High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

Breast self-examination practices among young rural women and its associated knowledge and attitudes in Tirunelveli District, Tamil Nadu.

CONTEXT: Breast self-examination (BSE) is a simple and cost-effective screening procedure in downstaging breast tumors. AIM: To assess the BSE practices and its associated knowledge and attitudes of rural women from Tirunelveli District, Tamil Nadu during the COVID-19 pandemic. SETTINGS AND DESIGN: A descriptive cross-sectional survey design was employed, and snowball sampling was used to recruit the sample of rural women from Tirunelveli. MATERIALS AND METHODS: Women ages 18-60 willing to take part were included. Data were collected online through a self-developed questionnaire. STATISTICAL ANALYSIS: Responses were analyzed using SPSS Version 20. Descriptive statistical analysis was used to present the general details and responses of the rural women through percentages. Using the appropriate tests, the mean differences of the BSE attitudes based on the personal variables were computed using one-way ANOVA. RESULTS: A total of 433 rural women (Mean age: 29.20 ± 9.35 years) from Tirunelveli responded to the online Knowledge, Attitude and Practice (KAP) questionnaire. Regular health checkups were undergone by 27.48% of women and 9.24% underwent breast cancer (BC) screening in the past. While 68.36% had heard of BC, 61% knew it could be detected in the early stages. Insufficient knowledge regarding BSE techniques was evident among the women. Knowledge about BC was highest among those earning more than 20,001 INR, women aged 36-45, widowed/separated/divorced women, and diploma graduates. Overall, BSE and BC knowledge score was low, with correspondingly low attitudes and practices. CONCLUSION: Findings showed the KAP among rural Tirunelveli women to be low.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

(Al, Ga)N-Based Quantum Dots Heterostructures on h-BN for UV-C Emission.

Aluminium Gallium Nitride (AlyGa1-yN) quantum dots (QDs) with thin sub-µm AlxGa1-xN layers (with x > y) were grown by molecular beam epitaxy on 3 nm and 6 nm thick hexagonal boron nitride (h-BN) initially deposited on c-sapphire substrates. An AlN layer was grown on h-BN and the surface roughness was investigated by atomic force microscopy for different deposited thicknesses. It was shown that for thicker AlN layers (i.e., 200 nm), the surface roughness can be reduced and hence a better surface morphology is obtained. Next, AlyGa1-yN QDs embedded in Al0.7Ga0.3N cladding layers were grown on the AlN and investigated by atomic force microscopy. Furthermore, X-ray diffraction measurements were conducted to assess the crystalline quality of the AlGaN/AlN layers and examine the impact of h-BN on the subsequent layers. Next, the QDs emission properties were studied by photoluminescence and an emission in the deep ultra-violet, i.e., in the 275-280 nm range was obtained at room temperature. Finally, temperature-dependent photoluminescence was performed. A limited decrease in the emission intensity of the QDs with increasing temperatures was observed as a result of the three-dimensional confinement of carriers in the QDs.

Vertical full-colour micro-LEDs via 2D materials-based layer transfer.

Micro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance1,2. However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density3-6. Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down7-14, but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique15-18 that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. These results establish routes to creating full-colour µLED displays for augmented and virtual reality, while also offering a generalizable platform for broader classes of three-dimensional integrated devices.

Empirical Study of Protein Feature Representation on Deep Belief Networks Trained With Small Data for Secondary Structure Prediction.

Protein secondary structure (SS) prediction is a classic problem of computational biology and is widely used in structural characterization and to infer homology. While most SS predictors have been trained on thousands of sequences, a previous approach had developed a compact model of training proteins that used a C-Alpha, C-Beta Side Chain (CABS)-algorithm derived energy based feature representation. Here, the previous approach is extended to Deep Belief Networks (DBN). Deep learning methods are notorious for requiring large datasets and there is a wide consensus that training deep models from scratch on small datasets, works poorly. By contrast, we demonstrate a simple DBN architecture containing a single hidden layer, trained only on the CB513 dataset. Testing on an independent set of G Switch proteins improved the Q 3 score of the previous compact model by almost 3%. The findings are further confirmed by comparison to several deep learning models which are trained on thousands of proteins. Finally, the DBN performance is also compared with Position Specific Scoring Matrix (PSSM)-profile based feature representation. The importance of (i) structural information in protein feature representation and (ii) complementary small dataset learning approaches for detection of structural fold switching are demonstrated.

