Gate Layer Research Articles

Joint Cross-Attention Network With Deep Modality Prior for Fast MRI Reconstruction.

Current deep learning-based reconstruction models for accelerated multi-coil magnetic resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using convolutional neural network (CNN). Although dual-domain information and data consistency constraint are commonly adopted in fast MRI reconstruction, the performance of existing models is still limited mainly by three factors: inaccurate estimation of coil sensitivity, inadequate utilization of structural prior, and inductive bias of CNN. To tackle these challenges, we propose an unrolling-based joint Cross-Attention Network, dubbed as jCAN, using deep guidance of the already acquired intra-subject data. Particularly, to improve the performance of coil sensitivity estimation, we simultaneously optimize the latent MR image and sensitivity map (SM). Besides, we introduce Gating layer and Gaussian layer into SM estimation to alleviate the "defocus" and "over-coupling" effects and further ameliorate the SM estimation. To enhance the representation ability of the proposed model, we deploy Vision Transformer (ViT) and CNN in the image and k-space domains, respectively. Moreover, we exploit pre-acquired intra-subject scan as reference modality to guide the reconstruction of subsampled target modality by resorting to the self- and cross-attention scheme. Experimental results on public knee and in-house brain datasets demonstrate that the proposed jCAN outperforms the state-of-the-art methods by a large margin in terms of SSIM and PSNR for different acceleration factors and sampling masks. Our code is publicly available at https://github.com/sunkg/jCAN.

Hybrid Nanostructures Based of Ta2O5-PMMA for Electronic Applications

The scientific interest on hybrid materials is mainly related to understanding the types of interactions between inorganic and organic component and the effect of these interactions on the properties of the new material formed. Hybrid nanostructured materials and especially dielectrics are used for electronics (for gate layer) and especially those applicable in structure of different types of thin film transistors (TFTs).In this paper is presented the research on thin film hybrid materials based on tantalum oxide (Ta2O5) starting with inorganic precursor - tantalum ethoxide and polymethylmethacrylate (PMMA). The chemical method sol -gel involves the precursor-tantalum ethoxide which is hydrolysed and functionalized (with special siloxane compound), and the organic methyl methacrylate monomer. The chemical reactions take place at low temperature below 160 oC. The sol is deposited as thin films by spin-coating to analyse intensity-voltage (I-V) and capacitance-voltage curves (C-V) to determine the electric properties. Metal-Insulator-Metal (MIM) structures were made-up for electric characterisation. The value of leakage currents was between 10-10 - 10-7 A at ± 40 V. The hybrid films were analysed by scanning electron microscopy (SEM) for thickness and morphology and for thermal stability the sol was investigated by TG and DSC.The dielectric permittivity ranges between 3.5 and 4 at 1 MHz, depending on the tantalum alkoxide: MMA molar ratio, showing good behaviour for gate layer in future TFTs.

Direct Z-scheme GaTe/SnS2 van der Waals heterojunction with tunable electronic properties: A promising highly efficient photocatalyst

The production of hydrogen through photocatalytic water splitting offers a secure and eco-friendly approach. In this study, the application of GaTe/SnS2 heterojunction in the field of photocatalysis was studied in depth by using the first-principles calculation method. Based on single-layer GaTe and SnS2, a novel two-dimensional GaTe/SnS2 van der Waals heterojunction (vdwH) was constructed. Through analytical calculations, it is obvious that the GaTe/SnS2 heterojunction has an indirect bandgap of 0.85eV. Furthermore, its type-II band alignment facilitates efficient separation of photogenerated electrons and holes across different layers. The application of Bader charge analysis revealed an intriguing observation: the GaTe layer transferred a charge of 0.058e to the SnS2 layer. This charge transfer led to the creation of a robust built-in electric field, which played a pivotal role in efficiently inhibiting the recombination of photogenerated electrons and holes. Under the condition of pH = 0, GaTe/SnS2 heterojunction can promote redox reaction and realize water splitting. In addition, when the biaxial strain of -3%–3 % is applied to the GaTe/SnS2 heterojunction, the band edge position and light absorption properties are effectively changed, and more photons participate in the water splitting process. More importantly, the solar-to-hydrogen (STH) efficiency of GaTe/SnS2 heterojunction reaches 56.6 %, and when ε = 3 %, ηSTH increases to 58.21 %. Hence, our research showcases the GaTe/SnS2 heterojunction's potential as a highly efficient Z-scheme photocatalyst for water splitting, offering promising prospects for hydrogen production.

