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Interfacial structural modification in Cu/low- <i>k</i> interconnects revealed by atomic force microscopy infrared spectroscopy
Process-induced modification confined to the immediate vicinity of Cu/low-k interfaces is a key issue in advanced interconnects, yet direct vibrational-band-specific characterization of this region on the tens-of-nanometers scale remains difficult. Here, resonance-enhanced atomic force microscopy (AFM) infrared spectroscopy has been applied from the upper surface of a practical damascene Cu/low-k interconnect structure comprising 330 nm wide Cu lines separated by 270 nm in a 200 nm thick, porous SiOC-based dielectric film, enabling direct comparison of the central low-k region and the interface-adjacent region. Local spectroscopy together with a 15-point line scan at an 18 nm step interval revealed reduced Si–CH3-related and Si–H-related vibrational responses and an enhanced OH-related response, including possible contributions from Si–OH, hydrogen-bonded hydroxyl species, and adsorbed water, near the Cu/low-k interface, with the most pronounced methyl-related change observed at the first low-k measurement point adjacent to the boundary. This direct nanoscale identification of interfacial structural modification has been enabled by resonance-enhanced photothermal detection and AFM-based positional control and has been demonstrated directly on a practical top-surface device structure without preparing a special cross-sectional specimen. The observed interfacial chemistry is consistent with localized demethylation, modification of Si–H-related environments, and increased hydroxyl/water-related character and is, therefore, consistent with the formation of a locally higher-k interfacial zone, which is expected to contribute to increased capacitance, leakage risk, and breakdown susceptibility in practical low-k interconnects.
Suppressing anisotropy in nanostructured Fe/Ni superlattices with monolayer periodicity
We described here the growth of carefully engineered Fe/Ni(100) superlattices with monolayer periodicity (i.e., Fe50Ni50) on a Cu(100) crystalline substrate, nanostructured to 70 × 40 nm islands. The nanometer-thick, islanded Fe/Ni(100) superlattices are characterized in terms of composition, crystalline structure, and magnetic properties and show a magnetic behavior perfectly isotropic in-plane, with negligible effective anisotropy out of plane because the strongly reduced shape anisotropy due to the single-domain islands almost exactly compensates the perpendicular interfacial anisotropy due to the Fe/Ni layering. The suppression of anisotropy is achieved without nanolithography processes or in situ application of larger magnetic fields. The nanostructured films show a small coercivity [although 20 times larger than the ones grown on flat Cu(100) substrates]. The reduced shape anisotropy in the nanostructured superlattice accounts for the observed suppression of the effective anisotropy in-plane and out-of-plane.
Enhancing optical confinement in hybrid surface plasmonic nanocavities via external mode coupling near avoided resonance crossings
Plasmonic devices have been intensively studied in the past few decades. However, the substantial intrinsic ohmic losses imposed by noble metals highly limited further applications of such devices. Herein, we demonstrate the formation of long-lived states via external mode coupling in coupled hybrid surface plasmonic nano-resonators in which improved Q factors have been achieved. By systematically engineering the spatial intervals between three silicon nanorods on a silver substrate, we demonstrate the formation of long-lived states through destructive interference of optical fields. Numerical simulations reveal that, at a critical spacing, the Q factor of a resonant mode is significantly boosted to approximately 450—an eightfold enhancement compared to isolated single-rod resonators. Notably, this drastic improvement in photon lifetime is achieved while maintaining an ultrasmall effective mode volume Veff of the order of 10−4 μm3. These findings provide an alternative strategy to material optimization for mitigating metallic losses, offering a robust framework for developing high-efficiency, low-threshold plasmonic nanolasers, and advanced on-chip photonic components.
