The tunnel field-effect transistor (TFET) is considered to be one of the best hopes for achieving a subthermal switch with a subthreshold swing (SS) smaller than the Boltzmann limit, ($k_{B}T$/q) × ln(10), which is 60 mV/dec at room temperature. However, many experimental studies show that the realistic SSs of TFETs are far inferior to the idealized simulation results. To explain the discrepancy between experiments and simulations, we developed a first-principles model of multiphonon-assisted tunneling and calculated the parasitic leakage current induced by phonon-assisted tunneling. The purpose of this work is to show the importance of phonon-assisted transport in the so-called cold-source device designs. The results show that this is an unavoidable intrinsic mechanism and that phonon-assistance effects impose a fundamental limit on the TFET performance.

This paper explores the use of bianisotropic mantle cloaks to reduce the scattering of a dipole antenna at its own operation frequency, preserving the radiating properties in transmission mode. The proposed model is based on the use of generalized sheet transition conditions to derive the susceptibilities that make the metasurface operate as a nonreciprocal transparent cloak characterized by zero scattering for external illumination and full transmission for antenna radiation. The ideal metasurface cloak is then implemented through an active metasurface integrating transistors acting as isolators. Simulation results confirm the proper behavior of the cloaked antenna, which exhibits excellent radiation performances in transmission mode and a low-scattering cross section in receiving mode.

Ferromagnetic-antiferromagnetic (FM-AFM) bilayer dynamics have been a recent topic of interest due to the interaction occurring at the interface, where the magnetic moments of the AFM can be imprinted into the FM, and the exchange bias field can affect these dynamics. Here, we investigate permalloy ($\mathrm{Py}$) and $\mathrm{Ni}\mathrm{O}$ ($\mathrm{Py}$/$\mathrm{Ni}\mathrm{O}$) hybrids and for comparison single $\mathrm{Py}$ films in the broad $\mathrm{Py}$ thickness range varied from few nm to 200 nm by using static (Kerr effect) and dynamic (spin-wave) measurements along with micromagnetic simulations. We observe hybrid modes between uniform (ferromagnetic resonance FMR, $n=0$) and perpendicular standing spin waves (PSSWs, $n=1$, 2) and a clear enhancement of the PSSW mode frequencies upon interfacing $\mathrm{Py}$ with $\mathrm{Ni}\mathrm{O}$ both from experiments and simulations. This enhancement becomes less pronounced as the thickness of the film increases, demonstrating its interfacial origin rooted in the exchange coupling between the FM and AFM layers. Besides, through micromagnetic simulations, we investigate and correlate changes in spatial profiles of the PSSWs with the interfacial exchange coupling. As the thickness is increased, we see that the $n=1$ and $n=2$ modes begin to couple with the fundamental FMR mode, resulting in asymmetric (with respect to the $\mathrm{Py}$ layer center) modes. Our results suggest that PSSW detection in a ferromagnet offers a means of probing the interfacial exchange coupling with the adjacent AFM layer. Furthermore, the controlled spatial symmetry breaking by the AFM layer enables engineering of PSSWs with different spatial profiles in the FM.

Acoustic metasurfaces have paved the way for the ongoing development of compact sound-absorbing devices that possess excellent ventilation capabilities. However, existing absorption mechanisms primarily focus on plane-wave fronts while neglecting omnidirectionally radiated cylindrical waves. In this study, drawing inspiration from wave-interference theory, we propose an approach to absorb cylindrical sound waves by combining artificially decorated passive monopoles and dipoles, which are synthesized by a coiled space resonator and a double-channel resonator, respectively. To demonstrate the effectiveness of our approach, we construct an ultrasparse annular metasurface absorber with an air-channel-area ratio of 46% and a deep-subwavelength thickness. Remarkably, this absorber achieves an absorption performance of 96% (91% in the experiment) for omnidirectionally radiated cylindrical sound waves. This achievement is realized by utilizing a circular array composed of 36 metamolecules arranged in a ringlike grating structure, with each metamolecule supporting synthesized multipoles. The near-unity absorption is accomplished through destructive interference between the composite monopole and dipole, characterized using a one-dimensional impedance tube. This high-efficiency absorption, combined with excellent ventilation and compactness, introduces alternative possibilities for designing functional sound-absorbing devices that can handle low-frequency sound waves with complex wave fronts.