Relaxing Hardware Requirements for Surface Code Circuits using Time-dynamics

The typical time-independent view of quantum error correction (QEC) codes hides significant freedom in the decomposition into circuits that are executable on hardware. Using the concept of detecting regions, we design time-dynamic QEC circuits directly instead of designing static QEC codes to decompose into circuits. In particular, we improve on the standard circuit constructions for the surface code, presenting new circuits that can embed on a hexagonal grid instead of a square grid, that can use ISWAP gates instead of CNOT or CZ gates, that can exchange qubit data and measure roles, and that move logical patches around the physical qubit grid while executing. All these constructions use no additional entangling gate layers and display essentially the same logical performance, having teraquop footprints within 25% of the standard surface code circuit. We expect these circuits to be of great interest to quantum hardware engineers, because they achieve essentially the same logical performance as standard surface code circuits while relaxing demands on hardware.

Normally-off n-ZnO/p-diamond heterojunction field effect transistor with recessed gate and current distribution layer

In this paper, an n-ZnO/p-diamond heterojunction field effect transistor (HJ-FET) with recessed gate and current distribution layer is designed and optimized by using TCAD software. It demonstrates that the threshold voltage and breakdown voltage can be remarkably affected by the doping concentration and thickness of both n-ZnO and p-diamond. Additionally, the threshold voltage can be further enhanced by introducing a recessed gate due to the enhanced depletion capacity on the channel layer. Although the conduction modulation by the enhanced electric field decreases the on-resistance, the obviously decreased breakdown voltage by the recessed gate degrades the electrical performances. Finally, a current distribution layer between source and drain electrodes is added to decrease the on-resistance of diamond HJ-FET. After optimizing the parameters, the diamond HJ-FET exhibits a larger threshold voltage, a smaller on-resistance and a higher breakdown voltage.

Electrostatic aid-free photo-floating gate two dimensional MoS2 synaptic transistors

Synaptic transistors are important component of neuromorphic computing systems, which is promising to reduce data traffic, time delay, and energy cost. However, most of synaptic behaviors normally need the aid of electrostatic gate voltage, which induces considerable consumption. Here, we propose a structure of the electrostatic aid-free photo-floating gate transistor based on the MoS2/MoOx/WSe2 heterojunctions, in which the MoOx acts as a unipolarity barrier layer and WSe2 functions as a photo-floating gate layer. Compared with the traditional floating-gate transistors, the designed device exhibits the distinct characteristics of visual synapse behaviors without the electrostatic aid gate, including the multilevel storage property of 13 stages, paired-pulse facilitation, the transition of short time plasticity to long time plasticity, and learning-forgetting-learning. This work provides a feasible strategy to construct neuromorphic computing device with ultra-low energy consumption.

GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition

PurposeBody composition analysis (BCA) of the body torso plays a vital role in the study of physical health and pathology and provides biomarkers that facilitate the diagnosis and treatment of many diseases, such as type 2 diabetes mellitus, cardiovascular disease, obstructive sleep apnea, and osteoarthritis. In this work, we propose a body composition tissue segmentation method that can automatically delineate those key tissues, including subcutaneous adipose tissue, skeleton, skeletal muscle tissue, and visceral adipose tissue, on positron emission tomography/computed tomography scans of the body torso. MethodsTo provide appropriate and precise semantic and spatial information that is strongly related to body composition tissues for the deep neural network, first we introduce a new concept of the body area and integrate it into our proposed segmentation network called Geographical Attention Network (GA-Net). The body areas are defined following anatomical principles such that the whole body torso region is partitioned into three non-overlapping body areas. Each body composition tissue of interest is fully contained in exactly one specific minimal body area. Secondly, the proposed GA-Net has a novel dual-decoder schema that is composed of a tissue decoder and an area decoder. The tissue decoder segments the body composition tissues, while the area decoder segments the body areas as an auxiliary task. The features of body areas and body composition tissues are fused through a soft attention mechanism to gain geographical attention relevant to the body tissues. Thirdly, we propose a body composition tissue annotation approach that takes the body area labels as the region of interest, which significantly improves the reproducibility, precision, and efficiency of delineating body composition tissues. ResultsOur evaluations on 50 low-dose unenhanced CT images indicate that GA-Net outperforms other architectures statistically significantly based on the Dice metric. GA-Net also shows improvements for the 95% Hausdorff Distance metric in most comparisons. Notably, GA-Net exhibits more sensitivity to subtle boundary information and produces more reliable and robust predictions for such structures, which are the most challenging parts to manually mend in practice, with potentially significant time-savings in the post hoc correction of these subtle boundary placement errors. Due to the prior knowledge provided from body areas, GA-Net achieves competitive performance with less training data. Our extension of the dual-decoder schema to TransUNet and 3D U-Net demonstrates that the new schema significantly improves the performance of these classical neural networks as well. Heatmaps obtained from attention gate layers further illustrate the geographical guidance function of body areas for identifying body tissues. Conclusions(i) Prior anatomic knowledge supplied in the form of appropriately designed anatomic container objects significantly improves the segmentation of bodily tissues. (ii) Of particular note are the improvements achieved in the delineation of subtle boundary features which otherwise would take much effort for manual correction. (iii) The method can be easily extended to existing networks to improve their accuracy for this application.

Terahertz bolometric detectors based on graphene field-effect transistors with the composite h-BN/black-P/h-BN gate layers using plasmonic resonances

We propose and analyze the performance of terahertz (THz) room-temperature bolometric detectors based on the graphene channel field-effect transistors (GC-FET). These detectors comprise the gate barrier layer (BL) composed of the lateral hexagonal-boron nitride black-phosphorus/hexagonal-boron nitride (h-BN/b-P/h-BN) structure. The main part of the GC is encapsulated in h-BN, whereas a short section of the GC is sandwiched between the b-P gate BL and the h-BN bottom layer. The b-P gate BL serves as the window for the electron thermionic current from the GC. The electron mobility in the GC section encapsulated in h-BN can be fairly large. This might enable a strong resonant plasmonic response of the GC-FET detectors despite relatively lower electron mobility in the GC section covered by the b-P window BL. The narrow b-P window diminishes the Peltier cooling and enhances the detector performance. The proposed device structure and its operation principle promote elevated-temperature GC-FET THz detector responsivity values and other characteristics, especially at the plasmonic resonances.

Improving Performance and Breakdown Voltage in Normally-Off GaN Recessed Gate MIS-HEMTs Using Atomic Layer Etching and Gate Field Plate for High-Power Device Applications.

A typical method for normally-off operation, the metal-insulator-semiconductor-high electron mobility transistor (MIS-HEMT) has been investigated. Among various approaches, gate recessed MIS-HEMT have demonstrated a high gate voltage sweep and low leakage current characteristics. Despite their high performance, obtaining low-damage techniques in gate recess processing has so far proven too challenging. In this letter, we demonstrate a high current density and high breakdown down voltage of a MIS-HEMT with a recessed gate by the low damage gate recessed etching of atomic layer etching (ALE) technology. After the remaining 3.7 nm of the AlGaN recessed gate was formed, the surface roughness (Ra of 0.40 nm) was almost the same as the surface without ALE (no etching) as measured by atomic force microscopy (AFM). Furthermore, the devices demonstrate state-of-the-art characteristics with a competitive maximum drain current of 608 mA/mm at a VG of 6 V and a threshold voltage of +2.0 V. The devices also show an on/off current ratio of 109 and an off-state hard breakdown voltage of 1190 V. The low damage of ALE technology was introduced into the MIS-HEMT with the recessed gate, which effectively reduced trapping states at the interface to obtain the low on-resistance (Ron) of 6.8 Ω·mm and high breakdown voltage performance.

