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Abstract Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability. Because the unique band dispersions of 2D materials can provide hidden paths to boost nonlinear responses independently, here we introduced a new free-space optical computing concept within a bare molybdenum disulfide array. This system can preserve high modulation performance with fast speed, low energy consumption, and high signal-to-noise ratio. Due to the freedom from the restrictions of fixed photonic structures, the tunability is also enhanced through the synergistic encodings of the 2D cells and the excitation pulses. The computing mechanism of transition from two-photon absorption to synergistic excited states absorption intrinsically improved the modulation capability of nonlinear optical responses, revealed from the relative transmittance modulated by a pump-probe-control strategy. Optical artificial neural network (ANN) and digital processing were demonstrated, revealing the feasibility of the free-space optical computing based on bare 2D materials toward neuromorphic applications.

Abstract 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 (WSe2) 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.

Abstract 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 VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (

Abstract Crystallization speed of phase change material is one of the main obstacles for the application of phase change memory (PCM) as storage class memory in computing systems, which requires the combination of nonvolatility with ultra‐fast operation speed in nanoseconds. Here, we propose a novel approach to speed up crystallization process of the only commercial phase change chalcogenide Ge2Sb2Te5 (GST). By employing TiO2 as the dielectric layer in phase change device, operation speed of 650 ps has been achieved, which is the fastest among existing representative PCM, and is comparable to the programing speed of commercial dynamic random access memory (DRAM). Because of its octahedral atomic configuration, TiO2 can provide nucleation interfaces for GST, thus facilitating the crystal growth at the determinate interface area. Ti–O–Ti–O four‐fold rings on the (110) plane of tetragonal TiO2 is critical for the fast‐atomic rearrangement in the amorphous matrix of GST that enables ultra‐fast operation speed. The significant improvement of operation speed in PCM through incorporating standard dielectric material TiO2 in DRAM paves the way for the application of phase change memory in high performance cache‐type data storage.

Abstract Due to its non‐invasive nature, ultrasound has been widely used for neuromodulation in biological systems, where its application influences the synaptic weights and the process of neurotransmitter delivery. However, such modulation has not been emulated in physical devices. Memristors are ideal electrical components for artificial synapses, but up till now they are hardly reported to respond to ultrasound signals. Here we design and fabricate a HfOx‐based memristor on 64°Y‐X LiNbO3 single crystal substrate, and successfully realize artificial synapses modulation by shear‐horizontal surface acoustic wave (SH‐SAW). It is a prominent short‐term resistance modulation, where ultrasound has been shown to cause resistance drop for various resistance states, which could fully recover after the ultrasound is shut off. The physical mechanism illustrates that ultrasound induced polarization potential in the HfOx dielectric layer acts on the Schottky barrier, leading to the resistance drop. The emulation of neuron firing frequency modulation through ultrasound signals is demonstrated. Moreover, the joint application of ultrasound and electric voltage yields fruitful functionalities, such as the enhancement of resistance window and synaptic plasticity through ultrasound application. All these promising results provide a new strategy for artificial synapses modulation, and also further advance neuromorphic devices toward system applications.

Abstract 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/cm2 on-current, ~10 ns speed, and a lifetime approaching 1010 cycles after 450 °C annealing. These findings allow the precise performance control of GeSAs-based OTS materials for high-density 3D PCM applications.

Abstract 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-LixMoS2 as edge contact, we show that ultrafast (10–100 ns) and robust (endurance>106 cycles, retention>10 years) memory operation can be simultaneously achieved in a two-dimensional van der Waals heterostructure flash memory with 2H-MoS2 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.

Abstract 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) FePSe3/Fe3GeTe2 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 Fe3GeTe2, due to the single-domain nature of Fe3GeTe2. 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.