Lateral injection of carriers through semipolar crystallographic planes into c-plane QWs is one of the new frontiers in $\mathrm{III}$-$\mathrm{N}$ light-emitting diodes (LEDs), especially for long wavelengths. Strategic use of V-defects has proven to be the most promising method for lateral injection, and creating optimal V-defect structure and density is an important research area for reducing forward voltage and increasing wall plug efficiency. In this article, we present a novel method for forming V-defects in nominally unstressed low-temperature $\mathrm{Ga}\mathrm{N}$ through the generation of pure edge-dislocation half-loops. We present a detailed material science analysis of the loops via scattering-contrast electron microscopy. The loops have pure-edge character with Burgers vector $1/3⟨11\overline{2}0⟩$, and form in a sessile orientation on $\{11\overline{2}0\}$ a-planes. The two arms of the loops are inclined such that the extra half-planes face down toward the growth substrate. The dislocation loops can be used to intentionally form V-defects through conditions of kinetically limited growth: these conditions also favor nucleation of V-defects at ∼100% of other threading dislocations in the $\mathrm{Ga}\mathrm{N}$ templates. Patterned sapphire substrates (PSS) are one of the most important substrates for $\mathrm{III}$-$\mathrm{N}$ LED growth because of their superior light extraction. However, due to its low threading dislocation density, PSS have not been used extensively for V-defect LEDs. This work provides a pathway for improved control of V-defect formation and density on LEDs grown on sapphire with the goal of enabling uniform lateral injection in these V-defect engineered LEDs with low forward voltage, including PSS for high light extraction.

We study experimentally the processes of parametric excitation in microscopic magnetically saturated disks of nanometer-thick yttrium iron garnet. We show that, depending on the relative orientation of the parametric pumping field and the static magnetization, excitation of either degenerate or nondegenerate magnon pairs is possible. In the latter case, which is particularly important for applications associated with the realization of computation in the reciprocal space, a single-frequency pumping can generate pairs of magnons whose frequencies correspond to different eigenmodes of the disk. We show that, depending on the size of the disk and the modes involved, the frequency difference in a pair can vary in the range 0.1–0.8 GHz. We demonstrate that in this system, one can easily realize a practically important situation where several magnon pairs share the same mode. We also observe the simultaneous generation of up to six different modes using a fixed-frequency monochromatic pumping. Our experimental findings are supported by numerical calculations that allow us to unambiguously identify the excited modes. Our results open new possibilities for the implementation of reciprocal-space computing.

Effective integration of multiple sensory information in the brain is essential to achieve accurate information perception and processing despite the noise and imperfections in biological neural systems, providing strong inspiration for improving computational accuracy in neuromorphic computing. In the future development of brainlike chips, e.g., biomimetic robots, multisensory-information integration is an essential capability for recognizing the external circumstances. Here, based on the computational model proposed by neuroscientists, we propose a hardware implementation scheme of the decentralized multisensory information-integration system using spintronic devices, in which magnetic tunnel junctions are employed as artificial neurons with stochastic dynamics. Using a one-dimensional continuous variable (orientation, head direction, etc.) as a typical example, we demonstrate that the input information from noisy cues is extracted with high accuracy after integration. This spintronic neuromorphic system exhibits remarkable tolerance for both nonuniform devices and the malfunction of different modules. The computational advantages and robustness of the spintronics-based information-integration system provide an important basis for the development of hardware neuromorphic platforms.