Dielectric layer modulated nanofluidic transport: Formation of parent–sister vortices

Our analysis, consistent with the induced-charge electrokinetic, aptly discusses the effect of gate potential on the formation of vortices of disparate scales in a nanofluidic channel. The nanochannel considered here is connected with reservoirs at its ends, while a pair of dielectric gate electrodes is also embedded on the outer layer of the channel wall. In this endeavor, we establish that a suitable modulation of the dielectric polarization and inherent surface charge of the channel wall leads to the development of pair of “parent–sister” vortices in the flow field alongside yields a net flow in the channel, as well a unique fluidic functionality achieved at small scale is reported here for the first time. Moreover, we identify for the first time that the critical value of surface charge density (“critical-sigma,” i.e., σc) for two different typical heights of the nanofluidic channel, leading to the formation of maximum strength and size vortices by ceasing the net throughput. Also, we establish a critical range of surface charge density for a window of an arbitrary dimension of the dielectric gate electrode—a range for which optimum vortices are generated in the pathway, simultaneously producing adequate net flow through the channel. Under the present modeling configuration, we obtain that |σc|∼O(1 mC/m2) for a 1 μm long nanochannel having embedded quartz layer gate electrodes with gate potential |Vg|∼O(1 V). We believe that critical-sigma would be a crucial parameter for the design and fabrication of state-of-the-art nanodevices/nanosystems intended for augmented fluidic functionalities, for example, efficient solute mixing.

Impact of Nitridation on Bias Temperature Instability and Hard Breakdown Characteristics of SiON MOSFETs.

We study how nitridation, applied to SiON gate layers, impacts the reliability of planar metal-oxide-semiconductor field effect transistors (MOSFETs) subjected to negative and positive bias temperature instability (N/PBTI) as well as hard breakdown (HBD) characteristics of these devices. Experimental data demonstrate that p-channel transistors with SiON layers characterized by a higher nitrogen concentration have poorer NBTI reliability compared to their counterparts with a lower nitrogen content, while PBTI in n-channel devices is negligibly weak in all samples independently of the nitrogen concentration. The Weibull distribution of HBD fields extracted from experimental data in devices with a higher N density are shifted towards lower values with respect to that measured in MOSFETs, and SiON films have a lower nitrogen concentration. Based on these findings, we conclude that a higher nitrogen concentration results in the aggravation of BTI robustness and HBD characteristics.

Strain engineering in 2D hBN and graphene with evaporated thin film stressors

We demonstrate a technique to strain two-dimensional hexagonal boron nitride (hBN) and graphene by depositing stressed thin films to encapsulate exfoliated flakes. We choose optically transparent stressors to be able to analyze strain in 2D flakes through Raman spectroscopy. Combining thickness-dependent analyses of Raman peak shifts with atomistic simulations of hBN and graphene, we can explore layer-by-layer strain transfer in these materials. hBN and graphene show strain transfer into the top four and two layers of multilayer flakes, respectively. hBN has been widely used as a protective capping layer for other 2D materials, while graphene has been used as a top gate layer in various applications. Findings of this work suggest that straining 2D heterostructures with evaporated stressed thin films through the hBN capping layer or graphene top contact is possible since strain is not limited to a single layer.

Wafer-Scale Field-Effect Transistor-Type Sensor Using a Carbon Nanotube Film as a Channel for Ppb-Level Hydrogen Sulfide Detection.

Sulfur hexafluoride is widely used in power equipment because of its excellent insulation and arc extinguishing properties. However, severe damage to power equipment may be caused and a large-scale collapse of the power grid may occur when SF6 is decomposed into H2S, SOF2, and SO2F2. It is difficult to detect the SF6 concentration as it is a kind of inert gas. Generally, the trace gas decomposed in the early stage of SF6 is detected to achieve the function of early warning. Consequently, it is of great significance to realize the real-time detection of trace gases decomposed from SF6 for the early fault diagnosis of power equipment. In this work, a wafer-scale gate-sensing carbon-based FET gas sensor is fabricated on a four-inch carbon wafer for the detection of H2S, a decomposition product of SF6. The carbon nanotubes with semiconductor properties and the noble metal Pt are respectively used as a channel and a sensing gate of the FET-type gas sensor, and the channel transmission layer and the sensing gate layer each play an independent role and do not interfere with each other by introducing the gate dielectric layer Y2O3, giving full play to their respective advantages to forming an integrated sensor of gas detection and signal amplification. The detection limit of the as-prepared gate-sensing carbon-based FET gas sensor can reach 20 ppb, and its response deviation is not more than 3% for the different batches of gas sensors. This work provides a potentially useful solution for the industrial production of miniaturized and integrated gas sensors.

