Search

Cubic boron nitride (c-BN) is an ultrawide-bandgap semiconductor of significant interest for high-frequency and high-power electronics applications owing to its high saturation drift velocity and high electric breakdown field. Beyond transistors, devices exploiting the valley degree of freedom or negative differential resistance are of keen interest. While diamond has been found to have potential for these applications, c-BN has not been considered owing to a lack of knowledge of the relevant charge transport properties. Here, we report a study of the high-field transport and noise properties of c-BN using first-principles calculations. We find that c-BN exhibits an abrupt region of negative differential resistance (NDR) below 140 K, despite the lack of multi-valley band structure typically associated with NDR. This feature is found to arise from a strong energy dependence of the scattering rates associated with optical phonon emission. The high optical phonon energy also leads to an intervalley scattering time rivaling that of diamond. The negative differential resistance and long intervalley scattering time indicate the potential of c-BN for transferred-electron and valleytronic devices, respectively.

Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whether other observable galaxy features, such as their morphologies or large-scale environments, can be used to tighten the correlation between galaxy properties and halo masses. In this work, we train a baseline random forest model to predict halo mass using galaxy features from the Illustris TNG50 hydrodynamical simulation, and compare with convolutional neural networks (CNNs) and graph neural networks (GNNs) trained respectively using galaxy image cutouts and galaxy point clouds. The best baseline model has a root mean squared error (RMSE) of 0.310 and mean absolute error (MAE) of 0.220, compared to the CNN (RSME=0.359, MAE=0.238), GNN (RMSE=0.248, MAE=0.158), and a novel combined CNN+GNN (RMSE=0.248, MAE=0.144). The CNN is likely limited by our small data set, and we anticipate that the CNN and CNN+GNN would benefit from training on larger cosmological simulations. We conclude that deep learning models can leverage information from galaxy appearances and environment, beyond commonly used summary statistics, in order to better predict the halo mass., Comment: 11 pages, 3 figures, accepted to ICML 2024 AI4Science Workshop

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Quantum computers have evolved from the theoretical realm into a race to large-scale implementations. This is due to the promise of revolutionary speedups, where achieving such speedup requires designing an algorithm that harnesses the structure of a problem using quantum mechanics. Yet many quantum programming languages today require programmers to reason at a low level of quantum gate circuitry. This presents a significant barrier to entry for programmers who have not yet built up an intuition about quantum gate semantics, and it can prove to be tedious even for those who have. In this paper, we present Qwerty, a new quantum programming language that allows programmers to manipulate qubits more expressively than gates, relegating the tedious task of gate selection to the compiler. Due to its novel basis type and easy interoperability with Python, Qwerty is a powerful framework for high-level quantum-classical computation., Comment: 30 pages, 27 figures

Quantum systems subject to random unitary evolution and measurements at random points in spacetime exhibit entanglement phase transitions which depend on the frequency of these measurements. Past work has experimentally observed entanglement phase transitions on near-term quantum computers, but the characterization approach using entanglement entropy is not scalable due to exponential overhead of quantum state tomography and post selection. Recently, an alternative protocol to detect entanglement phase transitions using linear cross-entropy was proposed which eliminates both bottlenecks. Here, we report the demonstration of this protocol in systems with one-dimensional and all-to-all connectivities on IBM's quantum hardware on up to 22 qubits, a regime which is presently inaccessible if post-selection is required. We demonstrate a collapse of the data into a scale-invariant form with critical exponents agreeing with theory within uncertainty. Our demonstration paves the way for studies of measurement-induced entanglement phase transitions and associated critical phenomena on larger near-term quantum systems., Comment: 23 pages, 15 figures