Influence of implantation and annealing temperatures on electrical property of low-dose implanted/annealed SiC
Terahertz time-domain ellipsometry was used to examine the electrical properties of low-dose implanted/annealed silicon carbide (SiC) subjected to different implantation and annealing temperatures. P- and Al-implantations were performed at a dose of 2 × 1013 cm−2 and at implantation temperatures of 30 and 500 °C, and the samples were subsequently annealed at 1200 and 1600 °C. For both the P- and Al-implanted/annealed SiC, resistivity was halved when the implantation temperature was increased from 30 to 500 °C. The decrease in resistivity is attributed to the suppression of implantation-induced damage at 500 °C. The resistivity of the P-implanted/annealed SiC decreased drastically as the annealing temperature was increased from 1200 to 1600 °C owing to electrical activation. However, for the Al-implanted/annealed SiC, the resistivity showed little change with increasing annealing temperature. The high ionization energy of Al is considered to have inhibited the reduction in resistivity. These results demonstrate the importance of controlling the implantation and annealing temperatures, even for low-dose implantation.
Pronounced bias-enhanced photocurrent with strong polarization sensitivity in 1T-TiSe2-based photodetectors
We report a 1T-TiSe2-based photodetector with pronounced polarization sensitivity and broadband visible-light photoresponse under low-bias conditions. Using a combined density-functional theory and nonequilibrium Green’s function approach, we calculate the transmission spectra and photocurrent under linearly polarized illumination. The photocurrent exhibits strong dependences on both photon energy and polarization angle and can be enhanced by up to two orders of magnitude under applied bias, with a concomitant increase in the extinction ratio. Analysis of the transmission spectra identifies the specific electronic transitions responsible for the photocurrent maxima. These results demonstrate an effective electrical approach to enhancing the photoresponsivity of low-dimensional photodetectors.
Growth window and wurtzite phase stability of molecular-beam-epitaxial ScInN thin films with composition-dependent photoluminescence
We establish the growth window and wurtzite phase stability of plasma-assisted molecular-beam-epitaxial ScInN thin films over a broad composition range. Lower growth temperatures suppress rock salt phase formation and enable wurtzite ScInN without detectable rock salt contributions up to x(Sc) = 0.36. X-ray diffraction reveals systematic composition-dependent changes in phase formation and lattice parameters. We observe a pronounced anisotropic lattice evolution, with the in-plane lattice parameter a showing only a minor dependence on scandium content, whereas a decrease in the out-of-plane lattice parameter c is more pronounced. This trend is consistent with results from density functional theory calculations we performed using the SCAN functional, as well as Raman spectroscopy measurements showing a strong shift of the A1(LO) modes and a much smaller shift of the E2 mode. Photoluminescence is observed across the investigated alloy series, with the maximum emission shifting from 0.85 eV for InN to 2.07 eV for x(Sc) = 0.40. These results establish molecular-beam-epitaxial ScInN as an experimentally accessible alloy system with an extended wurtzite stability range, a composition-dependent optical response, and a nearly constant in-plane lattice parameter enabling heterostructure design with reduced lattice mismatch.
Oxygen concentration effect on the behavior of point defects in YBa2Cu3O6.5−7: A first-principles study
Understanding defect behavior in high-temperature superconducting magnet materials, such as yttrium barium copper oxide (YBa2Cu3O7−δ), is vital for predicting changes in their superconducting properties during operation in compact fusion reactors. First-principles density functional theory (DFT) calculations are used to investigate the energetics of point defects in oxygen-deficient YBa2Cu3Ox (6.5 ≤ x &lt; 7) in comparison to YBa2Cu3O7. With decreasing oxygen stoichiometry, the formation energies of oxygen vacancies generally increase, while those of interstitials decrease. The formation energies of single cation defects as a function of oxygen stoichiometry are similar. Oxygen is predicted to diffuse along b-axes via a vacancy mechanism with an activation energy of around 0.5 eV. The diffusion activation energy of oxygen defects in oxygen-deficient YBa2Cu3Ox is larger than that in YBa2Cu3O7 without oxygen vacancies. The mean recombination energy of oxygen Frenkel pairs slightly increases with decreasing oxygen concentration. The formation energy of Frenkel pairs in oxygen-deficient YBa2Cu3Ox, particularly at x ≈ 6.9, is lower relative to YBa2Cu3O7, resulting in a higher number of irradiation defects in YBa2Cu3Ox; additionally, full-chain oxygen vacancy configurations in YBa2Cu3Ox (x = 6.75, 6.50) enhance material stability under irradiation relative to the x ≈ 6.9 structure.