Modulation of Exchange Bias in La0.35Sr0.65MnO3/La0.7Sr0.3MnO3 through Volatile Polarization of P(VDF‐TrFE) Gate Dielectric

AbstractElectric‐field regulation of magnetic properties in perovskite manganites has attracted much attention for its potential in spintronics. For antiferromagnetic perovskite manganites, fewer studies are reported due to technological difficulties in probing their magnetic properties. Here, negative exchange bias (EB) is realized in epitaxial antiferromagnetic/ferromagnetic manganite bilayers with atomically flat interfaces. The low‐voltage pulse modulation of EB is demonstrated using the field‐effect device geometry with the ferroelectric copolymer, polyvinylidene fluoride with trifluoroethylene as a dielectric gating layer, antiferromagnetic La0.35Sr0.65MnO3 (AF‐LSMO) as pinning layer, and ferromagnetic La0.7Sr0.3MnO3 (FM‐LSMO) as conduction channel. Instead of using non‐volatile polarizations to control the EB, volatile polarizations in ferroelectric field effect transistors are suggested to be capable of modulating the EB. With high‐resolution electron microscopy and spectroscopy, the non‐volatile regulation of EB is attributed to the creation/annihilation of oxygen vacancies in the AF‐LSMO layer via low‐voltage pulses. This study reveals the effect of volatile electric polarizations in ferroelectric field effect devices and highlights the potential for low‐voltage pulse control of the physical properties in antiferromagnetic perovskite oxide insulators.

Importance attribution in neural networks by means of persistence landscapes of time series

This article describes a method to analyze time series with a neural network using a matrix of area-normalized persistence landscapes obtained with topological data analysis. The network’s architecture includes a gating layer that is able to identify the most relevant landscape levels for a classification task, thus working as an importance attribution system. Next, a matching is performed between the selected landscape levels and the corresponding critical points of the original time series. This matching enables reconstruction of a simplified shape of the time series that gives insight into the grounds of the classification decision. As a use case, this technique is tested in the article with input data from a dataset of electrocardiographic signals. The classification accuracy obtained using only a selection of landscape levels from data was 94.00%pm 0.13 averaged after five runs of a neural network, while the original signals achieved 98.41% pm 0.09 and landscape-reduced signals yielded 97.04% pm 0.14.

Implementation of Photosynaptic and Electrical Memory Functions in Organic Nano‐Floating‐Gate Transistors via a Perovskite‐Nanocrystal‐Based Nanocomposite Tunneling Layer

An organic nano‐floating‐gate transistor (ONFGT) with both photosynaptic and electrical memory functions is developed using a perovskite (CsPbBr3) NC‐insulating polymer (polystyrene; PS) nanocomposite and CsPbBr3 NCs as the tunneling and floating gate layers, respectively. The introduction of the CsPbBr3 NCs–PS nanocomposite layer improves the photoresponsivity of the ONFGT under ultraviolet–visible irradiation, resulting in an increase in both the photocurrent and the light‐to‐dark current ratio by 10−8 A and 104 orders of magnitude, respectively. It also exhibits high responsivity (0.804 A W−1) and external quantum efficiency (249.3%) under 400 nm irradiation. Furthermore, the photosynaptic characteristics of the ONFGT under visible‐light irradiation are investigated. To mimic biological nervous systems, the photocurrent of the device is dynamically modulated by varying the light intensity and duration. Notably, an increase in synaptic weight is observed under repeated photonic stimulations, as shown by changes in synaptic weight with each light pulse. Also, the ONFGT exhibits excellent nonvolatile memory characteristics in the dark, displaying a hysteresis window value of 2.9 V for a gate double sweep under ±5.0 V. Consequently, the perovskite NCs–insulating polymer nanocomposite tunneling layer is crucial for enabling photoresponsivity and memory characteristics in nano‐floating‐gate transistors, making them suitable for multifunctional electronic devices.

