Nanochannel-Based Transport in an Interfacial Memristor Can Emulate the Analog Weight Modulation of Synapses.

By exploiting novel transport phenomena such as ion selectivity at the nanoscale, it has been shown that nanochannel systems can exhibit electrically controllable conductance, suggesting their potential use in neuromorphic devices. However, several critical features of biological synapses, particularly their conductance modulation, which is both memorable and gradual, have rarely been reported in these types of systems due to the fast flow property of typical inorganic electrolytes. In this work, we demonstrate that electrically manipulating the nanochannel conductance can result in nonvolatile conductance tuning capable of mimicking the analog behavior of synapses by introducing a room-temperature ionic liquid (IL) and a KCl solution into the two ends of a nanochannel system. The gradual conductance-tuning mechanism is identified through fluorescence measurements as the voltage-induced movement of the interface between the immiscible IL and KCl solution, while the successful memorization of the conductance tuning is ascribed to the large viscosity of the IL. We applied a nanochannel-based synapse to a handwritten digit-recognition task, reaching an accuracy of 94%. These promising results provide important guidance for the future design of nanochannel-based neuromorphic devices and the manipulation of nanochannel transport for computing.

Reconfigurable logic in nanosecond Cu/GeTe/TiN filamentary memristors for energy-efficient in-memory computing.

Owing to the capability of integrating the information storage and computing in the same physical location, in-memory computing with memristors has become a research hotspot as a promising route for non von Neumann architecture. However, it is still a challenge to develop high performance devices as well as optimized logic methodologies to realize energy-efficient computing. Herein, filamentary Cu/GeTe/TiN memristor is reported to show satisfactory properties with nanosecond switching speed (<60 ns), low voltage operation (<2 V), high endurance (>104 cycles) and good retention (>104 s @85 °C). It is revealed that the charge carrier conduction mechanisms in high resistance and low resistance states are Schottky emission and hopping transport between the adjacent Cu clusters, respectively, based on the analysis of current-voltage behaviors and resistance-temperature characteristics. An intuitive picture is given to describe the dynamic processes of resistive switching. Moreover, based on the basic material implication (IMP) logic circuit, we proposed a reconfigurable logic method and experimentally implemented IMP, NOT, OR, and COPY logic functions. Design of a one-bit full adder with reduction in computational sequences and its validation in simulation further demonstrate the potential practical application. The results provide important progress towards understanding of resistive switching mechanism and realization of energy-efficient in-memory computing architecture.

Mimicking the brain functions of learning, forgetting and explicit/implicit memories with SrTiO3-based memristive devices.

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-SrTiO3/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the "time-saving" effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO3 interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.

Impact of Water-Depletion Layer on Transport in Hydrophobic Nanochannels.

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.

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing.

Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102  Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.

Ultrafast and stable phase transition realized in MoTe2-based memristive devices.

Phase engineering of two-dimensional transition metal dichalcogenides has received increasing attention in recent years due to its atomically thin nature and polymorphism. Here, we first realize an electric-field-induced controllable phase transition between semiconducting 2H and metallic 1T' phases in MoTe2 memristive devices. The device performs stable bipolar resistive switching with a cycling endurance of over 105, an excellent retention characteristic of over 105 s at an elevated temperature of 85 °C and an ultrafast switching of ∼5 ns for SET and ∼10 ns for RESET. More importantly, the device works in different atmospheres including air, vacuum and oxygen, and even works with no degradation after being placed in air for one year, indicating excellent surrounding and time stability. In situ Raman analysis reveals that the stable resistive switching originates from a controllable phase transition between 2H and 1T' phases. Density functional theory calculations reveal that the Te vacancy facilitates the phase transition in MoTe2 through decreasing the barrier between 2H and 1T' phases, and serving as nucleation sites due to the elimination of repulsive forces. This electric-field-induced controllable phase transition in MoTe2 devices offers new opportunities for developing reliable and ultrafast phase transition devices based on atomically thin membranes.

Threshold switching memristor-based stochastic neurons for probabilistic computing.

Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%.

A new family of two-dimensional ferroelastic semiconductors with negative Poisson's ratios.

Two-dimensional (2D) materials with both ferroelasticity and negative Poisson's ratios have attracted intensive interest, but it is very rare to have both ferroelasticity and negative Poisson's ratios in a single material. Directional positive and negative Poisson's ratios in a switchable ferroelastic dielectric may enable non-destructive readout in ferroelastic data storage. Herein, we propose 14 kinds of stable 2D semiconductors: AB monolayers (A = Sc, Y, La; B = N, P, As, Sb, Bi) based on first-principles calculations. The band gaps of AB monolayers cover a wide range from 0.69 eV to 2.15 eV. Mechanical analysis reveals that these materials are flexible and 12 of 14 are predicted to possess an in-plane negative Poisson's ratio (NPR). Moreover, 10 of these 14 systems possess an out-of-plane NPR. More encouragingly, all AB monolayers are identified as 2D ferroelastic materials with reversible strains of around 5.94% to 20.30%. The ferroelastic switching barriers, mechanical properties and electronic structures of these materials are discussed in detail. Such outstanding properties make the AB monolayers very promising as switchable anisotropic 2D materials for nanoelectronics and micromechanical applications.

Pt5Se4 Monolayer: A Highly Efficient Electrocatalyst toward Hydrogen and Oxygen Electrode Reactions.