Crystalline Quality and Surface Morphology Improvement of Face-to-Face Annealed MBE-Grown AlN on h-BN.

In this study, AlN epilayers were grown by ammonia-assisted molecular beam epitaxy on 3 nm h-BN grown on c-sapphire substrates. Their structural properties were investigated by comparing as-grown and postgrowth annealed layers. The role of annealing on the crystalline quality and surface morphology was studied as a function of AlN thickness and the annealing duration and temperature. Optimum annealing conditions were identified. The results of X-ray diffraction showed that optimization of the annealing recipe led to a significant reduction in the symmetric (0 0 0 2) and skew symmetric (1 0 -1 1) reflections, which was associated with a reduction in edge and mixed threading dislocation densities (TDDs). Furthermore, the impact on the crystalline structure of AlN and its surface was studied, and the results showed a transition from a surface with high roughness to a smoother surface morphology with a significant reduction in roughness. In addition, the annealing duration was increased at 1650 °C to further understand the impact on both AlN and h-BN, and the results showed a diffusion interplay between AlN and h-BN. Finally, an AlN layer was regrown on the top of an annealed template, which led to large terraces with atomic steps and low roughness.

Tf-GCZSL: Task-free generalized continual zero-shot learning.

Learning continually from a stream of training data or tasks with an ability to learn the unseen classes using a zero-shot learning framework is gaining attention in the literature. It is referred to as continual zero-shot learning (CZSL). Existing CZSL requires clear task-boundary information during training which is not practically feasible. This paper proposes a task-free generalized CZSL (Tf-GCZSL) method with short-term/long-term memory to overcome the requirement of task-boundary in training. A variational autoencoder (VAE) handles the fundamental ZSL tasks. The short-term and long-term memory help to overcome the condition of the task boundary in the CZSL framework. Further, the proposed Tf-GCZSL method combines the concept of experience replay with dark knowledge distillation and regularization to overcome the catastrophic forgetting issues in a continual learning framework. Finally, the Tf-GCZSL uses a fully connected classifier developed using the synthetic features generated at the latent space of the VAE. The performance of the proposed Tf-GCZSL is evaluated in the existing task-agnostic prediction setting and the proposed task-free setting for the generalized CZSL over the five ZSL benchmark datasets. The results clearly indicate that the proposed Tf-GCZSL improves the prediction at least by 12%, 1%, 3%, 4%, and 3% over existing state-of-the-art and baseline methods for CUB, aPY, AWA1, AWA2, and SUN datasets, respectively in both settings (task-agnostic prediction and task-free learning). The source code is available at https://github.com/Chandan-IITI/Tf-GCZSL.

Natural Boron and 10B-Enriched Hexagonal Boron Nitride for High-Sensitivity Self-Biased Metal-Semiconductor-Metal Neutron Detectors.

Metal-semiconductor-metal (MSM) detectors based on Ti/Au and Ni/Au interdigitated structures were fabricated using 2.5 micrometer thick hexagonal boron nitride (h-BN) layer with both natural and 10B-enriched boron. Current-voltage (I-V) and current-time (I-t) curves of the fabricated detectors were recorded with (I N) and without (I d) neutron irradiation, allowing the determination of their sensitivity (S = (I N - I d)/I d = ΔI/I d). Natural and 10B-enriched h-BN detectors exhibited high neutron sensitivities of 233 and 367% at 0 V bias under a flux of 3 × 104 n/cm2/s, respectively. An imbalance in the distribution of filled traps between the two electric contacts could explain the self-biased operation of the MSM detectors. Neutron sensitivity is further enhanced with electrical biasing, reaching 316 and 1192% at 200 V and a flux of 3 × 104 n/cm2/s for natural and 10B-enriched h-BN detectors, respectively, with dark current as low as 2.5 pA at 200 V. The increased performance under bias has been attributed to a gain mechanism based on neutron-induced charge carrier trapping at the semiconductor/metal interface. The response of the MSM detectors under thermal neutron flux and bias voltages was linear. These results clearly indicate that the thin-film monocrystal BN MSM neutron detectors can be optimized to operate sensitively with the absence of external bias and generate stronger signal detection using 10B-enriched boron.