Nanopore sensor technology is widely used in biomolecular detection due to its advantages of low cost and easy operation. In a variety of nanopore manufacturing methods, controlled dielectric breakdown has the advantages of a simple manufacturing process and low cost under the premise of ensuring detection performance. In this paper, we have made enhancements to the applied pulses in controlled dielectric breakdown and utilized the improved dielectric breakdown technique to fabricate silicon nitride nanopores with diameters of 5 to 15 nm. Our improved fabrication method offers the advantage of precise control over the nanopore diameter (±0.4 nm) and enhances the symmetry of the nanopore. After fabrication, we performed electrical characterization on the nanopores, and the IV characteristics exhibited high linearity. Subsequently, we conducted detection experiments for DNA and protein using the prepared nanopores to assess the detection performance of the nanopores fabricated using our method. In addition, we also give a physical model of molecule translocation through the nanopores to give a reasonable explanation of the data processing results.

In-memory computing (IMC) using emerging nonvolatile devices has received considerable attention due to its great potential for accelerating artificial neural networks and machine learning tasks. As the basic concept and operation modes of IMC are now well established, there is growing interest in employing its wide and general application. In this perspective, the path that leads memristive IMC to general-purpose machine learning is discussed in detail. First, we reviewed the development timeline of machine learning algorithms that employ memristive devices, such as resistive random-access memory and phase-change memory. Then we summarized two typical aspects of realizing IMC-based general-purpose machine learning. One involves a heterogeneous computing system for algorithmic completeness. The other is to obtain the configurable precision techniques for the compromise of the precision-efficiency dilemma. Finally, the major directions and challenges of memristive IMC-based general-purpose machine learning are proposed from a cross-level design perspective.

Abstract Two‐dimensional material‐based field‐effect transistors (2DM‐FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM‐FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi‐Fermi‐level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer‐QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM‐FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non‐monotonic drain conductance characteristics. A three‐bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM‐FET‐based integrated circuits.

ABSTRACTInspired 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.

Abstract Similarity search, that is, finding similar items in massive data, is a fundamental computing problem in many fields such as data mining and information retrieval. However, for large‐scale and high‐dimension data, it suffers from high computational complexity, requiring tremendous computation resources. Here, based on the low‐power self‐selective memristors, for the first time, we propose an in‐memory search (IMS) system with two innovative designs. First, by exploiting the natural distribution law of the devices resistance, a hardware locality sensitive hashing encoder has been designed to transform the real‐valued vectors into more efficient binary codes. Second, a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel. Our IMS system demonstrated a 168× energy efficiency improvement over all‐transistors counterparts in clustering and classification tasks, while achieving a software‐comparable accuracy, thus providing a low‐complexity and low‐power solution for in‐memory data mining applications.

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.

The selector plays an important role in solving the leakage current issue in a large-scale memory crossbar array. Especially, the bidirectional selector has a broader application scope than the unidirectional one, since it can be connected with both unipolar and bipolar memory devices. In this letter, two engineering approaches to realize bidirectional tunneling barrier selectors were investigated: the crested barrier and variable oxide thickness (VARIOT). It is found that the selectors based on the crested barrier exhibit much higher nonlinearity and lower off-current than those fitting the VARIOT approach. The associated tunneling mechanisms have been proposed to explain the electrical properties of the crested barrier devices. The excellent multilayer barrier bidirectional selector has been realized in the ZnO/Ta2O5/ZnO stack with pA-level off current and high nonlinearity (>104). It is worth noting that the present ZnO/Ta2O5/ZnO selector shows the best performances compared with previously reported tunnel barrier selectors.

Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)‐based in‐memory search, achieving software‐comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in‐memory search with a high‐precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor‐based CAM, and several similar images are fetched for further high‐precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50×) compared to the CPU and higher search accuracy than the fully in‐memory computing method, thus alleviating the efficiency bottleneck of CPU‐based image retrieval.

