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Arsenic is one of the most frequently used elements for doping conventional silicon-based semiconductors and particularly in emerging phase-change-memory (PCM) chips, yet the detailed functional mechanism as well as its experimental demonstration is still lacking in the latter in spite of its wide application. Here, we fabricate chalcogenide-based ovonic threshold switching (OTS) selectors, which are essential units for suppressing sneak currents in 3D PCM arrays, with various As concentrations. We discovered that incorporation of 20 at. % As brings a more than 100 ℃ 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, as demonstrated by molecular-dynamics simulations. As a result, the addition of As reduces the leakage current by more than an order of magnitude and significantly suppresses the operational voltage drift, ultimately enabling a backend-of-the-line (BEOL)-compatible OTS selector with > 12 MA/cm2 on-current, >104 on/off ratio, ~ 10 ns speed, and a lifetime approaching 1010 cycles after 450 ℃ annealing. These performance enhancements can be explained by an As-induced increase of the band gap and of trap states, as determined by experimental photothermal deflection spectroscopy. Mechanistically, this is due to abundant defect states associated with Ge-Ge chains introduced by As-Ge and As-S bonds. These findings allow the use of precise control of the As concentration in OTS materials for improved-performance high-density 3D PCM applications.

An electric-field induced phase transition between semiconducting 2H and metallic 1T′ phases in a MoTe2 device is demonstrated for the first time. The phase transition exhibits faster switching compared with phase-change random-access memory (PCRAM), and shows more controllable switching than conventional memristive devices.

Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. However, owing to the physical limitation of the crossbar structure, although the memristor array is desirable for dense computation, it suffers from significant performance degradation in both energy and area efficiency when processing sparse linear algebra operations. In this work, we report a highly efficient in-memory sparse computing system based on the self-rectifying memristor, which originates from the joint effort of devices and algorithms and is used to solve computational modelling problems. This system is expected to have 74.9 – 19.6 TOPS / W energy efficiency for 2-bit to 8-bit sparse computation in computational modelling tasks. Compared to the previous in-memory computing hardware, our system provides over one order of magnitude improvement in energy efficiency with more than two orders of magnitude reduction in hardware overhead. This work could pave the road towards a highly efficient, unconventional computing solution for high-performance computing.

Nonlinear optical phenomena (NLOPs) in two-dimensional (2D) materials can be envisioned for neuromorphic functions at the device and related system level. But it has been attended rarely that transition among multi-energy states as one origin of NLOPs directly used for neuromorphic functions, which is assisted to understand nature of device-level nonlinear optical neuromorphic performance. Here we introduced a pump-probe-control technology to reveal multi-energy-state transition in multilayer molybdenum disulfide, enhancing nonlinear signals by transitions from two-photon absorption to synergistic excited states absorption and enabling an in-situ computing concept within an array of pure 2D flakes. Optical weighted average calculation and artificial neural network were realized without the fabrication of complex extrinsic structures, while preserving the femtosecond speed and femto-Joule power consumption, revealing the feasibility of pump-probe-control technology for nonlinear neuromorphic functions.

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 one-selector-one-resistor 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 local sensitive hash 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.

The data throughput in the von Neumann architecture-based computing system is limited by its separated processing and memory structure, and the mismatching speed between the two units. As a result, it is quite difficult to improve the energy efficiency in conventional computing system, especially for dealing with unstructured data. Meanwhile, artificial intelligence and robotics nowadays still behave poorly in autonomy, creativity, and sociality, which has been considered as the unimaginable computational requirement for sensorimotor skills. These two plights have urged the imitation and replication of the biological systems in terms of computing, sensing, and even motoring. Hence, the so-called neuromorphic system has drawn worldwide attention in recent decade, which is aimed at addressing the aforementioned needs from the mimicking of neural system. The recent developments on emerging memory devices, nanotechnologies, and materials science have provided an unprecedented opportunity for this aim.