Towards P-Type Conduction in Hexagonal Boron Nitride: Doping Study and Electrical Measurements Analysis of hBN/AlGaN Heterojunctions.

Reliable p-doped hexagonal boron nitride (h-BN) could enable wide bandgap optoelectronic devices such as deep ultra-violet light emitting diodes (UV LEDs), solar blind photodiodes and neutron detectors. We report the study of Mg in h-BN layers as well as Mg h-BN/AlGaN heterostructures. Mg incorporation in h-BN was studied under different biscyclopentadienyl-magnesium (Cp2Mg) molar flow rates. 2θ-ω x-ray diffraction scans clearly evidence a single peak, corresponding to the (002) reflection plane of h-BN with a full-width half maximum increasing with Mg incorporation in h-BN. For a large range of Cp2Mg molar flow rates, the surface of Mg doped h-BN layers exhibited characteristic pleats, confirming that Mg doped h-BN remains layered. Secondary ion mass spectrometry analysis showed Mg incorporation, up to 4 × 1018 /cm3 in h-BN. Electrical conductivity of Mg h-BN increased with increased Mg-doping. Heterostructures of Mg h-BN grown on n-type Al rich AlGaN (58% Al content) were made with the intent of forming a p-n heterojunction. The I-V characteristics revealed rectifying behavior for temperatures from 123 to 423 K. Under ultraviolet illumination, photocurrent was generated, as is typical for p-n diodes. C-V measurements evidence a built-in potential of 3.89 V. These encouraging results can indicate p-type behavior, opening a pathway for a new class of wide bandgap p-type layers.

Psychometric validation of the reconstructed version of the assessment of reasoning tool.

BACKGROUND: Assessing learners' competence in diagnostic reasoning is challenging and unstandardized in medical education. We developed a theory-informed, behaviorally anchored rubric, the Assessment of Reasoning Tool (ART), with content and response process validity. This study gathered evidence to support the internal structure and the interpretation of measurements derived from this tool. METHODS: We derived a reconstructed version of ART (ART-R) as a 15-item, 5-point Likert scale using the ART domains and descriptors. A psychometric evaluation was performed. We created 18 video variations of learner oral presentations, portraying different performance levels of the ART-R. RESULTS: 152 faculty viewed two videos and rated the learner globally and then using the ART-R. The confirmatory factor analysis showed a favorable comparative fit index = 0.99, root mean square error of approximation = 0.097, and standardized root mean square residual = 0.026. The five domains, hypothesis-directed information gathering, problem representation, prioritized differential diagnosis, diagnostic evaluation, and awareness of cognitive tendencies/emotional factors, had high internal consistency. The total score for each domain had a positive association with the global assessment of diagnostic reasoning. CONCLUSIONS: Our findings provide validity evidence for the ART-R as an assessment tool with five theoretical domains, internal consistency, and association with global assessment.

Effectiveness of selective area growth using van der Waals h-BN layer for crack-free transfer of large-size III-N devices onto arbitrary substrates.

Selective Area van der Waals Epitaxy (SAVWE) of III-Nitride device has been proposed recently by our group as an enabling solution for h-BN-based device transfer. By using a patterned dielectric mask with openings slightly larger than device sizes, pick-and-place of discrete LEDs onto flexible substrates was achieved. A more detailed study is needed to understand the effect of this selective area growth on material quality, device performance and device transfer. Here we present a study performed on two types of LEDs (those grown on h-BN on patterned and unpatterned sapphire) from the epitaxial growth to device performance and thermal dissipation measurements before and after transfer. Millimeter-size LEDs were transferred to aluminum tape and to silicon substrates by van der Waals liquid capillary bonding. It is shown that patterned samples lead to a better material quality as well as improved electrical and optical device performances. In addition, patterned structures allowed for a much better transfer yield to silicon substrates than unpatterned structures. We demonstrate that SAVWE, combined with either transfer processes to soft or rigid substrates, offers an efficient, robust and low-cost heterogenous integration capability of large-size devices to silicon for photonic and electronic applications.