Three-dimensional kinetic Monte Carlo modeling of disordered organic semiconductor devices with molecular doping
Molecular doping plays a pivotal role in enhancing the performance of organic light-emitting diodes, particularly by facilitating charge injection across large energy barriers at the electrode/organic interfaces. Recently, it has been shown that the Schottky-contact formation between metal electrodes and molecularly doped disordered organic semiconductors leads to modified charge-carrier mobilities compared to undoped bulk materials, smaller depletion widths than inorganic semiconductors, as well as ultraslow charge-carrier dynamics [Yu et al., Phys. Rev. Appl. 19, 024041 (2023)]. These results bring up the question whether conventional one-dimensional drift–diffusion approach can accurately model devices with doped injection layers. In this work, to gain a comprehensive understanding of the microscopic mechanisms involved, we systematically investigate disordered organic semiconductor devices with molecular doping using three-dimensional kinetic Monte Carlo simulations and compare with a one-dimensional drift–diffusion model. We find that for full-device simulations, (a) one-dimensional drift–diffusion approach overestimates current density (without special treatment of mobility in the doped layer); (b) one-dimensional drift–diffusion approach overestimates the depletion layer width; and (c) although the dynamics of carrier profiles could be slow (∼10−5 s), the device current density can reach a steady state much faster (∼10−6 s). The three-dimensional kinetic Monte Carlo simulations are employed to study both single doped layers and bipolar doped layers (P–N junctions) and are shown to well reproduce experimental current density–voltage characteristics.
Revealing the effects of particle shape and distribution on the direct wafer-to-wafer bonding dynamics
Particle contamination control is one of the most critical challenges in wafer-to-wafer (W2W) hybrid bonding technology. Particles induce voids at the bonding interface, thereby compromising the bonding quality and reliability. However, the effects of particle shape and distribution on the W2W pre-bonding process have not yet been investigated. In this work, three-dimensional (3D) W2W hybrid bonding analysis involving particles was conducted. The influence of particle on bonding front propagation was analyzed. Besides, the effects of particle shape and position on bonding time, interfacial void size, and upper wafer von Mises stress were quantitatively evaluated. The results show that the presence of particle leads to the split of the bonding front and a pronounced delay in bonding time. Compared with the cylindrical particle, the square particle results in a longer bonding delay (4.4 s vs 3.1 s) and produces a larger void height (17.1 μm vs 11.1 μm). Moreover, it is found that particle contamination significantly amplifies the void defects. Particle position is found to have a non-monotonic influence on bonding dynamics. As the particle location gradually moves away from the wafer center, the area and height of void, as well as the total bonding time exhibit a trend of first increasing and then decreasing. Specifically, a cylindrical particle located at 70–80 mm from wafer center generates the largest void area (up to 2350 mm2), the highest void height (14.8 μm), and the longest bonding time (3.6 s). This study offers a quantitative guidance for contamination control in 3D heterogeneous integration technology.
Effect of ions on the ultrafast dynamics of electron–phonon and phonon–phonon interactions in gold nanospheres in water
Modulating localized surface plasmon resonances (LSPRs) plays an essential role in the incorporation of metal nanoparticles (MNPs) in various applications. For instance, the ability to tune the LSPR frequency by varying the size and shape of MNPs is crucial for photodetection and imaging applications. This work reports on the effect of tuning the ionic strength around gold (Au) NPs on the dynamics of electron–phonon and phonon–phonon interactions. Steady-state and transient absorption spectroscopies are employed to investigate the effect of NaCl salt on LSPRs in Au-NPs in water. The results indicated that the presence of the Na+ and Cl− ions leads to a ∼3 nm-“red” shift of the plasmon resonance, and the formation of weak broad absorption bands on the wings of the LSPR peak. Pump–probe experiments show that, after excitation of Au-NPs at ∼400 nm, the lifetimes of electron–phonon and phonon–phonon interactions increase linearly with NaCl molarity, which is similar to their dependence on excitation power and, thus, on the density of photogenerated charge carriers. These effects are attributed to the change of the dielectric environment induced by the presence of Na+ and Cl− ions on the surface of Au-NPs, which alters the plasmon damping rates, and the change in the lifetimes of electron–phonon and phonon–phonon interactions is assigned to modifications of the surface electron density. Adjusting LSPR dynamics by tuning the ionic strength of the surroundings opens new horizons for the application of plasmonic devices with enhanced functionality.
