrmions, are key to advancing spintronics by enabling ultrathin, energy-efficient memory, and high-density data storage and processing. However, their realization remains hindered by the scarcity of suitable host materials and the formidable experimental challenges associated with the characterization of these intricate chiral magnetic states. Here, we report the observation of tunable chiral magnetic textures in van der Waals magnet CrPS$_4$ with nonlinear optics. These tunable textures exhibit strong chiral third-order nonlinear optical responses, driven by interlayer and intralayer spin couplings under varying magnetic fields and temperatures. These pronounced chiral nonlinear optical responses highlight the potency and high sensitivity of the nonlinear optical readout for probing non-collinear magnetic orders. Moreover, our findings position van der Waals magnets and their heterostructures as an exceptional platform for reconfigurable spin-photonics and spintronics, unifying optical, electrical, and magnetic properties through unique intralayer and interlayer spin coupling properties and effective spin interaction between photons and electrons.

cond-mat.mes-hall↗

arXiv2025

Photonics in Flatland: Challenges and Opportunities for Nanophotonics with 2D Semiconductors

Two-dimensional (2D) semiconductors are emerging as a versatile platform for nanophotonics, offering unprecedented tunability in optical properties through exciton resonance engineering, van der Waals heterostructuring, and external field control. These materials enable active optical modulation, single-photon emission, quantum photonics, and valleytronic functionalities, paving the way for next-generation optoelectronic and quantum photonic devices. However, key challenges remain in achieving large-area integration, maintaining excitonic coherence, and optimizing amplitude-phase modulation for efficient light manipulation. Advances in fabrication, strain engineering, and computational modelling will be crucial to overcoming these limitations. This perspective highlights recent progress in 2D semiconductor-based nanophotonics, emphasizing opportunities for scalable integration into photonics.

physics.optics↗

arXiv2025

Photonic Altermagnets: Magnetic Symmetries in Photonic Structures

The unique physical properties of altermagnets, when transplanted to photonic systems, are anticipated to offer a new degree of freedom for engineering electromagnetic waves. Here, we show that a photonic analogue of altermagnetism can be mimicked in photonic crystals, where engineered photonic crystals can host spin space group symmetries. Our approach allows for the creation of spin-split bands and the corresponding transport properties provide an effective platform for circularly polarized light isolation without the need of geometrodynamic spin-orbit interaction. Beyond the concurrent solid-state materials, we anticipate our work to offer photonic crystals as a versatile platform to test the spin-split band properties and inspire optical designs for photospintronic applications.

physics.optics↗

arXiv2025

Direct tensor processing with coherent light

Tensor processing is the cornerstone of modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism, and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here, we report Parallel Optical Matrix Matrix Multiplication (POMMM), a novel paradigm that enables fully parallel tensor processing through a single coherent light propagation. This approach addresses key limitations of current optical methods, scaling the performance with data dimension, while improving theoretical computational power and efficiency. We demonstrate its high consistency with GPU based matrix matrix multiplication across both real-valued and complex valued domains. Moreover, we showcase its adaptability, scalability, and versatility in tensor processing applications such as convolutional and vision transformer neural networks. Furthermore, we analyse the theoretical compatibility and efficiency of POMMM in relation to existing optical computing paradigms, highlighting its potential to outperform current state-of-the-art methods. By enabling a variety of computational tasks and supporting multi2 wavelength and large-scale expansion, POMMM provides a scalable, high-efficient foundation for advancing next-generation optical computing.

physics.optics↗

arXiv2025

Roadmap for Photonics with 2D Materials

Triggered by the development of exfoliation and the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals currently constitute a wide research field protruding in multiple directions in combination with layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary collection of those directions, where 2D materials contribute with polaritons of unique characteristics such as strong spatial confinement, large optical-field enhancement, long lifetimes, high sensitivity to external stimuli (e.g., electric and magnetic fields, heating, and strain), a broad spectral range from the far infrared to the ultraviolet, and hybridization with spin and momentum textures of electronic band structures. The explosion of photonics with 2D materials as a vibrant research area is producing breakthroughs, including the discovery and design of new materials and metasurfaces with unprecedented properties as well as applications in integrated photonics, light emission, optical sensing, and exciting prospects for applications in quantum information, and nanoscale thermal transport. This Roadmap summarizes the state of the art in the field, identifies challenges and opportunities, and discusses future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

cond-mat.mtrl-sci↗

arXiv2025

On-chip integrated light sources with MoS2/WSe2 moir\'e superlattices at telecom wavelengths