Highly-efficient water splitting based on solar energy is one of the most attractive research focuses in the energy field. Searching for more candidate photocatalysts that can work under visible-light irradiation are highly demanded. Herein, using first principle calculations based on density functional theory, we predict that the two dimensional silicon chalcogenides, i.e. SiX (X=S, Se, Te) monolayers, as semiconductors with 2.43 eV~3.00 eV band gaps, exhibit favorable band edge positions for photocatalytic water splitting. The optical adsorption spectra demonstrate that the SiX monolayers have pronounced optical absorption in the visible light region. Moreover, the band gaps and band edge positions of silicon chalcogenides monolayers can be tuned by applying biaxial strain or increasing the number of layers, in order to better fit the redox potentials of water. The combined novel electronic, high carrier mobility, and optical properties render the two dimensional SiX a promising photocatalyst for water splitting., 5 figures, 2 tables

In-memory computing based on memristive logic is considered as a prospective non von Neumann computing paradigm. In this letter, we systematically analyze the four-variable logic method and map it into the operation of two anti-serial complementary memristors in the crossbar array architecture. Arbitrary Boolean logic can be implemented within three cycles with the experimental evidence of reconfigurable NAND, NOR, and XOR logic using Pt/HfO2/TiN devices. Taking advantage of the functional flexibility, a parallel 1-bit full adder that can be realized in 8 cycles within a $\textsf {4}\times \textsf {3}$ array has been designed and verified in simulation.

Two-dimensional materials with high carrier mobility and tunable magnetism are in high demand for nanoelectronic and spintronic applications. Herein, we predict a novel two-dimensional monolayer KTlO that possesses an indirect band gap of 2.25 eV (based on HSE06 calculations) and high carrier mobility (450 cm2 V-1 s-1 for electrons and 160 cm2 V-1 s-1 for holes) by means of ab initio calculations. The electron mobility can be increased up to 26 280 cm2 V-1 s-1 and 54 150 cm2 V-1 s-1 for bilayer and trilayer KTlO, respectively. The KTlO monolayer has a calculated cleavage energy of 0.56 J m-2, which suggests exfoliation of the bulk material as a viable means for the preparation of mono- and few-layer materials. Remarkably, the KTlO monolayer demonstrates tunable magnetism and half-metallicity with hole doping, which are attributed to the novel Mexican-hat-like bands and van Hove singularities in its electronic structure. Furthermore, monolayer KTlO exhibits moderate optical absorption over the visible light and ultraviolet regions. The band gap value and band characteristics of monolayer KTlO can be substantially manipulated by biaxial and uniaxial strains to meet the requirement of various applications. All these novel properties make monolayer KTlO a promising functional material for future nanoelectronic and spintronic applications.

Although good performance has been reported in shallow neural networks, the application of memristor synapses towards realistic deep neural networks has met more stringent requirements on the synapse properties, particularly the high precision and linearity of the synaptic analog weight tuning. In this study, a LiAlOX memristor synapse was fabricated and optimized to address these demands. By delicately tuning the initial conductance states, 120-level continuously adjustable conductance states were obtained and the nonlinearity factor was substantially reduced from 8.96 to 0.83. The significant enhancements were attributed to the reduced Schottky barrier height (SBH) between the filament tip and the electrode, which was estimated from the measured I-V curves. Furthermore, a deep neural network for realistic action recognition task was constructed, and the recognition accuracy was found to be increased from 15.1% to 91.4% on the Weizmann video dataset by adopting the above-described device optimization method.

Electrocatalysts with high activities toward multiple electrode reactions are scarce and therefore highly sought. Here, we investigate the electrocatalytic performance of the two-dimensional (2D) Pt