We report local time-resolved thermometry in a silicon-nanowire quantum dot device designed to host a linear array of spin qubits. Using two alternative measurement schemes based on rf reflectometry, we are able to probe either local electron or bosonic bath temperatures with microsecond time scale resolution and a noise-equivalent temperature of $3\mathrm{mK}/\sqrt{\mathrm{Hz}}$. Following the application of short microwave pulses, causing local periodic heating, time-dependent thermometry can track the dynamics of thermal excitation and relaxation, revealing clearly different characteristic time scales. This work opens important prospects to investigate the out-of-equilibrium thermal properties of semiconductor quantum electronic devices operating at very low temperature. In particular, it may provide a powerful handle to understand heating effects recently observed in semiconductor spin-qubit systems.

Plasmonic nanostructures offer a prominent foundation for plasmon sensing owing to their capacity to confine light within nanoscale regions and enhance the intensity of the local electromagnetic field. Nevertheless, the development of plasmonic sensors that operate in the midinfrared (MIR) region still presents a notable obstacle, primarily due to the limited detection sensitivity of commercial MIR detectors. To break this bottleneck, we demonstrate the characterization of the plasmonic nanostructures’ MIR spectral response using a time-stretched quantum spectroscopy with frequency upconversion. Quantum spectroscopy of correlated photon pairs offers a novel approach to overcome the limitations associated with the detection sensitivity of localized surface plasmon resonances (LSPRs), particularly under conditions of excessive noise. This scheme not only extends the application of quantum spectroscopy for the plasmonic nanostructures to the MIR regions, but also circumvents the necessity of employing various types of MIR detectors. In addition, by wavelength-to-time mapping and using a single-pixel detector to obtain the MIR spectral response of a plasmonic sample, nonlocal time stretching significantly increases system stability and efficiently reduces the measurement time under a low-light-level illumination. With an integration time of 600 s, time-stretched quantum spectroscopy demonstrates a detection sensitivity of 0.22 photons per pulse. The outcomes of our research pave the way for the realization of enhanced detection sensitivity in the investigation of plasmonic nanostructures in the MIR range.

The heating efficiency of magnetite nanoparticles for therapeutic hyperthermia is shown to be substantially enhanced by applying a uniformly rotating magnetic field in place of a field directed along an axis, when all other factors are held constant. Optimization of the heating efficiency is actively pursued in order to keep the volume fraction of nanoparticles as low as possible, reducing the adverse effects emerging from nanoparticle accumulation in organs. The effect of a rotating magnetic field is calculated by solving rate equations for the magnetic moments of magnetite nanoparticles with predominant Néel relaxation and pictured as double-well systems. The model results in a simple expression for the power density generated by nanoparticles with random easy-axis directions. A thermal model of a tissue simulant is used to show that applying a rotating instead of a linear field permits us to more than halve the dose of nanoparticles needed to attain the target temperature in the tissue.

$\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$ is a promising ultrawide-bandgap semiconductor material for power electronics and optical applications. However, its low hardness and cleavage nature pose major challenges to crystal growth and processing. In this study, $\mathrm{Al}$ alloying is proposed as an effective method to improve the mechanical properties of $\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$ as seed crystals and substrates. First-principle calculations are employed to assess the alloying-induced evolution of crystal structure and mechanical behavior. Elastic constants, ideal shear strengths, and ideal cleavage strengths are all shown to enhance with increasing $\mathrm{Al}$ concentration. Our analysis reveals the possibility of obtaining the mechanically favorable $\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$ seed crystals and substrates with proper $\mathrm{Al}$ alloying, providing theoretical guidance to the development of $\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$ materials and devices.