Control of the Mechanical Adhesion of III-V Materials Grown on Layered h-BN.

Hexagonal boron nitride (h-BN) can be used as a p-doped material in wide-bandgap optoelectronic heterostructures or as a release layer to allow lift-off of grown three-dimensional (3D) GaN-based devices. To date, there have been no studies of factors that lead to or prevent lift-off and/or spontaneous delamination of layers. Here, we report a unique approach of controlling the adhesion of this layered material, which can result in both desired lift-off layered h-BN and mechanically inseparable robust h-BN layers. This is accomplished by controlling the diffusion of Al atoms into h-BN from AlN buffers grown on h-BN/sapphire. We present evidence of Al diffusion into h-BN for AlN buffers grown at high temperatures compared to conventional-temperature AlN buffers. Further evidence that the Al content in BN controls lift-off is provided by comparison of two alloys, Al0.03B0.97N/sapphire and Al0.17B0.83N/sapphire. Moreover, we tested that management of Al diffusion controls the mechanical adhesion of high-electron-mobility transistor (HEMT) devices grown on AlN/h-BN/sapphire. The results extend the control of two-dimensional (2D)/3D hetero-epitaxy and bring h-BN closer to industrial application in optoelectronics.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI.

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Highly Ordered Boron Nitride/Epigraphene Epitaxial Films on Silicon Carbide by Lateral Epitaxial Deposition.

The realization of high-performance nanoelectronics requires control of materials at the nanoscale. Methods to produce high quality epitaxial graphene (EG) nanostructures on silicon carbide are known. The next step is to grow van der Waals semiconductors on top of EG nanostructures. Hexagonal boron nitride (h-BN) is a wide bandgap semiconductor with a honeycomb lattice structure that matches that of graphene, making it ideally suited for graphene-based nanoelectronics. Here, we describe the preparation and characterization of multilayer h-BN grown epitaxially on EG using a migration-enhanced metalorganic vapor phase epitaxy process. As a result of the lateral epitaxial deposition (LED) mechanism, the grown h-BN/EG heterostructures have highly ordered epitaxial interfaces, as desired in order to preserve the transport properties of pristine graphene. Atomic scale structural and energetic details of the observed row-by-row growth mechanism of the two-dimensional (2D) epitaxial h-BN film are analyzed through first-principles simulations, demonstrating one-dimensional nucleation-free-energy-barrierless growth. This industrially relevant LED process can be applied to a wide variety of van der Waals materials.

Mission Aware Motion Planning (MAP) Framework With Physical and Geographical Constraints for a Swarm of Mobile Stations.

In this paper, we propose a mission aware motion planning (MAP) framework for a swarm of autonomous unmanned ground vehicles (UGVs) or mobile stations in an uncertain environment for efficient supply of resources/services to unmanned aerial vehicles (UAVs) performing a specific mission. The MAP framework consists of two levels, namely, centralized mission planning and decentralized motion planning. On the first level, the centralized mission planning algorithm estimates the density of UAV in a given environment for determining the number of UGVs and their initial operating location. In the subsequent level, a decentralized motion planning algorithm which provides a closed-form expression for velocity command using adaptive density estimation has been proposed. Further, the physical and geographical constraints are integrated into motion planning. A Monte-Carlo simulation is performed to evaluate the advantages of the MAP over distributed stationary stations (DSSs) often used in the literature. The obtained results clearly indicate that in comparison with DSS, MAP reduces the average distance traveled by UAVs about 20%, reduces the loss of mission time by 90 s per interruption and power loss by 3 dB.

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network.