The next-generation nonvolatile memory storage may well be based on resistive random access memories (RRAMs). $$\hbox {TiO}_{2}$$ and $$\hbox {HfO}_{2}$$ have been widely used as the resistive switching layer for RRAM devices. However, the electronic properties of the filament-to-dielectric interfaces are still not well understood yet, compared to those of the electrodes and the dielectric. In this work, we study the electronic structures of three typical filament and dielectric structures, $$\hbox {Ti}_{4}\hbox {O}_{7}/\hbox {TiO}_{2}$$ , $$\hbox {Hf}_{2}\hbox {O}_{3}/\hbox {HfO}_{2}$$ and $$\hbox {Hf}/\hbox {HfO}_{2}$$ , using ab initio calculations. We implement the GGA-1/2 method, which rectifies the band gaps of GGA through self-energy correction. Our calculation predicts an ohmic contact for the $$\hbox {Ti}_{4}\hbox {O}_{7}/\hbox {TiO}_{2}$$ interface, where the defective $$\hbox {Ti}_{4}\hbox {O}_{7}$$ phase was experimentally identified as the filament composition in $$\hbox {TiO}_{2}$$ . However, there is a finite Schottky barrier existing in either $$\hbox {Hf}_{2}\hbox {O}_{3}/\hbox {HfO}_{2}$$ interface (1.96 eV) or $$\hbox {Hf}/\hbox {HfO}_{2}$$ interface (0.61 eV), the two probable filament–dielectric configurations in hafnia-based RRAM. Our results suggest that the distinct filament-to-dielectric band alignments in $$\hbox {TiO}_{x}$$ and $$\hbox {HfO}_{x}$$ systems account for the much larger resistance window for the latter.

Nonvolatile stateful logic through RRAM is a promising route to build in-memory computing architecture. In this letter, a logic methodology based on 1T1R structure has been proposed to implement functionally complete Boolean logics. Arbitrary logic functions could be realized in two steps: initialization and writing. An additional read step is required to read out the logic result, which is in situ stored in the nonvolatile resistive state of the memory. Cascade problem in building larger logic circuits is also discussed. Our 1T1R logic device and operation method could be beneficial for massive integration and practical application of RRAM-based logic.

Resistive random access memory (RRAM) based reconfigurable logic provides a temporal programmable dimension to realize Boolean logic functions and is regarded as a promising route to build non-von Neumann computing architecture. In this work, a reconfigurable operation method is proposed to perform nonvolatile sequential logic in a HfO2-based RRAM array. Eight kinds of Boolean logic functions can be implemented within the same hardware fabrics. During the logic computing processes, the RRAM devices in an array are flexibly configured in a bipolar or complementary structure. The validity was demonstrated by experimentally implemented NAND and XOR logic functions and a theoretically designed 1-bit full adder. With the trade-off between temporal and spatial computing complexity, our method makes better use of limited computing resources, thus provides an attractive scheme for the construction of logic-in-memory systems.

Scintillators are widely utilized for radiation detections in many fields, such as nondestructive inspection, medical imaging, and space exploration. Lead halide perovskite scintillators have recently received extensive research attention owing to their tunable emission wavelength, low detection limit, and ease of fabrication. However, the low light yields toward X-ray irradiation and the lead toxicity of these perovskites severely restricts their practical application. A novel lead-free halide is presented, namely Rb

An X-ray detector with high sensitivity would be able to increase the generated signal and reduce the dose rate; thus, this type of detector is beneficial for applications such as medical imaging and product inspection. The inorganic lead halide perovskite CsPbBr

X-ray detectors are broadly utilized in medical imaging and product inspection. Halide perovskites recently demonstrate excellent performance for direct X-ray detection. However, ionic migration causes large noise and baseline drift, limiting the detection and imaging performance. Here we largely eliminate the ionic migration in cesium silver bismuth bromide (Cs2AgBiBr6) polycrystalline wafers by introducing bismuth oxybromide (BiOBr) as heteroepitaxial passivation layers. Good lattice match between BiOBr and Cs2AgBiBr6 enables complete defect passivation and suppressed ionic migration. The detector hence achieves outstanding balanced performance with a signal drifting one order of magnitude lower than all previous studies, low noise (1/f noise free), a high sensitivity of 250 µC Gy air−1 cm–2, and a spatial resolution of 4.9 lp mm−1. The wafer area could be easily scaled up by the isostatic-pressing method, together with the heteroepitaxial passivation, strengthens the competitiveness of Cs2AgBiBr6-based X-ray detectors as next-generation X-ray imaging flat panels., Ionic migration degrades not only the characteristics of halide perovskite solar cells, but also those of perovskite X-ray detectors. Here Yang et al. employ heteroepitaxial BiOBr to passivate Cs2AgBiBr6 double perovskite, which suppresses ionic migration and obtain high performance X-ray detectors.