Electronic properties of crystals can be manipulated using spatially periodic modulations. Long-wavelength, incommensurate modulations are of particular interest, exemplified recently by moir\'e patterned van der Waals (vdW) heterostructures. Bulk vdW superlattices hosting interfaces between clean 2D layers represent scalable bulk analogs of vdW heterostructures and present a complementary venue to explore incommensurately modulated 2D states. Here we report the bulk vdW superlattice SrTa$_2$S$_5$ realizing an incommensurate 1D modulation of 2D transition metal dichalcogenide (TMD) $H$-TaS$_2$ layers. High-quality electronic transport in the $H$-TaS$_2$ layers, evidenced by quantum oscillations, is made anisotropic by the modulation and shows commensurability oscillations akin to lithographically modulated 2D systems. We also find unconventional, clean-limit superconductivity (SC) in SrTa$_2$S$_5$ with a pronounced suppression of interlayer coherence relative to intralayer coherence. Such a hierarchy can arise from pair-density wave (PDW) SC with mismatched spatial arrangement in adjacent superconducting layers. Examining the in-plane magnetic field $H_{ab}$ dependence of interlayer critical current density $J_c$, we find anisotropy with respect to $H_{ab}$ orientation: $J_c$ is maximized (minimized) when $H_{ab}$ is perpendicular (parallel) to the stripes, consistent with 1D PDW SC. From diffraction we find the structural modulation is shifted between adjacent $H$-TaS$_2$ layers, suggesting mismatched 1D PDW is seeded by the striped structure. With a high-mobility Fermi liquid in a coherently modulated structure, SrTa$_2$S$_5$ is a promising host for novel phenomena anticipated in clean, striped metals and superconductors. More broadly, SrTa$_2$S$_5$ establishes bulk vdW superlattices as macroscopic platforms to address long-standing predictions for modulated electronic phases., Comment: 19 pages, 4 figures

Light echoes of debris disks around active stars can reveal disk structure and composition even when disks are not spatially resolved. Unfortunately, distinguishing reflected light from quiescent starlight and unexpected post-peak flare structure is challenging, especially for edge-on geometries where the time delay between observed flare photons and light scattered from the near side of the disk is short. Here, we take advantage of the fact that scattered light from a dusty disk is polarized, depending on the location of the scattering site and the orientation of the disk relative to a distant observer. Filtering reflected light into its polarized components allows echoes to stand out in predictable ways. We test this idea with a simple model for a disk around an active M dwarf. Our results demonstrate that the use of polarimetric data of flaring stars can significantly enhance echo signals relative to starlight and yield more robust and accurate fits to disk parameters compared to analyses based on the total intensity alone., Comment: 14 pages, 9 figures, 1 table, AJ accepted

Lithium niobate (LiNbO$_3$, LN) is a ferroelectric crystal of interest for integrated photonics owing to its large second-order optical nonlinearity and the ability to impart periodic poling via an external electric field. However, on-chip device performance based on thin-film lithium niobate (TFLN) is presently limited by optical loss arising from corrugations between poled regions and sidewall surface roughness. Atomic layer etching (ALE) could potentially smooth these features and thereby increase photonic performance, but no ALE process has been reported for LN. Here, we report a directional ALE process for $x$-cut MgO-doped LN using sequential exposures of H$_2$ and SF$_6$/Ar plasmas. We observe etch rates up to $1.01 \pm 0.05$ nm/cycle with a synergy of $94$%. We also demonstrate ALE can be achieved with SF$_6$/O$_2$ or Cl$_2$/BCl$_3$ plasma exposures in place of the SF$_6$/Ar plasma step with synergies above $90$%. When combined with a wet post-process to remove redeposited compounds, the process yields a 50% decrease in surface roughness. With additional optimization to reduce the quantity of redeposited compounds, these processes could be used to smoothen surfaces of TFLN waveguides etched by physical Ar$^+$ milling, thereby increasing the performance of TFLN nanophotonic devices or enabling new integrated photonic capabilities.

Ab-initio calculations of charge transport properties in materials without adjustable parameters have provided microscopic insights into electron-phonon interactions which govern charge transport properties. Other transport properties such as the diffusion coefficient provide additional microscopic information and are readily accessible experimentally, but few ab-initio calculations of these properties have been performed. Here, we report first-principles calculations of the hot electron diffusion coefficient in Si and its dependence on electric field over temperatures from 77 -- 300 K. While qualitative agreement in trends such as anisotropy at high electric fields is obtained, the quantitative agreement that is routinely achieved for low-field mobility is lacking. We examine whether the discrepancy can be attributed to an inaccurate description of f-type intervalley scattering by computing the microwave-frequency noise spectrum and piezoresistivity. These calculations indicate that any error in the strength of f-type scattering is insufficient to explain the diffusion coefficient discrepancies. Our findings suggest that the measured diffusion coefficient is influenced by factors such as space charge effects which are not included in ab-initio calculations, impacting the interpretation of this property in terms of charge transport processes.