FEAT: A general framework for feature-aware multivariate time-series representation learning

Multivariate time-series is complex and uncertain. The overall temporal patterns change dynamically over time, and each feature is often observed to have a unique pattern. Therefore, it is challenging to model a framework that can flexibly learn feature-specific unique patterns as well as dynamically changing temporal patterns simultaneously. We propose a general framework for FEature-Aware multivariate Time-series representation learning, called FEAT. Unlike previous methods that only focus on training the overall temporal dependencies, we focus on training feature-specific as well as feature-agnostic representations in a data-driven manner. Specifically, we introduce a feature-wise encoder to explicitly model the feature-specific information and design an element-wise gating layer that learns the influence of feature-specific patterns per dataset in general. FEAT outperforms the benchmark models in average accuracy on 29 UEA multivariate time-series classification datasets and in MSE and MAE on four multivariate time-series forecasting datasets.

Cascade-Net for predicting cylinder wake at Reynolds numbers ranging from subcritical to supercritical regime

The application of machine learning techniques embedded with fluid mechanics has gained significant attention due to their exceptional ability to tackle intricate flow dynamics problems. In this study, an energy-cascade-conceptualized network termed Cascade-Net is proposed. This model is grounded in generative adversarial networks to predict the spatiotemporal fluctuating velocity in the near-wall wake of a circular cylinder in a physics-informed manner. A comprehensive dataset is obtained by wind tunnel testing, comprising the near-wake velocity field and wall pressure of a rough circular cylinder with Reynolds numbers from subcritical to supercritical regimes. By leveraging convolutional neural networks, the Cascade-Net utilizes the pressure data, Reynolds numbers, and a few of velocity measured in the wake field to predict the spatiotemporal fluctuating velocity. The velocity fluctuations are predicted hierarchically at different resolved scales, ensuring that the energy cascade in turbulence is accurately simulated. The results show that the Cascade-Net presents good generalization performance and is capable of accurately predicting fluctuating velocity fields and the second-order moments in both extrapolation and interpolation cases at various Reynolds numbers. The mechanism of Cascade-Net in prediction is also investigated by parametric analysis in the convolutional layer and spatial attention gate, manifesting that the Cascade-Net is heavily dependent on the velocity characteristics of the larger resolved scale adjacent to target smaller scales to prediction, which interprets the success of Cascade-Net in capturing the intricate physics of the cylinder wake.

Graphene-based field-effect transistor using gated highest-k ferroelectric thin film

We report a graphene-based highest-k ferroelectric field-effect transistor (GFE-FET) in which few-layer graphene sheets (FLGS) act as a channel layer and highest-k ferroelectric like barium zirconium titanate/barium calcium titanate (BZT/BCT) acts as an insulating gate layer. The electrochemical and sol-gel method has been used to prepare the used good quality FLGS and excellent BZT/BCT ferroelectric materials. SEM, XRD, UV–Vis, FTIR, and Raman spectroscopy measurements were done to characterize the produced materials. In order to evaluate the mobility of the fabricated GFE-FET, FET was fabricated with BZT/BCT gate and FLGS on top of it as conducting channel. The resultant field-effect mobility obtained from the fabricated GFE-FET is approximately ∼1.72 × 104 cm2V–1s−1 with 103 on/off ratio. These results ensure potential applications in some future high-speed electronic devices.

Selective Nanoscale Fabrication with Roughness Reduction of Polycrystalline Silicon by Physically Induced Fluorine Bonds Using Atomic Force Microscope

Polycrystalline silicon (poly‐Si) is widely used as a gate layer in integrated circuits, transistors, and channels through nanofabrication. Nanoremoval and roughness control are required for nanomanufacturing of various electronic devices. Herein, a nanoscale removal method is developed to overcome the limitations of microcracks, complex procedures, and time‐consuming conventional fabrication and lithography methods. The method is implemented with a mechanically induced poly‐Si phase transition using atomic force microscope (AFM). Mechanical force induces the covalent bonds between silicon and fluorine atoms which cause the phase transition of poly‐Si. Then, the bond structure of the Si molecules is weakened and selectively removed by nano‐Newton‐scale force using AFM. A selective nanoscale removal with roughness control is implemented in 0.5 mM TBAF solution after mechanical force (43.58–58.21 nN) is applied. By the magnitude of nano‐Newton force, the removal depth of poly‐Si is controlled from 2.66 to 21.52 nm. Finally, the nanoscale fabrication on poly‐Si wafer is achieved. The proposed nanoremoval mechanism is a simple fabrication method that provides selective, nanoscale, and highly efficient removal with roughness control.