On the amorphous to crystalline structure transition of oxidized LEAs
Oxide crystallization in multicomponent alloys is often delayed relative to mono-metals, yet the mechanisms underlying this behavior remain incompletely understood. Here, we investigate the relationship between oxygen saturation and crystallization in thin-film mono-metals and low-entropy alloys (LEAs) by introducing two temperatures in our annealing experiment: the thickness saturation temperature (Tf), marking completion of oxygen uptake, and the oxide crystallization temperature (T2), marking the onset of crystalline oxide formation. We show that mono-metals typically crystallize prior to or concurrent with saturation (T2 ≤ Tf), whereas LEAs generally show the opposite (T2 &gt; Tf), indicative of delayed oxide crystallization. Therefore, we define the difference (T2 − Tf) as the crystallization delay strength (CDS), providing a quantitative metric for comparing oxide crystallization behavior across compositions. By correlating key oxide uptake saturation and crystallization temperatures (Tf, T2, thus also CDS) with bond-character descriptors derived from the Van Arkel–Ketelaar model, we show that bond ionicity and covalency systematically influence oxygen saturation and crystallization delay. We report that LEAs with higher ionicity show lower saturation temperatures Tf and increased crystallization delay, while more covalent alloys crystallize without significant CDS. These results establish bond character as a predictive descriptor for oxide crystallization behavior in alloy thin films.
Physics-constrained inverse estimation of irradiation-induced strain in He–H ion-implanted 4H-SiC using nanoindentation and finite element modeling
Nanoindentation is widely used to evaluate the mechanical properties of irradiated materials; however, its potential for quantifying irradiation-induced subsurface strain remains underexplored. In this work, an integrated experimental–numerical framework based on a physics-constrained inverse modeling approach is employed to estimate the magnitude of a depth-dependent irradiation-induced strain distribution in single-crystal 4H-SiC following sequential He and H ion implantation. The approach combines depth-sensing nanoindentation, finite element modeling (FEM), and a simplex-based inverse optimization routine to calibrate a physically motivated eigenstrain profile derived from ion-damage simulations. The strain field is assumed to follow a lognormal distribution consistent with independently determined damage profiles (stopping and range of ions in matter) and is implemented in the FEM model through a depth-dependent thermal expansion formulation. By minimizing the squared error between simulated and experimental force–displacement curves, the peak tensile strain is estimated to be ∼0.91%, accompanied by an effective Young's modulus of 310 GPa and a yield strength of 16.4 GPa. Independent validation by nano-beam precession electron diffraction confirms good agreement between the reconstructed and experimentally measured out-of-plane strain profiles in both magnitude and spatial distribution. The results demonstrate that nanoindentation, when combined with physics-based inverse modeling, can provide a practical tool for quantifying irradiation-induced strain and residual stress in nuclear ceramics. This methodology offers a complementary approach to diffraction-based techniques for assessing subsurface damage in ion-irradiated materials relevant to advanced nuclear systems.
Spin Edelstein effect in irradiated topological insulator thin films
We investigate the low-temperature spin Edelstein effect in topological insulator thin films subjected to circularly polarized light. Using a high-frequency Floquet formalism, we derive an effective Hamiltonian that captures light-induced renormalization of the mass gap and Fermi velocity. Based on this model, we compute the spin Edelstein susceptibilities using the Kubo linear-response theory, accounting for both intraband and interband contributions. Our results reveal that optical driving enables strong tunability of spin polarization, with critical behavior emerging at the Floquet-engineered band structure where the effective Fermi velocity vanishes. We further demonstrate that surface asymmetry, hybridization strength, and interband relaxation processes play crucial roles in shaping the spin responses. These findings suggest promising routes for dynamically controlling spin accumulation in topological materials via light, offering new opportunities for spintronic applications.