Compared with periodic systems, quasicrystals without translational invariance exhibit unexpected localization properties. The extended-localized transition in quasicrystals has been observed in both quantum and classical wave systems. However, its manifestation in diffusion systems, which serve as novel platforms for exploring phases of matter in condensed-matter physics, remains unexplored. Here we present an implementation of the extended-localized transition in a diffusive quasicrystal based on a coupled-ring chain structure. By modulating the thermal conductivities of the rings, we obtain the diffusive one-dimensional Aubry-André-Harper (AAH) model, which exhibits an extended-localized transition. Thanks to the ring-shaped chain, we clearly demonstrate the extended-localized transition under uniform excitation through temperature-field simulations. For the localized state, the temperature field clearly demonstrates a phenomenon of multiple localization centers, which has no counterpart in wave systems. We also quantitatively investigate the temperature evolution and size effect of this transition. Furthermore, local excitation is used to demonstrate the temperature field for both the extended state and the localized state. Besides, we implement the non-Hermitian diffusive AAH model by rotating the rings, whose temperature field shows a phenomenon of moving multiple localization centers in the localized phase. Finally, we give experimental suggestions for the diffusive AAH model and propose a potential application named the “double-trace distributed generator.” Our results should facilitate the design of flexible thermal devices and efficient heat management.

Recent advances in twisted photonic structures (e.g., twisted bilayer graphene and twisted anisotropic metasurfaces) have enabled many exotic phenomena of light-matter interactions, such as topological transition of the isofrequency contour of surface waves at the magic angle, photonic flatbands, and localization-to-delocalization transition of light. In general, the optical features of these twisted photonic structures are sensitive to the interlayer twist angle. Here we investigate the mode hybridization of surface waves supported by twisted bilayer anisotropic heterometasurfaces, whose two constituent metasurfaces are nonidentical and support surface waves with similar spatial confinements but distinct polarizations. Counterintuitively, we find that the shape of the isofrequency contour of these hybrid surface waves—eigenmodes with both transverse magnetic and transverse electric wave components—can be insensitive to the twist angle, due to the weak interlayer electromagnetic coupling. Moreover, the interlayer distance provides a unique route to reshape the dispersion of one specific kind of hybrid surface wave, while it has negligible influence on the other kinds. Our finding enriches the physics of light-matter interactions in twisted photonic structures and may open new possibilities to mold the flow of light at the subwavelength scale.

Cavity magnonics, which studies the interaction of light with magnetic systems in a cavity, is a promising platform for quantum transducers and quantum memories. At microwave frequencies, the coupling between a cavity photon and a magnon, the quasiparticle of a spin-wave excitation, is a consequence of the Zeeman interaction between the cavity’s magnetic field and the magnet’s macroscopic spin. For each photon-magnon interaction, a coupling phase factor exists, and this is often neglected in simple systems; however, in “loop-coupled” systems, where there are at least as many couplings as modes, the coupling phases become relevant for the physics and lead to synthetic gauge fields. We present experimental evidence of the existence of such coupling phases by considering two spheres made of yttrium-iron-garnet and two different re-entrant cavities. We predict numerically the values of the coupling phases, and we find good agreement between the theory and the experimental data. These results show that in cavity magnonics, one can engineer synthetic gauge fields, which can be useful for cavity-mediated coupling and engineering dark-mode physics.

Light sails using Earth-based lasers for propulsion require passive stabilization to stay within the beam. This can be achieved through the scattering properties of the sail, creating optical restoring forces and torques. Undamped restoring forces produce uncontrolled oscillations, which could jeopardize the mission, but it is not obvious how to achieve damping in the vacuum of space. Using a simple two-dimensional model, we show that the Doppler effect and relativistic aberration of the propelling laser beam create damping terms in the optical forces and torques. The effect is similar to the Poynting-Robertson effect causing loss of orbital momentum of dust particles around stars but can be enhanced by design of the geometry of the sail.