Microwave loss in superconducting titanium nitride (TiN) films is attributed to two-level systems in various interfaces arising in part from oxidation and microfabrication-induced damage. Atomic layer etching (ALE) is an emerging subtractive fabrication method which is capable of etching with Angstrom-scale etch depth control and potentially less damage. However, while ALE processes for TiN have been reported, they either employ HF vapor, incurring practical complications; or the etch rate lacks the desired control. Further, the superconducting characteristics of the etched films have not been characterized. Here, we report an isotropic plasma-thermal TiN ALE process consisting of sequential exposures to molecular oxygen and an SF$_6$/H$_2$ plasma. For certain ratios of SF$_6$:H$_2$ flow rates, we observe selective etching of TiO$_2$ over TiN, enabling self-limiting etching within a cycle. Etch rates were measured to vary from 1.1 \r{A}/cycle at 150 $^\circ$C to 3.2 \r{A}/cycle at 350 $^\circ$C using ex-situ ellipsometry. We demonstrate that the superconducting critical temperature of the etched film does not decrease beyond that expected from the decrease in film thickness, highlighting the low-damage nature of the process. These findings have relevance for applications of TiN in microwave kinetic inductance detectors and superconducting qubits., Comment: 17 pages, 7 figures

The transport properties of hot holes in silicon at cryogenic temperatures exhibit several anomalous features, including the emergence of two distinct saturated drift velocity regimes and a non-monotonic trend of the current noise versus electric field at microwave frequencies. Despite prior investigations, these features lack generally accepted explanations. Here, we examine the microscopic origin of these phenomena by extending a recently developed ab-initio theory of high-field transport and noise in semiconductors. We find that the drift velocity anomaly may be attributed to scattering dominated by acoustic phonon emission, leading to an additional regime of drift velocity saturation at temperatures $\sim 40$ K for which the acoustic phonon occupation is negligible; while the non-monotonic trend in the current noise arises due to the decrease in momentum relaxation time with electric field. The former conclusion is consistent with the findings of prior work, but the latter distinctly differs from previous explanations. This work highlights the use of high-field transport and noise phenomena as sensitive probes of microscopic charge transport phenomena in semiconductors., Comment: 19 pages, 4 figures

Motivated by the manifold hypothesis, which states that data with a high extrinsic dimension may yet have a low intrinsic dimension, we develop refined statistical bounds for entropic optimal transport that are sensitive to the intrinsic dimension of the data. Our bounds involve a robust notion of intrinsic dimension, measured at only a single distance scale depending on the regularization parameter, and show that it is only the minimum of these single-scale intrinsic dimensions which governs the rate of convergence. We call this the Minimum Intrinsic Dimension scaling (MID scaling) phenomenon, and establish MID scaling with no assumptions on the data distributions so long as the cost is bounded and Lipschitz, and for various entropic optimal transport quantities beyond just values, with stronger analogs when one distribution is supported on a manifold. Our results significantly advance the theoretical state of the art by showing that MID scaling is a generic phenomenon, and provide the first rigorous interpretation of the statistical effect of entropic regularization as a distance scale., Comment: 53 pages

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Fast and programmable transport of liquid droplets on a solid substrate is desirable in microfluidic, thermal, biomedical, and energy devices. Past research has focused on designing substrates with asymmetric structures or gradient wettability where droplet behaviors are passively controlled, or by applying external electric, thermal, magnetic, or acoustic stimuli that either require the fabrication of electrodes or a strong applied field. In this work, we demonstrate tunable and programmable droplet motion on liquid-infused surfaces (LIS) and inside solid-surface capillary channels using low-intensity light and photo-responsive surfactants. When illuminated by the light of appropriate wavelengths, the surfactants can reversibly change their molecular conformation thereby tuning interfacial tensions in a multi-phase fluid system. This generates a Marangoni flow that drives droplet motions. With two novel surfactants that we synthesized, we demonstrate fast linear and complex 2D movements of droplets on liquid surfaces, on LIS, and inside microchannels. We also visualized the internal flow pattern using tracer particles and developed simple scaling arguments to explain droplet-size-dependent velocity. The method demonstrated in this study serves as a simple and exciting new approach for the dynamic manipulation of droplets for microfluidic, thermal, and water harvesting devices.

The semiconductor BAs has drawn significant interest due to experimental reports of simultaneous high thermal conductivity and ambipolar charge mobility. The \textit{ab~initio} prediction of high electron and hole mobility assumed the dominance of charge carrier scattering by one phonon. Recently, higher-order electron-phonon scattering processes in polar and non-polar semiconductors have been reported to have a non-negligible impact on charge transport properties, suggesting they may play a role in BAs as well. Here, we report an \textit{ab~initio} study of two-phonon electron and hole scattering processes in BAs. We find that inclusion of these higher-order processes reduces the computed room temperature electron and hole mobility in BAs by around 40\% from the one-phonon value, resulting in an underestimate of experimental values by a similar percentage. We suggest an experimental approach to test these predictions using luminescence spectroscopy that is applicable to the defective samples which are presently available.