Precipitation of Y-rich nano-oxides in FeCr alloy: An ion beam synthesis study
Y-rich nano-oxide precipitates were successfully produced by ion beam synthesis in high purity Fe–10%Cr. After annealing at 1100 °C of Y and O ion-implanted FeCr, a very high density (&gt;1023 m−3) of small nano-oxides that are fairly uniform in size is achieved. A careful analysis of the structure and composition of these precipitates using HRTEM, STEM-HAADF (scanning transmission electron microscopy coupled with high-angle annular dark-field), and STEM-EDX (scanning transmission electron microscopy coupled with energy dispersive x-ray spectroscopy) demonstrates characteristics that are similar to those of the nano-oxides in non-Ti-doped oxide dispersion strengthened (ODS) steels. Both cubic and monoclinic Y2O3 precipitates are identified, and a Cr-rich shell has begun to form around them. In contrast to precipitates with a cubic structure, those with a monoclinic structure are observed to exhibit large distortions. Comparison of the precipitate characteristics with those from a previous study involving sequential Y, Ti, and O ion implantation into the same material indicates that the additional presence of Ti substantially reduces the size of the precipitates, consistent with observations in conventionally produced ODS steels, and stabilizes the cubic structure of yttria at the expense of the monoclinic structure. The present ion beam synthesis investigation demonstrates that the mechanism of Ti-stimulated precipitate refinement is not related to the specific precipitating structure because Ti addition promotes smaller Y–Ti precipitates as compared to pure Y2O3 ones even when these Y–Ti oxide phases have crystallographic structure and lattice parameters matching those of pure cubic yttria.
Reversing chiral selectivity in optical trapping via Babinet complementary metasurfaces
We systematically investigate the chiral trapping mechanism of Babinet complementary plasmonic metasurfaces. The original structure and its complementary one exhibit naturally opposite chiral selectivity, which can be dynamically flipped by modulating the polarization angle of the incident light. The results reveal a competitive mechanism between the electromagnetic trapping potential and the chiral trapping potential. At a critical parameter, the two trapping potentials exactly cancel, leaving chiral nanoparticles completely free. Moreover, the maximum total trapping potential shifts from the fully chiral extrema (κ = ±1) to an intrinsically preferred optimal chirality parameter (κopt ≈ ±0.7). Our results advance the understanding of chiral optical trapping and provide a theoretical foundation for chiral sorting using a single geometry and its complement.
Repulsive magnetic field-enhanced laser-induced plasma-assisted ablation for high-efficiency and high-quality sapphire microfabrication
Nanosecond laser microfabrication of sapphire remains challenging due to its broadband optical transparency and pronounced thermal effects. In this paper, a repulsive magnetic field-enhanced laser-induced plasma ablation (LIPAA) technique is proposed to improve processing efficiency and fabrication accuracy. The processing quality of sapphire microstructures is investigated at different magnetic induction intensities. The repulsive magnetic field effectively suppresses the horizontal expansion of plasma plume, forming deeper and narrower ablation craters. At the optimal magnetic induction intensity of 400 mT, the sapphire microfabrication achieves a 4.6-fold increase in materials’ removal rate and a 72% reduction in sidewall roughness compared to the LIPAA without magnetic field assistance. This improvement is attributed to the reduction of Larmor radius under the magnetic confinement, which concentrates the plasma into a smaller ablation area. A uniform microcolumn array is successfully fabricated on sapphire, demonstrating its potentials in optical device microstructures.
First-principles study of infrared, Raman, piezoelectric, and elastic properties of Mg–IV–N2 (IV = Si, Ge, Sn)
Mg–IV–N2 compounds with IV = Si, Ge, and Sn are ultra-wide bandgap semiconductors with various potential electronic and optoelectronic applications. They share the Pna21 space group crystal structure. Here, we present density functional perturbation theory calculations of the vibrational modes of these materials. We focus on the vibrational modes at the zone center to establish the relation between vibrational modes and their corresponding point-group symmetries, which determine the Raman and infrared spectra but also report the full Brillouin zone phonon dispersions and density of states. We also determine the piezoelectric tensor and the elastic compliance tensor.