Extensive research has been conducted on near-field radiative heat transfer (NFRHT) due to its wide range of applications in energy conversion, radiative cooling, and thermal diodes. The main objective of studying NFRHT at the nanoscale gap is to enhance system performance. This research proposes a new approach to designing and optimizing a near-field thermophotovoltaic (NFTPV) system using deep-learning techniques. Our study utilizes a fully connected network (FCN) and an automated-machine-learning (AutoML) model to simulate radiative heat transfer, aiming to improve radiative heat flux, power generation, and overall system efficiency. By comparing two emitter configurations, we find that the hyperbolic emitter outperforms other configurations, as evidenced by its impact on various system-performance parameters. Significant achievements have been made through our investigations. For the $\mathrm{Si}\mathrm{C}$-plate emitter configuration, we have achieved a notable power density of $0.589\mathrm{W}/{\mathrm{cm}}^2$ and an efficiency of 23% after accounting for nonradiative recombination at a temperature difference of 600 K with a 100-nm gap. Moreover, the four-period emitter configuration has yielded even more impressive results, with a power density of $0.9452\mathrm{W}/{\mathrm{cm}}^2$ and an efficiency of 30% after accounting for nonradiative recombination. This study demonstrates the immense potential of utilizing FCN and AutoML for theoretical modeling and optimization of structural parameters in NFTPV systems, as well as providing an accurate model for predicting photocurrent generation. By highlighting the capabilities of these advanced techniques, we have hopefully paved the way for further advancements and innovations in NFRHT.

Antiferromagnetic materials hold potential for use in spintronic devices with fast operation frequencies and field robustness. Despite the rapid progress in proof-of-principle functionality in recent years, there has been a notable lack of understanding of antiferromagnetic domain formation and manipulation, which translates to either incomplete or nonscalable control of the magnetic order. Here, we demonstrate simple and functional ways of influencing the domain structure in $\mathrm{Cu}\mathrm{Mn}\mathrm{As}$ and $\mathrm{Mn}$${}_2$$\mathrm{Au}$, two key materials of antiferromagnetic spintronics research, using device patterning and strain engineering. Comparing x-ray microscopy data from two different materials, we reveal the key parameters dictating domain formation in antiferromagnetic devices and show how the nontrivial interaction of magnetostriction, substrate clamping, and edge anisotropy leads to specific equilibrium domain configurations. More specifically, we observe that patterned edges have a significant impact on the magnetic anisotropy and domain structure over long distances and we propose a theoretical model that relates short-range edge anisotropy and long-range magnetoelastic interactions. The principles invoked are of general applicability to the domain formation and engineering in antiferromagnetic thin films at large, which will hopefully pave the way toward realizing truly functional antiferromagnetic devices.

Dissipationless nonlinearities for three-wave mixing are a key component of many superconducting quantum devices, such as amplifiers and bosonic qubits. So far, such third-order nonlinearities have been primarily achieved with circuits of concatenated Josephson tunnel junctions. In this work, we theoretically develop an alternative approach to realize third-order nonlinearities from gate-tunable and intrinsically symmetry-broken quantum material Josephson junctions. We illustrate this approach on two examples, an Andreev interferometer and a magnetic Josephson junction. Our results show that both setups enable Kerr-free three-wave mixing for a broad range of frequencies, an attribute that is highly desirable for amplifier applications. Moreover, we also find that the magnetic junction constitutes a paradigmatic example for three-wave mixing in a minimal single-junction device without the need for any external biases. We hope that our work will guide the search of dissipationless nonlinearities in quantum material superconducting devices and inspire other ways of characterizing symmetry breaking in quantum materials with microwave techniques.

In the domain of disordered photonics, the characterization of optically opaque materials for light manipulation and imaging is a primary aim. Among various complex devices, multimode optical fibers stand out as cost-effective and easy-to-handle tools, making them attractive for several tasks. In this context, we use these fibers as random hardware projectors, transforming an input dataset into a higher-dimensional speckled image set. The goal of our study is to demonstrate that using such randomized data for classification by training a single logistic regression layer improves accuracy compared to training on direct raw images. Interestingly, we found that the classification accuracy achieved is higher than that obtained with the standard transmission-matrix model, a widely accepted tool for describing light transmission through disordered devices. We conjecture that this improved performance could be due to the hardware classifier operating in a flatter region of the loss landscape when trained on fiber data, which aligns with the current theory of deep neural networks. These findings suggest that the class of random projections operated by multimode fibers generalize better to previously unseen data, positioning them as promising tools for optically assisted neural networks. With this study, we seek to contribute to advancing the knowledge and practical utilization of these versatile instruments, which may play a significant role in shaping the future of neuromorphic machine learning.