External‐memory‐based few‐shot learning has emerged as a promising solution to deal with the shortage of labeled samples in traditional deep neural networks (DNNs). The external memories here are used to perform explicit similarity calculations. Despite several encouraging schemes were proposed to merge data storage and similarity calculation using non‐volatile memories, two major challenges, including the long encoding bits and the degraded accuracy of hardware implementations, remain to be tackled. Herein, a cosine‐similarity‐based few‐shot learning method with analog memristors is designed. A regulation block in the embedding net is used as a hardware–software co‐optimization unit to reduce the encoding lengths of the embedding vectors. Then, a lifelong memory adaptation method, termed the online prototype augmentation, is proposed for the first time to promote the accuracy of extremely low‐shot learning problems. Results show that the encoding bits are 4–32 times smaller than popular schemes. Using the Omniglot dataset, record accuracy (98.3% and 97.3% for the 5‐way 5‐shot and 5‐way 1‐shot problems, respectively) can be obtained for the few‐shot learning with memristive cosine similarities. In addition, the online prototype augmentation method leads to at least ≈ 2% improvement in accuracy for 1‐shot learning, which is outstanding compared to prior studies.

Abstract The wake‐up phenomenon widely exists in hafnia‐based ferroelectric capacitors, which causes device parameter variation over time. Crystallization at a higher temperature has been reported to be effective in eliminating wake‐up, but high temperature may yield the monoclinic phase or generate more oxygen vacancies. In this work, a unidirectional annealing method is proposed for the crystallization of Hf0.5Zr0.5O2 (HZO) superlattice ferroelectrics, which involves heating from the Pt/ZrO2 interface side. It is demonstrated that 600 °C annealing only leads to a moderate content of monoclinic phase in HZO, and the TiN/HZO/Pt capacitor exhibits wake‐up free nature and a switchable remnant polarization value of 27.4 µC cm−2. On the other hand, heating from the TiN/HfO2 side, or using 500 °C annealing temperature, could yield ferroelectric devices that require a wake‐up process. The special configuration of Pt/ZrO2 is verified by comparative studies with several other superlattice structures and HZO solid‐state solutions. It is discovered that heating from the Pt/HfO2 side at 600 °C leads to high leakage current and a memristor behavior. The mechanisms of ferroelectric phase stabilization and memristor formation have been discussed. The unidirectional heating method can also be useful for other hafnia‐based ferroelectric devices.

Understanding the mechanism of the formation and rupture of conductive filaments in HfO2-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.

Biomimetic machine vision systems require structures, including an artificial retina to capture and process images, and an artificial network that imitates the cortex for visual inspection and identification tasks. One of the key challenges for biomimetic perceptional machine visual systems is the self‐adaptive behavior of the retina, which is known as the ability to adjust and eventually perceive objects through a wide range of environmental light intensities. To achieve this, current systems use either complex processing algorithms or specific devices with optoelectronic properties which results in complexity and less efficiency. Herein, an artificial retina circuit is reported to imitate the adaptation process to light (photopic and scotopic adaptation) of the perceptional biovisual system. The implementation of an artificial Hodgkin–Huxley neuron circuit based on adaptive VO2 memristors has helped the artificial retina to successfully demonstrate time‐varying activation and inhibition characteristics. The adaptable perception function under different illumination conditions is faithfully achieved by the artificial HH neuron based on the self‐adaptability of the memristor. The function is consistent with the dynamic light amplitude ranges of biological vision systems. More importantly, this development shows a pervasive way to design and fabricate a universal environmentally adaptable artificial system.

Nonvolatile memory (NVM)-based convolutional neural networks (NvCNNs) have received widespread attention as a promising solution for hardware edge intelligence. However, there still exist many challenges in the resource-constrained conditions, such as the limitations of the hardware precision and cost and, especially, the large overhead of the analog-to-digital converters (ADCs). In this study, we systematically analyze the performance of NvCNNs and the hardware restrictions with quantization in both weight and activation and propose the corresponding requirements of NVM devices and peripheral circuits for multiply–accumulate (MAC) units. In addition, we put forward an in situ sparsity-aware processing method that exploits the sparsity of the network and the device array characteristics to further improve the energy efficiency of quantized NvCNNs. Our results suggest that the 4-bit-weight and 3-bit-activation (W4A3) design demonstrates the optimal compromise between the network performance and hardware overhead, achieving 98.82% accuracy for the Modified National Institute of Standards and Technology database (MNIST) classification task. Moreover, higher-precision designs will claim more restrictive requirements for hardware nonidealities including the variations of NVM devices and the nonlinearities of the converters. Moreover, the sparsity-aware processing method can obtain 79%/53% ADC energy reduction and 2.98×/1.15× energy efficiency improvement based on the W8A8/W4A3 quantization design with an array size of 128 × 128.