Physics-informed deep learning for predicting optical properties of Nb–Ta anodic oxide films from spectroscopic ellipsometry
The prediction of optical properties for anodic oxide films from spectroscopic ellipsometry data constitutes a typical inverse problem. Traditional iterative fitting methods suffer from high computational cost and strong dependence on the model structure. This study develops a physics-informed deep learning framework that addresses this challenge through two complementary modeling scenarios. At its core is a Fresnel-constrained neural network, which predicts optical constants (n, k) and ellipsometric parameters [tan(Ψ), cos(Δ)] using the two inputs of base metal composition and anodizing voltage. A hybrid loss function involving the Fresnel equations is employed to guarantee physical consistency with optical reflection rules, thus avoiding non-physical solutions. In addition, two complementary data-optimization strategies were introduced: wavelength selection and model-based data augmentation. The wavelength-selection strategy compresses the spectral input by retaining the most informative 200–350 nm region, while Gaussian-noise-enhanced synthetic samples are generated to expand the training dataset and reduce the risk of surrogate-model bias propagation. The integrated model exhibits good prediction accuracy, with test-set RMSE values of 0.090 for n and 0.038 for k. Notably, the model maintains its training and prediction efficiency even as the volume of data increases. These results show that embedding fundamental optical laws into a deep learning structure yields a robust and efficient framework. Together with targeted data-optimization strategies, this framework offers a promising avenue for the high-throughput inverse design and characterization of complex functional oxide systems.
How fatigue frequency and load level govern grain growth kinetics in indentation and micro-bending beam fatigue in confined volumes
Nanocrystalline metals offer exceptional strength but are prone to microstructural instability under cyclic loading. Fatigue-induced grain growth, in particular, limits their long-term reliability. This work examines how fatigue frequency, load ratio, and volume confinement govern microstructural evolution in nanocrystalline nickel across different loading regimes. Bulk indentation fatigue produces predominantly homogeneous grain coarsening confined to the plastically deformed zone, whereas micromechanical bending fatigue promotes localized grain growth along fatigue cracks and ahead of crack tips. Increasing fatigue frequency in indentation fatigue further alters the deformation mode, shifting the response from pileup-dominated plasticity toward material flow and grain alignment. These results identify fatigue frequency, load level, and confinement as key boundary conditions controlling fatigue- and strain-induced grain growth in nanocrystalline metals.
Data-driven automated identification of optimal feature-representative images in infrared thermography using statistical and morphological descriptors
Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features, such as subsurface. Most IRT data post-processing methodologies result in the generation of image sequences in which defect visibility varies strongly across time, frequency, or coefficient/index domains, making the selection of defect-representative images a non-trivial and critical task. Conventional evaluation metrics, such as the signal-to-noise ratio or the Tanimoto criterion, often rely on prior knowledge of defect location or defect-free reference regions, which limits their applicability for automated and unsupervised analysis. In this work, a data-driven methodology is proposed to identify images within IRT datasets that are most likely to contain and represent features, especially anomalies and defects, without requiring any prior information about their spatial position. The investigation focuses on three complementary descriptors. First, the Homogeneity Index of Mixture quantifies statistical heterogeneity through deviations of local intensity distributions from a global reference distribution. The second descriptor is a Representative Elementary Area derived from a Minkowski-functional-based adaptation of the Representative Elementary Volume concept to two-dimensional images. Building upon these approaches, a third descriptor is introduced: a geometrical-topological Total Variation Energy index based on two-dimensional Minkowski functionals, designed to enhance sensitivity to localized anomalies. The proposed framework is validated experimentally using pulse-heated IRT data acquired from a carbon fiber-reinforced polymer plate containing six artificial defects at depths between 0.135 and 0.810 mm and is further supported by one-dimensional N-layer thermal model simulations. The results demonstrate that the proposed descriptors enable robust, unbiased ranking of image sequences and provide a reliable basis for automated defect-oriented image selection in IRT.