First-principles prediction of ferroelectric Janus Si2XY (X/Y = S/Se/Te, X ≠ Y) monolayers with negative Poisson's ratios.

Nowadays, two-dimensional (2D) materials with Janus structures evoke much attention due to their unique mechanical and electronic properties. In this work, Janus Pma2-Si2XY (X/Y = S/Se/Te, X ≠ Y) ferroelectric monolayers are firstly proposed and systematically investigated by first-principles calculations. These monolayers exhibit remarkable mechanical properties, including small Young's modulus values, negative Poisson's ratios (NPRs) and large critical strains, reflecting their exceptional flexibility and stretchability. More strikingly, the novel structures of Si2STe and Si2SeTe also endow them with in-plane spontaneous polarization (Ps) and low energy barrier for phase transition, with Ps and energy barrier values being 1.632 × 10-10 C m-1 and 159 meV for Si2STe and 1.149 × 10-10 C m-1 and 196.6 meV for Si2SeTe. The ab initio molecular dynamics (AIMD) simulations reveal high Curie temperatures (Tc) for Si2STe and Si2SeTe, ranging between 1300 K and 1400 K. Additionally, Si2XY monolayers exhibit high anisotropic carrier mobility (∼103 cm2 V-1 s-1) and an extraordinary light absorption coefficient (∼105 cm-1). Our research not only broadens the family of 2D Janus ferroelectric materials, but also demonstrates their potential applications in nanomechanical, nanoelectronic and optoelectronic devices.

Janus monolayer PXC (X = As/Sb) for photocatalytic water splitting with a negative Poisson's ratio.

Two-dimensional (2D) Janus materials have attracted considerable attention in photocatalysis owing to their robust redox capability and efficient segregation. In this study, we propose a novel Janus monolayer structure, denoted as PXC (X = As/Sb), exhibiting favorable stability in terms of dynamics, thermal properties, and mechanical characteristics. The PXC monolayers demonstrate a relatively smaller Young's modulus (132.5/119.5 N m-1 for PAsC/PSbC) and large negative Poisson's ratios (-0.15/-0.101 for PAsC/PSbC). Moreover, the HSE06 + SOC functional results show that PAsC/PSbC are indirect semiconductors with a 2.33/1.43 eV band gap, exhibiting a suitable band alignment for photocatalytic water splitting. The calculated high carrier mobility (104 cm2 V-1 s-1), along with a significant discrepancy, determined by the deformation potential theory and the built-up field induced by the large intrinsic dipole, effectively suppresses the recombination of photogenerated carriers. Furthermore, PXC monolayers possess a strong absorption capacity in the visible and ultraviolet light region (105 cm-1). Therefore, our results indicate that PXC monolayers hold great potential for application in the field of photocatalytic water splitting.

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor.

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2(5 nm)/Ta2O5(5 nm)/Pt with the Ta2O5TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

Electrostatic gating dependent multiple band alignments in ferroelectric VS2/Ga2O3 van der Waals heterostructures.

Two-dimensional (2D) van der Waals (vdW) heterostructures with spontaneous intrinsic ferroelectrics play an essential role in ferroelectric memories. Also, the reversal of polarized directions induces band alignment transitions among different types to provide a new path for multifunctional devices. In this work, the structural and electronic properties of 2D VS2/Ga2O3 vdW heterostructures under different polarizations were investigated using first-principles calculations with the vdW correction of the DFT-D2 method. The results reveal that the polarized direction of a 2D Ga2O3 monolayer can cause a distinct band structure reversion from a metal to a semiconductor due to the shift of band alignment induced by the interlayer charge transfer. Moreover, the VS2/P↑ Ga2O3 heterostructures retain type-I and type-II band alignments in the majority and minority channel, respectively, under an external electric field. Interestingly, applying the external electric field for VS2/P↓ Ga2O3 heterostructures can lead to a transition from type-II to type-I in the majority channel, and from type-II to type-III in the minority channel. Our work provides a feasible way to realize 2D VS2/Ga2O3 vdW heterostructures for potential applications in ferroelectric memories and electrostatic gating dependent multiple band alignment devices.

Robust ferroelectricity in low-dimensional δ-SiX (X = S/Se): a first-principles study.