We studied the pseudo-homeothermic synaptic behaviors by integrating complimentary metal-oxide-semiconductor-compatible materials (hafnium oxide, aluminum oxide, and silicon substrate). A wide range of temperatures, from 25 °C up to 145 °C, in neuronal dynamics was achieved owing to the homeothermic properties and the possibility of spike-induced synaptic behaviors was demonstrated, both presenting critical milestones for the use of emerging memristor-type neuromorphic computing systems in the near future. Biological synaptic behaviors, such as long-term potentiation, long-term depression, and spike-timing-dependent plasticity, are developed systematically, and comprehensive neural network analysis is used for temperature changes and to conform spike-induced neuronal dynamics, providing a new research regime of neurocomputing for potentially harsh environments to overcome the self-heating issue in neuromorphic chips.

Understanding the physical mechanisms that govern the ion and fluidic transport in salt-concentration-based nanochannel/nanopore systems is essential for the potential applications in bioanalysis. One central challenge is to interpret the observed four-stage change from osmosis to the reverse one with increasing salt gradient. Here we provide a unified model that outlines the intriguing role of two competing factors, the exclusion- and diffusion-induced electrical potentials. We demonstrate theoretically a direction control of a hydrodynamic flow via the salt gradient. Based on this, we also propose a salt-gradient approach for regulating DNA motion in nanochannels that enables voltage-free single-molecule capture with a significantly low translocation speed. The present method would be used as a useful protocol to overcome the key hurdle of tailoring the capture-to-translocation dynamics of polynucleotides for nanopore sequencing.

RRAM is a promising candidate to construct in-memory computing architecture which can break through the von Neumann bottleneck. Taking advantage of the CMOS compatible 1T1R RRAM, functionally complete Boolean logics can be realized within two steps in a single unit that can suppress sneak pass problem and avoid cascading problem partially. In addition, an 8-bit pre- calculation adder with low computation complexity is designed and demonstrated experimentally to verify the feasibility and efficiency of 1T1R based in-memory computing architecture, which is applicable to future energy-efficient information processing systems.

The tunable disorder of vacancies upon annealing is an important character of crystalline phase-change material Ge2Sb2Te5 (GST). A variety of resistance states caused by different degrees of disorder can lead to the development of multilevel memory devices, which could bring a revolution to the memory industry by significantly increasing the storage density and inspiring the neuromorphic computing. This work focuses on the study of disorder-induced carrier localization which could result in multiple resistance levels of crystalline GST. To analyze the effect of carrier localization on multiple resistant levels, the intrinsic field effect (the change in surface conductance with an applied transverse electric field) of crystalline GST was measured, in which GST films were annealed at different temperatures. The field effect measurement is an important complement to conventional transport measurement techniques. The field effect mobility was acquired and showed temperature activation, a hallmark of carrier localization. Based on the relationship between field effect mobility and annealing temperature, we demonstrate that the annealing shifts the mobility edge towards the valence-band edge, delocalizing more carriers. The insight of carrier transport in multilevel crystalline states is of fundamental relevance for the development of multilevel phase change data storage.

Resistive random access memory (RRAM) based reconfigurable logic provides a temporal programmable dimension to realize Boolean logic functions and is regarded as a promising route to build non-von Neumann computing architecture. In this work, a reconfigurable operation method is proposed to perform nonvolatile sequential logic in a HfO

To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO

Recent experiments showed that by fabricating nanofluidic channels with hydrophobic materials, the measured amplitudes of both electroosmotic flow (EOF) and ionic current deviated significantly from the conventional electrokinetic modeling indication. Among these unexpected observations, the complicated dependence of EOF on the surface charge concentration of the channel wall remains most confusing. In this work we give a complete and unified picture for the phenomena by outlining the competing two mechanisms in the water-depletion layer around the channel wall: the decreasing trend of fluidic flow due to the redistribution of net charges, and the increasing trend because of the reduced solution viscosity there. Our quantitative evaluation illustrates that the alternate dominating by the two mechanisms leads to the observed transport behaviors. Furthermore, by considering the decreasing of ionic mobility in the depletion layer, our calculations show quantitative agreement with the latest experiments using BN nanotube channels.