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The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO 2 has been accepted as one of the most promising resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO 2 -based memristors. Here, the conductive filament system in the amorphous HfO 2 -based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf 6 O and its crystalline surroundings (monoclinic or tetragonal HfO x ). The phase of the HfO x shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO 2 is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO 2 -based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications. Understanding the mechanism of the formation and rupture of conductive filaments in HfO 2 -based memristors is essential to solve the problem of scalability of the devices. Here, Zhang et al. visualize this process by tracking atomic-scale evolution of conductive filaments during resistive switching cycles.

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

The recent development of three‐dimensional semiconductor integration technology demands a key component—the ovonic threshold switching (OTS) selector to suppress the current leakage in the high‐density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first‐principles computation on disordered systems, a universal theory to explain the origin of mid‐gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short‐range order (SRO) and medium‐range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles. The 3D semiconductor fabrication technology requires an “ovonic threshold switching (OTS)” selector device to control the open and shut of each memory unit. The physics of these materials, however, has not been well understood due to complex structure of chalcogenide glass. The authors focus on the defect states which are responsible for OTS behaviors via machine learning of the large amount of structure data. The physical origin of OTS is revealed and the properties of these materials can be predicted, paving the way for the materials design toward high‐density memory integration.

As the prevailing non-volatile memory (NVM), flash memory offers mass data storage at high integration density and low cost. However, due to the ‘speed-retention-endurance’ dilemma, their typical speed is limited to ~microseconds to milliseconds for program and erase operations, restricting their application in scenarios with high-speed data throughput. Here, by adopting metallic 1T-Li x MoS 2 as edge contact, we show that ultrafast (10–100 ns) and robust (endurance>10 6 cycles, retention>10 years) memory operation can be simultaneously achieved in a two-dimensional van der Waals heterostructure flash memory with 2H-MoS 2 as semiconductor channel. We attribute the superior performance to the gate tunable Schottky barrier at the edge contact, which can facilitate hot carrier injection to the semiconductor channel and subsequent tunneling when compared to a conventional top contact with high density of defects at the metal interface. Our results suggest that contact engineering can become a strategy to further improve the performance of 2D flash memory devices and meet the increasing demands of high speed and reliable data storage. The speed-retention-endurance trade-off usually limits the performance of flash memory devices. Here, the authors report the realization of van der Waals flash memory cells based on 2H-MoS 2 semiconducting channels with phase-engineered 1T-Li x MoS 2 edge contacts, showing program/erasing speed of ~10/100 ns, endurance of >10 6 cycles and expected retention lifetime of >10 years.

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO 2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO 2 memristor including endurance (>10 12 ), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces. Constructing crossmodal in-sensor processing system based on high-performance flexible devices is important for the development of wearable human-machine interfaces. This work reports a bio-inspired spiking sensory neuron based on a flexible VO2 memristor and demonstrates a crossmodal in-sensor encoding and computing system.

Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe 2 ) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red–green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain–computer interfaces, and intelligent robotics. Realizing complex functions in artificial visual systems is challenging. Here, the authors report a human visual pathway-replicated hardware with a split floating gate crossbar arrays and related peripheral circuits, achieving colour-blindness processing, shape recognition, and self-driven motion tracking.

The exchange bias (EB) effect plays an undisputed role in the development of highly sensitive, robust, and high-density spintronic devices in magnetic data storage. However, the weak EB field, low blocking temperature, as well as the lack of modulation methods, seriously limit the application of EB in van der Waals (vdW) spintronic devices. Here, we utilized pressure engineering to tune the vdW spacing of the two-dimensional (2D) FePSe 3 /Fe 3 GeTe 2 heterostructures. The EB field ( H EB , from 29.2 mT to 111.2 mT) and blocking temperature ( T b , from 20 K to 110 K) are significantly enhanced, and a highly sensitive and robust spin valve is demonstrated. Interestingly, this enhancement of the EB effect was extended to exposed Fe 3 GeTe 2 , due to the single-domain nature of Fe 3 GeTe 2 . Our findings provide opportunities for the producing, exploring, and tuning of magnetic vdW heterostructures with strong interlayer coupling, thereby enabling customized 2D spintronic devices in the future. When an antiferromagnet is in close proximity to a ferromagnet, the antiferromagnet pins the spins of the ferromagnet, resulting in an exchange bias effect. This effect has been instrumental in the development of a variety of spintronic devices. Here, Haung et al. u s e pressure to tune the exchange bias effect in all van der Waals heterostructure composed of FePSe 3 /Fe 3 GeTe 2 .