This paper presents a new learning algorithm developed for a three layered spiking neural network for pattern classification problems. The learning algorithm maximizes the interclass margin and is referred to as the two stage margin maximization spiking neural network (TMM-SNN). In the structure learning stage, the learning algorithm completely evolves the hidden layer neurons in the first epoch. Further, TMM-SNN updates the weights of the hidden neurons for multiple epochs using the newly developed normalized membrane potential learning rule such that the interclass margins (based on the response of hidden neurons) are maximized. The normalized membrane potential learning rule considers both the local information in the spike train generated by a presynaptic neuron and the existing knowledge (synaptic weights) stored in the network to update the synaptic weights. After the first stage, the number of hidden neurons and their parameters are not updated. In the output weights learning stage, TMM-SNN updates the weights of the output layer neurons for multiple epochs to maximize the interclass margins (based on the response of output neurons). Performance of TMM-SNN is evaluated using ten benchmark data sets from the UCI machine learning repository. Statistical performance comparison of TMM-SNN with other existing learning algorithms for SNNs is conducted using the nonparametric Friedman test followed by a pairwise comparison using the Fisher's least significant difference method. The results clearly indicate that TMM-SNN achieves better generalization performance in comparison to other algorithms.

SEFRON: A New Spiking Neuron Model With Time-Varying Synaptic Efficacy Function for Pattern Classification.

This paper presents a new time-varying long-term Synaptic Efficacy Function-based leaky-integrate-and-fire neuRON model, referred to as SEFRON and its supervised learning rule for pattern classification problems. The time-varying synaptic efficacy function is represented by a sum of amplitude modulated Gaussian distribution functions located at different times. For a given pattern, the SEFRON's learning rule determines the changes in the amplitudes of weights at selected presynaptic spike times by minimizing a new error function reflecting the differences between the desired and actual postsynaptic firing times. Similar to the gamma-aminobutyric acid-switch phenomenon observed in a biological neuron that switches between excitatory and inhibitory postsynaptic potentials based on the physiological needs, the time-varying synapse model proposed in this paper allows the synaptic efficacy (weight) to switch signs in a continuous manner. The computational power and the functioning of SEFRON are first illustrated using a binary pattern classification problem. The detailed performance comparisons of a single SEFRON classifier with other spiking neural networks (SNNs) are also presented using four benchmark data sets from the UCI machine learning repository. The results clearly indicate that a single SEFRON provides a similar generalization performance compared to other SNNs with multiple layers and multiple neurons.

Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI).

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently, spatio-temporal decomposition via spatial filtering (Fukunaga-Koontz transform, ICA) have gained attention in the analysis of fMRI time-series data. Their ability to decompose the blood oxygen level dependent (BOLD) rs-fMRI time series data into discriminative spatial and temporal components have resulted in better classification accuracy and the ability to isolate the important brain circuits responsible for the observed differences in brain activity. However, they are prone to errors in the estimation of covariance matrices due to the significant presence of atypical samples in the ADHD dataset. In this paper, we present a regularization framework to obtain a robust estimation of the covariance matrices such that the effect of atypical samples is reduced. The resulting approach called as regularized spatial filtering method (R-SFM) further uses Mahalanobis whitening to lower the effect of two-way correlations while preserving the spatial arrangement of the data in the feature extraction process. R-SFM was evaluated on the benchmark ADHD200 dataset and not only obtained a 6% improvement in classification accuracy, but also a 66.66% decrease in standard deviation over the previously developed SFM approach. Also R-SFM produces higher specificity which results in lower misclassification of ADHD, thereby reducing the risk of misdiagnosis. These results clearly show that R- SFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data.

Controlled crack propagation for atomic precision handling of wafer-scale two-dimensional materials.

Although flakes of two-dimensional (2D) heterostructures at the micrometer scale can be formed with adhesive-tape exfoliation methods, isolation of 2D flakes into monolayers is extremely time consuming because it is a trial-and-error process. Controlling the number of 2D layers through direct growth also presents difficulty because of the high nucleation barrier on 2D materials. We demonstrate a layer-resolved 2D material splitting technique that permits high-throughput production of multiple monolayers of wafer-scale (5-centimeter diameter) 2D materials by splitting single stacks of thick 2D materials grown on a single wafer. Wafer-scale uniformity of hexagonal boron nitride, tungsten disulfide, tungsten diselenide, molybdenum disulfide, and molybdenum diselenide monolayers was verified by photoluminescence response and by substantial retention of electronic conductivity. We fabricated wafer-scale van der Waals heterostructures, including field-effect transistors, with single-atom thickness resolution.