Abstract The adjustable conductance of a two‐terminal memristor in a crossbar array can facilitate vector‐matrix multiplication in one step, making the memristor a promising synapse for efficiently implementing neuromorphic computing. To achieve controllable and gradual switching of multi‐level conductance, important for neuromorphic computing, a theoretical design of a superlattice‐like (SLL) structure switching layer for the multi‐level memristor is proposed and validated, refining the growth of conductive filaments (CFs) and preventing CFs from the abrupt formation and rupture. Ti/(HfOx/AlOy)SLL/TiN memristors are shown with transmission electron microscopy , X‐ray photoelectron spectroscopy , and ab initio calculation findings corroborate the SLL structure of HfOx/AlOy film. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factor α = 1.06), and the convolutional neural network based on the SLL memristive synapse improves the handwritten digit recognition accuracy to 94.95%. Meanwhile, this improved synaptic device has a fast operating speed (30 ns), a long data retention time (≥ 104 s at 85 ℃), scalability, and CMOS process compatibility. Finally, its physical nature is explored and the CF evolution process is characterized using nudged elastic band calculations and the conduction mechanism fitting. In this work, as an example the HfOx/AlOy SLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.

Abstract 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.

Abstract The rise of emerging technologies such as Big Data, the Internet of Things, and artificial intelligence, which requires efficient power schemes, is driving brainstorming in data computing and storage technologies. In this study, merely relying on the fundamental structure of two memristors and a resistor, arbitrary Boolean logic can be reconfigured and calculated in two steps, while no additional voltage sources are needed beyond “±VP” and 0, and all state reversals are based on memristor set switching. Utilizing the proposed logic scheme in an elegant form of unity structure and minimum cost, the implementation of a 1‐bit adder is demonstrated economically, and a promising circuit scheme for the N‐bit adder is exhibited. Some critical issues including the crosstalk problem, energy consumption, and peripheral circuits are further simulated and discussed. Compared with existing works on memristive logic, such methods support building a memristor‐based digital in‐memory calculation system with high functional reconfigurability, simple voltage sources, and low power and area consumption.

In recent years, hafnia-based ferroelectrics have attracted enormous attention due to their capability of maintaining ferroelectricity below 10 nm thickness and excellent compatibility with microelectronics flow lines. However, the physical origin of their ferroelectricity is still not fully clear, although it is commonly attributed to a polar Pca 2 _1 orthorhombic phase. The high-temperature paraelectric phases (the tetragonal phase or the cubic phase) do not possess a soft mode at the Brillouin zone center, thus the ferroelectric distortion has to be explained in terms of trilinear coupling among three phonon modes in the tetragonal phase. It is necessary to explore new materials with possible ferroelectricity due to the polar Pca 2 _1 phase, which in turn should be very helpful in evaluating the microscopic theory for ferroelectric hafnia. In this work, based on the idea of the Materials Genome Engineering, a series of hafnia-like ferroelectrics have been found, exemplified by LaSeCl, LaSeBr, LuOF and YOF, which possess adequate spontaneous polarization values and also relatively favorable free energies for the polar phase. Their common features and individual differences are discussed in detail. In particular, a promising potential ferroelectric material, Pca 2 _1 phase LuOF, is predicted and recommended for further experimental synthesis and investigation.

Inspired by competitive rules of the nature, competitive learning contributes to the specialization of the human brain and the general creativity of mankind. However, the construction of hardware competitive learning neural network still faces great challenges due to the lack of an accurate distance computation method and a self‐adaptive in situ training scheme. Herein, a fully memristive Euclidean distance (ED) engine based on analog multiply‐accumulate operation in a 32 × 32 TiN/TaO x /HfO x /TiN 1T1R array is demonstrated. The dual‐layer devices perform multilevel modulation under the target‐aware programming method with excellent read linearity in a dynamic range of 10–100 μS. The ED calculation is verified experimentally on a test board with an O(1) temporal complexity. Furthermore, in situ training and offline inference schemes for competitive learning, based on the ED engine, are developed and the simulated results show comparable success rates with those obtained by the CPU‐based software. Compared with a state‐of‐the‐art RTX6000 GPU (0.5 TOPS W−1), the energy efficiency of competitive learning models on ED engines can yield 100× improvements by utilizing optimized memristive devices.