On-chip integrated light sources are essential for photonic integrated circuits, requiring waveguides to interface various components, from light sources to detectors. Two-dimensional (2D) transition metal dichalcogenide (TMD) heterostructures offer exceptional tunability and direct bandgaps, opening new avenues for on-chip light sources. However, a waveguide-integrated light source based on 2D materials operating in the telecom windows has yet to be realized. In this work, we demonstrate that the creation of a moir\'e superlattice enables light emission in the optical fiber communication (OFC) O-band (1260-1360 nm) with brightness surpassing that of intralayer excitons in monolayer MoTe2. Furthermore, we realize waveguide-integrated light sources emitting in the O-band by integrating these superlattices with asymmetric nanobeam cavities. This cavity design not only significantly enhances light emission but also improves the spectral purity of the single cavity mode. Moreover, the device output remains remarkably stable across varying pump power in an ambient environment, demonstrating excellent operational stability. The device performance remains unchanged over a nine-month measurement period, highlighting its long-term stability. This work presents a new architecture for on-chip light sources, advancing practical photonic applications.

physics.optics↗

arXiv2025

Phase-matching of high harmonic generation in twisted solids

High harmonic generation (HHG) in solids could enable attosecond and ultraviolet light sources with high compactness, great controllability and rich functions. However, the HHG process is accompanied by a quite large wavevector mismatch that is uncompensated by any traditional phase-matching method, directly limiting its energy conversion efficiency. Here, we propose an effective strategy for phase-matching of HHG with arbitrary harmonic orders in solids. Two flakes of solids with an interlayer twist induce a nonlinear optical phase that depends on the crystal symmetry, twist angle and harmonic order, which can be accurately designed to compensate for the phase mismatch in HHG. Guided by the twist-phase-matching theory, we achieved a record-high conversion efficiency of $~1.5\times10^{-5}$ for the fifth HHG in twisted hexagonal boron nitride crystals with a total thickness of only 1 ${\mu}m$. Our work establishes a foundation for developing ultrashort-wavelength and ultrafast-pulse laser sources in compact solid-state tabletop systems for fundamental and applied sciences.

physics.optics↗

arXiv2025

Electrical probe of spin-spiral order in quantum spin Hall/spin-spiral magnet van der Waals heterostructures

Two-dimensional spin-spiral magnets provide promising building blocks for van der Waals heterostructures due to their tunable spin textures and potential for novel functionalities for quantum devices. However, due to its vanishing magnetization and two-dimensional nature, it is challenging to detect the existence of its noncollinear magnetization. Here, we show that a van der Waals junction based on a spin-spiral magnet and a quantum spin Hall insulator enables obtaining signatures of noncollinear magnetization directly from electrical measurements. Our strategy exploits the sensitivity of helical states to local breaking of time-reversal symmetry, potentially enabling the detection of local magnetic orders even in the absence of net magnetization. We show that the combination of spin-spiral order and nonmagnetic disorder gives rise to scattering in the helical channels that can be directly associated with the spiral exchange coupling and residual nonmagnetic disorder strength. Our results show how electrical transport measurement may offer a way to detect spin-spiral magnets by leveraging helical states in van der Waals heterostructures.

cond-mat.mes-hall↗

arXiv2024

Broadband miniaturized spectrometers with a van der Waals tunnel diode

Miniaturized spectrometers are of immense interest for various on-chip and implantable photonic and optoelectronic applications. State-of-the-art conventional spectrometer designs rely heavily on bulky dispersive components (such as gratings, photodetector arrays, and interferometric optics) to capture different input spectral components that increase their integration complexity. Here, we report a high-performance broadband spectrometer based on a simple and compact van der Waals heterostructure diode, leveraging a careful selection of active van der Waals materials -- molybdenum disulfide and black phosphorus, their electrically tunable photoresponse, and advanced computational algorithms for spectral reconstruction. We achieve remarkably high peak wavelength accuracy of ~2 nanometers, and broad operation bandwidth spanning from ~500 to 1600 nanometers in a device with a ~30x20 {\mu}m2 footprint. This diode-based spectrometer scheme with broadband operation offers an attractive pathway for various applications, such as sensing, surveillance and spectral imaging.

physics.optics↗