The deviations of cryogenic collector current-voltage characteristics of SiGe heterojunction bipolar transistors (HBTs) from ideal drift-diffusion theory have been a topic of investigation for many years. Recent work indicates that direct tunneling across the base contributes to the non-ideal current in highly-scaled devices. However, cryogenic discrepancies have been observed even in older-generation devices for which direct tunneling is negligible, suggesting another mechanism may also contribute. Although similar non-ideal current-voltage characteristics have been observed in Schottky junctions and were attributed to spatial inhomogeneities in the base-emitter junction potential, this explanation has not been considered for SiGe HBTs. Here, we experimentally investigate this hypothesis by characterizing the collector current ideality factor and built-in potential of a SiGe HBT versus temperature using a cryogenic probe station. The temperature-dependence of the ideality factor and the relation between the built-in potential as measured by capacitance-voltage and current-voltage characteristics are in good qualitative agreement with the predictions of a theory of electrical transport across a junction with a Gaussian distribution of potential barrier heights. These observations suggest that lateral inhomogeneities in the base-emitter junction potential may contribute to the cryogenic non-idealities. This work helps to identify the physical mechanisms limiting the cryogenic microwave noise performance of SiGe HBTs.

The quantum computation of molecular response properties on near-term quantum hardware is a topic of significant interest. While computing time-domain response properties is in principle straightforward due to the natural ability of quantum computers to simulate unitary time evolution, circuit depth limitations restrict the maximum time that can be simulated and hence the extraction of frequency-domain properties. Computing properties directly in the frequency domain is therefore desirable, but the circuits require large depth when the typical hardware gate set consisting of single- and two-qubit gates is used. Here, we report the experimental quantum computation of the response properties of diatomic molecules directly in the frequency domain using a three-qubit iToffoli gate, enabling a reduction in circuit depth by a factor of two. We show that the molecular properties obtained with the iToffoli gate exhibit comparable or better agreement with theory than those obtained with the native CZ gates. Our work is among the first demonstrations of the practical usage of a native multi-qubit gate in quantum simulation, with diverse potential applications to the simulation of quantum many-body systems on near-term digital quantum computers., Comment: 10 pages, 6 figures

Recent ab-initio studies of electron transport in GaAs have reported that electron-phonon (e-ph) interactions beyond the lowest order play a fundamental role in charge transport and noise phenomena. Inclusion of the next-leading-order process in which an electron scatters with two phonons was found to yield good agreement for the high-field drift velocity, but the characteristic non-monotonic trend of the power spectral density of current fluctuations (PSD) with electric field was not predicted. The high computational cost of the ab-initio approach necessitated various approximations to the two-phonon scattering term, which were suggested as possible origins of the discrepancy. Here, we report a semi-analytical transport model of two-phonon electron scattering via the Fr\"ohlich mechanism, allowing a number of the approximations in the ab-initio treatment to be lifted while retaining the accuracy to within a few percent. We compare the calculated and experimental transport and noise properties as well as scattering rates measured by photoluminescence experiments. We find quantitative agreement within 15% for the drift velocity and 25% for the $\Gamma$ valley scattering rates, and agreement with the $\Gamma-L$ intervalley scattering rates within a factor of two. Considering these results and prior studies of current noise in GaAs, we conclude that the most probable origin of the non-monotonic PSD trend versus electric field is the formation of space charge domains rather than intervalley scattering as has been assumed., Comment: 28 pages (including 5 pages appendix), 3 figures, submitted to Physical Review B

We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying distribution. This quantity is based on a recently proposed matrix-based entropy that uses the eigenvalues of a normalized Gram matrix to compute an estimate of the eigenvalues of an uncentered covariance operator in a reproducing kernel Hilbert space. We show that a difference of matrix-based entropies (DiME) is well suited for problems involving the maximization of mutual information between random variables. While many methods for such tasks can lead to trivial solutions, DiME naturally penalizes such outcomes. We compare DiME to several baseline estimators of mutual information on a toy Gaussian dataset. We provide examples of use cases for DiME, such as latent factor disentanglement and a multiview representation learning problem where DiME is used to learn a shared representation among views with high mutual information.