Abstract 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.

Abstract Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off‐state current. To this end, a crossbar device using 2D HfSe2 is fabricated, and then the top layers are oxidized into “high‐k” dielectric HfSexOy via oxygen plasma treatment, so that the cell resistance can be remarkably increased. This two‐terminal Ti/HfSexOy/HfSe2/Au device exhibits excellent forming‐free resistive switching performance with high switching speed (

The state-of-the-art phase-change memory is usually composed of ovonic threshold switching (OTS) material and ovonic memory switching (OMS) material for selective and data storage, respectively. OMS materials have been intensely studied, while the knowledge of the OTS mechanism is still inadequate. In this article, we have explored the local structure and electronic property of a simple OTS material, the amorphous (a-) SiTe, by first-principles calculations. The results reveal that most of the atoms in a-SiTe obey the “8-N” rule in contrast to a-GeTe, a well-studied OMS material. 76.5% of Si-centered configurations are in the form of randomly distributed tetrahedral clusters, while Te-centered configurations are relatively disordered without notable conformation. Furthermore, a large number of fivefold rings are found in a-SiTe. All of these structural characteristics lead to the high stability of a-SiTe, prohibiting its crystallization. In addition, the p state of Te also contributes much to the mid-gap states, which may be relevant for OTS behavior. Our findings provide an in-depth understanding of the structural signature and electronic properties of a-SiTe, having important implications for the design and applications of OTS materials.

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.

Abstract Superlattices consisting of stacked nano-sized GeTe and Sb2Te3 blocks have attracted considerable attention owing to their potential for an efficient non-melting switching mechanism, associated with complex bonding between blocks. Here, we propose possible atomic models for the superlattices, characterized by different interfacial bonding types. Based on interplanar distances extracted from ab initio calculations and electron diffraction measurements, we reveal possible intercalation of dangling bonds as the GeTe content in the superlattice increases. The dangling bonds were further confirmed by X-ray photoelectron spectroscopy, anisotropic temperature dependent resistivity measurements down to 2 K and magnetotransport analysis. Changes of partially coherent decoupled topological surfaces states upon dangling bonds varying contributed to the switching mechanism. Furthermore, the topological surface states controlled by changing the bonding between stacking blocks may be optimized for multi-functional applications.

All optical switching is an emerging recording technology by using femtosecond laser to realize ultrafast magnetization manipulation for super-high magnetic storage. TbCo films possess ferrimagnetic structure and large perpendicular anisotropy and thus have the potential to be used as a AOS material. In this paper, we simulate the ultrafast magnetization reversal of TbCo films under 100fs pulse laser with a general theoretical framework and the simulated results show that TbCo films can generate magnetization reversal at the timescale of several ps. Co-sublattice completes demagnetization and subsequently triggers magnetize reversal within 1.0ps, whereas Tb- sublattice begins to magnetize reversal more than 4.0ps. Moreover, large laser power intensity easily induce the complete demagnetization and subsequent magnetization reversal regardless of the composition of TbCo films.

First principles calculations based on density functional theory (DFT) are performed to investigate the structural, electronic and magnetic properties of wurtzite NiO (w-NiO) and w-NiO/ZnO (0001) interface. By applying DFT+U method we discover that wurtzite NiO retains the half-metallic behavior in the w-NiO/ZnO (0001) heterostructure. Through analyses of density of state, charge population and magnetic moments, we find the half-metallicity is weakened around the interface but the interface effect is quite localized. More over the interface configuration keeps a ferromagnetic ground state as bulk w-NiO does. Based on the simulations of epitaxial growth case, w-NiO is predicted to be a promising candidate for electrode injecting spin polarized currents.