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) Pt5Se4 monolayer toward hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR). Our density functional theory calculations show that the Pt5Se4 monolayer can serve as a low-Pt-loading trifunctional electrocatalyst with good kinetic and thermal stabilities. Specifically, the HER performance of the Pt5Se4 basal plane is predicted to be superior to that of 2D layered Pd or Pt dichalcogenides. Even considering the solvent effect, the catalytic OER performance of the Pt5Se4 monolayer is predicted to be comparable to the prevalent OER catalyst-IrO2, while the catalytic ORR performance of the Pt5Se4 monolayer is even higher than the predominating Pt(111) surface. Overall, the Pt5Se4 monolayer can be a promising trifunctional catalyst that exhibits high activities toward all hydrogen and oxygen electrode reactions.

Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network 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.

Gallium Thiophosphate: An Emerging Bidirectional Auxetic Two-Dimensional Crystal with Wide Direct Band Gap.

Two-dimensional (2D) materials with negative Poisson's ratio (NPR) attract considerable attention because of their exotic mechanical properties. We propose a new 2D material, monolayer GaPS4, which shows NPR for both in-plane (-0.033) and out-of-plane (-0.62) directions. Such coexistence of NPR in two distinct directions could be explained by its corner- and edge-shared tetrahedra pucker structure. GaPS4 has an ultralow cleavage energy of 0.23 J m-2 according to our calculation, such that exfoliation of the bulk material is feasible for the preparation of mono- and few-layer GaPS4. Direct wide band gap of 3.55 eV and moderate electron mobility have been revealed in monolayer GaPS4, while the direct gap feature is robust within a strain range of -6% to 6%. These findings render 2D GaPS4 a promising candidate for applications in nanoelectronics and low-dimensional electromechanical devices.

KTlO: a metal shrouded 2D semiconductor with high carrier mobility and tunable magnetism.

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.

Lead-Free Halide Rb2 CuBr3 as Sensitive X-Ray Scintillator.

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 Rb2 CuBr3 , as a scintillator with exceptionally high light yield. Rb2 CuBr3 exhibits a 1D crystal structure and enjoys strong carrier confinement and near-unity photoluminescence quantum yield (98.6%) in violet emission. The high photoluminescence quantum yield combined with negligible self-absorption from self-trapped exciton emission and strong X-ray absorption capability enables a record high light yield of ≈91056 photons per MeV among perovskite and relative scintillators. Overall, Rb2 CuBr3 provides nontoxicity, high radioluminescence intensity, and good stability, thus laying good foundations for potential application in low-dose radiography.

Hot-Pressed CsPbBr3 Quasi-Monocrystalline Film for Sensitive Direct X-ray Detection.

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 CsPbBr3 possesses relatively larger density and a higher atomic number in contrast to its hybrid counterpart. Therefore, it is expected to provide high detection sensitivity for X-rays; however, it has rarely been studied as a direct X-ray detector. Here, a hot-pressing method is employed to fabricate thick quasi-monocrystalline CsPbBr3 films, and a record sensitivity of 55 684 µC Gyair -1 cm-2 is achieved, surpassing all other X-ray detectors (direct and indirect). The hot-pressing method is simple and produces thick quasi-monocrystalline CsPbBr3 films with uniform orientations. The high crystalline quality of the CsPbBr3 films and the formation of self-formed shallow bromide vacancy defects during the high-temperature process result in a large µτ product and, therefore, a high photoconductivity gain factor and high detection sensitivity. The detectors also exhibit relatively fast response speed, negligible baseline drift, and good stability, making a CsPbBr3 X-ray detector extremely competitive for high-contrast X-ray detections.

Heteroepitaxial passivation of Cs2AgBiBr6 wafers with suppressed ionic migration for X-ray imaging.

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.

An electro-photo-sensitive synaptic transistor for edge neuromorphic visual systems.

The practical application of optoelectronic artificial synapses in neuromorphic visual systems is still hindered by their limited functionality, reliability and the challenge of mass production. Here, an electro-photo-sensitive synapse based on a highly reliable amorphous InGaZnO thin-film transistor is demonstrated. Not only does the synapse respond to electrical voltage spikes due to charge trapping/detrapping, but also the weight is modified directly by persistent photocurrent effects under UV-light stimulation. Representative forms of synaptic plasticity, including inhibitory and excitatory postsynaptic currents, frequency-dependent characteristics, short-term to long-term plasticity transitions, and summation effects, are successfully demonstrated. In particular, optoelectronic synergetic modulation leads to reconfigurable excitatory and inhibitory synaptic behaviors, which provides a promising way to achieve the homeostatic regulation of synaptic weights. Moreover, the analogue channel conductance with 100 states is used as the weight update rule to perform MNIST handwritten digit recognition, using system-level LeNet-5 convolutional neural network simulations. The network shows a high recognition accuracy of 95.99% and good tolerance to noisy input patterns. This study highlights the commercial potential of mature optoelectronic InGaZnO transistor technology in edge neuromorphic systems.

Observation of carrier localization in cubic crystalline Ge2Sb2Te5 by field effect measurement.

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.

Neuronal dynamics in HfOx/AlOy-based homeothermic synaptic memristors with low-power and homogeneous resistive switching.

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.

Short channel effects on electrokinetic energy conversion in solid-state nanopores.

The ion selectivity of nanopores due to the wall surface charges is capable of inducing strong coupling between fluidic and ionic motion within the system. This interaction opens up the prospect of operating nanopores as nanoscale devices for electrokinetic energy conversion. However, the very short channel lengths make the ionic movement and fluidics inside the pore to be substantially affected by the ion depletion/accumulation around the pore ends. Based on three-dimensional electrokinetic modeling and simulation, we present a systematic theoretical study of nanopore electrical resistance, fluidic impedance, and streaming conductance. Our results show that by utilizing the short channel effect and preparing slippery nanopores the energy conversion efficiency can be dramatically increased to about 9% under large salt concentrations.

Nonvolatile reconfigurable sequential logic in a HfO2 resistive random access memory array.

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.