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Imaging Reconfigurable Molecular Concentration on a Graphene Field-Effect Transistor
The spatial arrangement of adsorbates deposited onto a clean surface under vacuum typically cannot be reversibly tuned. Here we use scanning tunneling microscopy to demonstrate that molecules deposited onto graphene field-effect transistors (FETs) exhibit reversible, electrically tunable surface concentration. Continuous gate-tunable control over the surface concentration of charged F 4 TCNQ molecules was achieved on a graphene FET at T = 4.5K. This capability enables the precisely controlled impurity doping of graphene devices and also provides a new method for determining molecular energy level alignment based on the gate-dependence of molecular concentration. Gate-tunable molecular concentration is explained by a dynamical molecular rearrangement process that reduces total electronic energy by maintaining Fermi level pinning in the device substrate. Finally, the molecular surface concentration is fully determined by the device back-gate voltage, its geometric capacitance, and the energy difference between the graphene Dirac point and the molecular LUMO level.
In Situ Atomic Tracking on the Interfacial Etching and Reconfiguration of Cu-ReSe 2 Contact during Thermal Annealing
The Schottky barrier height can be greatly affected by the metal diffusion, reaction, and covalent bonding formation at the contact. Exploring novel methods and revealing the fundamental mechanisms for contact engineering are of vital importance for microelectronic devices. Here, in this study, the annealing induced interfacial reactions at Cu-ReSe 2 contact are dynamically revealed from the atomic scale. Accompanied by the diffusion of Se to Cu, ReSe 2 is gradually decomposed to a thin Re interlayer through a “chain-by-chain” manner. Theoretical calculations show that the Cu atoms can facilitate the chemical bond breaking of ReSe 2 , significantly lowering the Se diffusion energy barrier toward Cu. The formed Re/ReSe 2 heterostructure presents a metal-like band structure, which underscores the critical role of Cu in altering the interfacial chemistry and promoting carrier transport across the interface. Our results can provide vital insights into the contact properties of ReSe 2 and provide a possible method for fabricating high-performance ReSe 2 -based devices.
Self-Assembled Magnetic Nanoparticle Layers: Structural Control for Reconfigurable Magnetism and Functional Applications
We advance soft X-ray vector ptychographic tomography to map the 3D magnetization field in self-assembled superparamagnetic nanoparticles at a liquid–liquid interface, revealing how layered structures influence magnetic ordering. We observe that monolayers with low coordination numbers exhibit weak magnetic order, with magnetic vortices disrupting spin alignment. In contrast, bilayers and trilayers with higher coordination numbers display long-range magnetic order with strong spin correlations across larger distances and a suppression of magnetic vortices. We further quantify the average distance for vortex–antivortex pairs as 26.0 ± 2.0 nm, while vortex–vortex and antivortex–antivortex pairs exhibit larger separations, averaging 44.9 ± 5.2 and 54.1 ± 7.4 nm, respectively. These experimental results are supported by micromagnetic Monte Carlo simulations. Our findings illustrate how layered structures enhance magnetic order and spin correlation in superparamagnetic nanoparticle assemblies, providing a promising approach for tuning magnetic properties in applications such as data storage, microrobotics, and biomedicine.
Scalable photonic-phonoinc integrated circuitry for reconfigurable signal processing
Not Available
All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories
Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.
Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks
Abstract The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the $${\hbox {HfO}}_{2}$$ HfO 2 based memristive device. $$1\textrm{st}$$ 1 st and $$2\textrm{nd}$$ 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to $$\sim$$ ∼ 4.3 $$\times$$ × and $$\sim$$ ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.
Platinum-like HER onset in a GNR/MoS 2 quantum dot heterostructure through curvature-dependent electron density reconfiguration
Tailoring the curvature-directed lattice strain in GNRs along with optimal surface anchoring of molybdenum disulfide (MoS 2 ) quantum dots (QDs) can lead to a unique heterostructure with Pt-like HER activity (onset potential –60 mV). The curvature-induced electronic charge redistribution at the curved region in the graphene nanoribbons allows a facile GNR-MoS 2 interfacial charge transfer in the heterostructure, making the interfacial sulfur (S) more active towards the HER. Finally, the density functional theory (DFT) calculations confirmed electronically activated interfacial S-based catalytic centers in the curved GNR-based heterostructure leading to Pt-like HER activity.
Structured illumination with thermal imaging (SI-TI): A dynamically reconfigurable metrology for parallelized thermal transport characterization
The recent push for the “materials by design” paradigm requires synergistic integration of scalable computation, synthesis, and characterization. Among these, techniques for efficient measurement of thermal transport can be a bottleneck limiting the experimental database size, especially for diverse materials with a range of roughness, porosity, and anisotropy. Traditional contact thermal measurements have challenges with throughput and the lack of spatially resolvable property mapping, while non-contact pump-probe laser methods generally need mirror smooth sample surfaces and also require serial raster scanning to achieve property mapping. Here, we present structured illumination with thermal imaging (SI-TI), a new thermal characterization tool based on parallelized all-optical heating and thermometry. Experiments on representative dense and porous bulk materials as well as a 3D printed thermoelectric thick film (~50 μm) demonstrate that SI-TI (1) enables paralleled measurement of multiple regions and samples without raster scanning; (2) can dynamically adjust the heating pattern purely in software, to optimize the measurement sensitivity in different directions for anisotropic materials; and (3) can tolerate rough (~3 μm) and scratched sample surfaces. Here, this work highlights a new avenue in adaptivity and throughput for thermal characterization of diverse materials.
Reconfiguration of an Electrothermal-Arc Plasma Source for In Situ PMI Studies
An electrothermal-arc plasma source (ET-Arc) has been developed to produce transient plasma heat and particle fluxes similar to those produced by edge localized modes onto divertor plasma-facing components in tokamaks. The ET-Arc utilizes a capacitive discharge to send current through a 4-mm-diameter, 9-cm-long capillary source liner. The liner material is ablated to form a high-velocity plasma jet that impacts the target downstream. With the current discharge circuit configuration, pulse lengths are 1 to 2 ms in duration and deliver heat fluxes of 0.25 to 2.1 GW m-2. The plasma was previously characterized with optical emission spectroscopy (OES) on helium emission lines. The He I line ratios were interpreted with collisional radiative analysis to calculate ne and Te. The electron temperature and electron density ranged from Te = 1 to 5 eV and ne = 1022 to 1028 electrons/m3, respectively.
Reconfigurable Quantum Local Area Network Over Deployed Fiber
Not Available
Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware
This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment
The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.
Large-Signal Stability Analysis for Inverter-Based Dynamic Microgrids Reconfiguration
Not provided.