The growing demand for scalable solar‐blind image sensors with remarkable photosensitive properties has stimulated the research on more advanced solar‐blind photodetector (SBPD) arrays. In this work, the authors demonstrate ultrahigh‐performance metal‐semiconductor‐metal (MSM) SBPDs based on amorphous (a‐) Ga2O3 via a post‐annealing process. The post‐annealed MSM a‐Ga2O3 SBPDs exhibit superhigh sensitivity of 733 A/W and high response speed of 18 ms, giving a high gain‐bandwidth product over 104 at 5 V. The SBPDs also show ultrahigh photo‐to‐dark current ratio of 3.9 × 107. Additionally, the PDs demonstrate super‐high specific detectivity of 3.9 × 1016 Jones owing to the extremely low noise down to 3.5 fW Hz−1/2, suggesting high signal‐to‐noise ratio. Underlying mechanism for such superior photoelectric properties is revealed by Kelvin probe force microscopy and first principles calculation. Furthermore, for the first time, a large‐scale, high‐uniformity 32 × 32 image sensor array based on the post‐annealed a‐Ga2O3 SBPDs is fabricated. Clear image of target object with high contrast can be obtained thanks to the high sensitivity and uniformity of the array. These results demonstrate the feasibility and practicality of the Ga2O3 PDs for applications in solar‐blind imaging, environmental monitoring, artificial intelligence and machine vision. Ultraviolet imaging technology is widely used in meteorology, medical science, and military science. For the first time, a high‐uniformity 32 × 32 solar‐blind image sensor array with outstanding imaging capability is demonstrated based on high‐performance Ga2O3 photodetectors. Schottky barrier lowering effect is experimentally revealed to attribute to the internal gain mechanism.

Arsenic is an essential dopant in conventional silicon-based semiconductors and emerging phase-change memory (PCM), yet the detailed functional mechanism is still lacking in the latter. Here, we fabricate chalcogenide-based ovonic threshold switching (OTS) selectors, which are key units for suppressing sneak currents in 3D PCM arrays, with various As concentrations. We discovered that incorporation of As into GeS brings >100 °C increase in crystallization temperature, remarkably improving the switching repeatability and prolonging the device lifetime. These benefits arise from strengthened As-S bonds and sluggish atomic migration after As incorporation, which reduces the leakage current by more than an order of magnitude and significantly suppresses the operational voltage drift, ultimately enabling a back-end-of-line-compatible OTS selector with >12 MA/cm 2 on-current, ~10 ns speed, and a lifetime approaching 10 10 cycles after 450 °C annealing. These findings allow the precise performance control of GeSAs-based OTS materials for high-density 3D PCM applications. Spin defects in semiconductors are promising for quantum technologies but understanding of defect formation processes in experiment remains incomplete. Here the authors present a computational protocol to study the formation of spin defects at the atomic scale and apply it to the divacancy defect in SiC.

Atomically thin hexagonal boron nitride ( h -BN) is often regarded as an elastic film that is impermeable to gases. The high stabilities in thermal and chemical properties allow h -BN to serve as a gas barrier under extreme conditions. Here, we demonstrate the isolation of hydrogen in bubbles of h -BN via plasma treatment. Detailed characterizations reveal that the substrates do not show chemical change after treatment. The bubbles are found to withstand thermal treatment in air, even at 800 °C. Scanning transmission electron microscopy investigation shows that the h -BN multilayer has a unique aligned porous stacking nature, which is essential for the character of being transparent to atomic hydrogen but impermeable to hydrogen molecules. In addition, we successfully demonstrated the extraction of hydrogen gases from gaseous compounds or mixtures containing hydrogen element. The successful production of hydrogen bubbles on h -BN flakes has potential for further application in nano/micro-electromechanical systems and hydrogen storage. Hexagonal boron nitride (hBN) is a two-dimensional material with wide band gap and high thermal and chemical stability. Here the authors demonstrate the formation and trapping of hydrogen gas bubbles in hBN interlayers upon plasma treatment, promising for extracting and storing hydrogen.