Low-dimensional ferroelectric materials hold great promise for application in nonvolatile memory devices. In this work, ferroelectricity in two-dimensional monolayers and one-dimensional nanowires based on δ-SiX (X = S and Se) materials with spontaneous polarization and ferroelectric switching energy barriers has been predicted using the first-principles method. The results show that the intrinsic ferroelectric values due to spontaneous polarization of 2D-SiS, 2D-SiSe, 1D-SiS and 1D-SiSe are 3.22 × 10-10 C m-1, 3.00 × 10-10 C m-1, 7.58 × 10-10 C m-1 and 6.81 × 10-10 C m-1, respectively. The Monte Carlo simulations and ab initio molecular dynamics (AIMD) simulations both indicate that 2D-SiX and 1D-SiX exhibit room-temperature ferroelectricity. Moreover, the polarization and ferroelectric switching energy barrier can be tuned by applying a strain. Notably, spontaneous spin polarization can be achieved by hole doping in one-dimensional nanowires. Our findings not only broaden the research field of low-dimensional ferroelectric materials, but also provide a promising platform for the application of novel nano-ferroelectric devices.

A bioinspired configurable cochlea based on memristors.

Cochleas are the basis for biology to process and recognize speech information, emulating which with electronic devices helps us construct high-efficient intelligent voice systems. Memristor provides novel physics for performing neuromorphic engineering beyond complementary metal-oxide-semiconductor technology. This work presents an artificial cochlea based on the shallen-key filter model configured with memristors, in which one filter emulates one channel. We first fabricate a memristor with the TiN/HfOx/TaOx/TiN structure to implement such a cochlea and demonstrate the non-volatile multilevel states through electrical operations. Then, we build the shallen-key filter circuit and experimentally demonstrate the frequency-selection function of cochlea's five channels, whose central frequency is determined by the memristor's resistance. To further demonstrate the feasibility of the cochlea for system applications, we use it to extract the speech signal features and then combine it with a convolutional neural network to recognize the Free Spoken Digit Dataset. The recognition accuracy reaches 92% with 64 channels, compatible with the traditional 64 Fourier transform transformation points of mel-frequency cepstral coefficients method with 95% recognition accuracy. This work provides a novel strategy for building cochleas, which has a great potential to conduct configurable, high-parallel, and high-efficient auditory systems for neuromorphic robots.

Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization.

A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaOx/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.

A Heterogeneously Integrated Spiking Neuron Array for Multimode-Fused Perception and Object Classification.

Multimode-fused sensing in the somatosensory system helps people obtain comprehensive object properties and make accurate judgments. However, building such multisensory systems with conventional metal-oxide-semiconductor technology presents serious device integration and circuit complexity challenges. Here, a multimode-fused spiking neuron (MFSN) with a compact structure to achieve human-like multisensory perception is reported. The MFSN heterogeneously integrates a pressure sensor to process pressure and a NbOx -based memristor to sense temperature. Using this MFSN, multisensory analog information can be fused into one spike train, showing excellent data compression and conversion capabilities. Moreover, both pressure and temperature information are distinguished from fused spikes by decoupling the output frequencies and amplitudes, supporting multimodal tactile perception. Then, a 3 × 3 MFSN array is fabricated, and the fused frequency patterns are fed into a spiking neural network for enhanced tactile pattern recognition. Finally, a larger MFSN array is simulated for classifying objects with different shapes, temperatures, and weights, validating the feasibility of the MFSNs for practical applications. The proof-of-concept MFSNs enable the building of multimodal sensory systems and contribute to the development of highly intelligent robotics.

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging.

The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO2 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 HfO2-based memristors. Here, the conductive filament system in the amorphous HfO2-based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf6O and its crystalline surroundings (monoclinic or tetragonal HfOx). The phase of the HfOx 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 HfO2 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 HfO2-based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications.

Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors.

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

A dual-functional Ta/TaO x /Ru device with both nonlinear selector and resistive switching behaviors.

In this work, we demonstrate that a Ta/TaO x /Ru device can act as both a highly uniform and nonlinear selection device and a stable resistive switching device, respectively, by controlling the voltage applied to the Ta electrode. As a selection device, it shows high selectivity (103), high current density (25 kA cm-2), very low variation, and good endurance. The non-linear performance of the device may be attributed to a trapezoidal band structure modulated by the concentration gradient of oxygen vacancies. Furthermore, with a large voltage bias on the Ta electrode, a repeatable and stable resistive switching behavior was observed, which could be attributed to the formation of conductive filaments probably composed of Ta metal and oxygen vacancies. This research deepens the understanding of the mechanism of Ta/TaO x devices, and provides a potential solution for large-scale memristor arrays.

Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks.

Spiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems.

A Habituation Sensory Nervous System with Memristors.

The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a Lix SiOy -based memristor (TiN/Lix SiOy /Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate-and-fire neuron based on a Ag/SiO2 :Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing.

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb2 O5 and Lix SiO2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (106 ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.

An artificial spiking afferent nerve based on Mott memristors for neurorobotics.

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

Modulating the filament rupture degree of threshold switching device for self-selective and low-current nonvolatile memory application.

Resistive switching devices have tremendous potential for memory, logic, and neuromorphic computing applications. Cation-based resistive switching devices intrinsically show nonvolatile memory characteristics under high compliance current (I CC), while show volatile threshold switching (TS) selector characteristics under low I CC. However, separate researches about cation-based memory or selector are hard to evade the typical current-retention dilemma, which results in the hardship to obtain low-current memory and high-current selector. Here, we propose a novel strategy to realize nonvolatile storage characteristics in a volatile TS device by modulating the rupture degree of conductive filament (CF). Enlarging the rupture degree of the CF with a certain RESET process, as confirmed by transmission electron microscope and energy dispersive spectrometry results, the threshold voltage of the Ag/HfO2/Pt TS devices can be enlarged from 0.9 to 2.8 V. Generation of the voltage difference enables the volatile TS devices the ability of self-selective nonvolatile storage. Increasing the RESET magnitude and shrinking the device size have been proved effective ways to increase the read window of the TS memory (TSM) devices. Evading the limit of the current-retention dilemma, ultra-low energy dissipation can be obtained by decreasing I CC to nA level. With self-selective, low-energy, and potential high-density integration characteristics, the proposed TSM device can act as a potential supplement of novel storage class memories.

Time-Tailoring van der Waals Heterostructures for Human Memory System Programming.

The human memory system plays an indispensable role in oblivion, learning, and memorization. Implementing a memory system within electronic devices contributes an important step toward constructing a neuromorphic system that emulates advanced mental functions of the human brain. Given the complex time-tailoring requirement, integrating a human memory system into one system is a great challenge. Here, one van der Waals heterostructure with flexible time-tailoring ability is demonstrated, which can meet the high requirement of human memory system programming. By stacking volatile and nonvolatile function layers, all basic memory types, including sensory memory, short-term and long-term memory are integrated into the device and the transition between these memory types are flexible. Moreover, decision-making action and in situ result storage are also demonstrated. It is anticipated that the demonstrated time-tailoring system will support the model of a human memory system.

Transformation of threshold volatile switching to quantum point contact originated nonvolatile switching in graphene interface controlled memory devices.

Resistive switching devices based on binary transition metal oxides have been widely investigated. However, these devices invariably manifest threshold switching characteristics when the active metal electrode is silver, the dielectric layer is hafnium oxide and platinum is used as the bottom electrode, and have a relatively low compliance current (<100 µA). Here we developed a way to transform an Ag-based hafnium oxide selector into quantum-contact originated memory with a low compliance current, in which a graphene interface barrier layer is inserted between the silver electrode and hafnium oxide layer. Devices with structure Ag/HfO x /Pt acts as a bipolar selector with a high selectivity of >108 and sub-threshold swing of ∼1 mV dec-1. After introducing a graphene interface barrier, high stress dependent (forming at +3 V) formation of localized conducting filaments embodies stable nonvolatile memory characteristics with low set/reset voltages (<±1.0 V), low reset power (6 µW) and multi-level potential. Grain boundaries of the graphene interface control the type of switching in the devices. A good barrier can switch the Ag-based volatile selector into Ag-based nonvolatile memory.

Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing.

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al2O3/TaOx/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (<1%) can be obtained with random initial states. However, the device shows non-linear conductance change characteristics, and a nearly linear conductance change behavior is obtained by optimizing the training scheme. Based on these results, the device is a promising emulator for biology synapses, which could be of great benefit to memristor-based neuromorphic computing.