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The electronic structure and conduction mechanism of chalcogenide-based Ovonic threshold switches (OTS) used as selectors in cross-point memory arrays is derived from density functional calculations and quasi-Fermi level models. The switching mechanism in OTS is primarily electronic. This uses a specific electronic structure, with a wide tail of localized states below the conduction band edge. In amorphous GeSe 2−x the conduction band consists of Ge-Se σ*states with a low effective mass, and with a broad tail of localized Ge-Ge σ* states below this band edge. This leads to the OTS behavior. At high fields the electron quasi-E F moves up through these tail states, lowering the conductivity activation energy, and giving the non-linear switching process. The 4:2 coordinated GeSe 2−x based alloys are the most favorable OTS material because they have the correct network connectivity to give a high electron mobility and lack of crystallization, a favorable band structure to produce the non-linear conduction, an optimum band gap, and with nitrogen or carbon alloying, a sufficiently low off-current.

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm −2 and a ~10 6 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field. Designing efficient selector devices for large-scale nonvolatile memory and neuromorphic array systems remains a challenge. Here, the authors propose a two-terminal ovonic threshold switching selector device with a large drive current density and a high nonlinearity, capable emulating stochastic integrate-and-fire neuron behavior.

The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices. Resistive switching devices have great promise for a wide variety of technological applications. Here, Yang et al demonstrate that electrochemically induced tellurium filament can give rise to resistive switching, and show that devices based on this can provide a number of advantages compared to metallic filament-based devices.

Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing. Designing efficient AI hardware capable of creating artificial general intelligence remains a challenge. Here, the authors present an approach for the on-demand generation of complex networks within a single memristor by harnessing device dynamics with intrinsic cycle-to-cycle variability and demonstrate the effectiveness of memristive complex network-based reservoirs.

Two-dimensional (2D) heterostructures based on the combination of transition metal dichalcogenides (TMDs) and transition metal oxides (TMOs) have aroused growing attention due to their integrated merits of both components and multiple functionalities. However, nondestructive approaches of constructing TMD-TMO heterostructures are still very limited. Here, we develop a novel type of lateral TMD-TMO heterostructure (NbS 2 -Nb 2 O 5 -NbS 2 ) using a simple lithography-free, direct laser-patterning technique. The perfect contact of an ultrathin TMO channel (Nb 2 O 5 ) with two metallic TMDs (NbS 2 ) electrodes guarantee strong electrical signals in a two-terminal sensor. Distinct from sensing mechanisms in separate TMOs or TMDs, this sensor works based on the modulation of surface conduction of the ultrathin TMO (Nb 2 O 5 ) channel through an adsorbed layer of water molecules. The sensor thus exhibits high selectivity and ultrahigh sensitivity for room-temperature detection of NH 3 (Δ R / R = 80% at 50 ppm), superior to the reported NH 3 sensors based on 2D materials, and a positive temperature coefficient of resistance as high as 15%–20%/°C. Bending-invariant performance and high reliability are also demonstrated in flexible versions of sensors. Our work provides a new strategy of lithography-free processing of novel TMD-TMO heterostructures towards high-performance sensors, showing great potential in the applications of future portable and wearable electronics.

Bi 2 O 2 Se is an emerging semiconducting, air-stable layered material (Nat. Nanotechnol. 2017, 1 2 , 530 ; Nano Lett. 2017, 17, 3021 ), potentially exceeding MoS 2 and phosphorene in electron mobility and rivalling typical Van der Waals stacked layered materials in the next-generation high-speed and low-power electronics. Holding the promise of functional versatility, it is arousing rapidly growing interest from various disciplines, including optoelectronics, thermoelectronics and piezoelectronics. In this work, we comprehensively study the electrical properties of the native point defects in Bi 2 O 2 Se, as an essential step toward understanding the fundamentals of this material. The defect landscapes dependent on both Fermi energy and the chemical potentials of atomic constituents are investigated. Along with the bulk defect analysis, a complementary inspection of the surface properties, within the simple context of charge neutrality level model, elucidates the observed n-type characteristics of Bi 2 O 2 Se based FETs. This work provides important guide to engineer the defects of Bi 2 O 2 Se for desired properties, which is key to the successful application of this emerging layered material 27 .

Two-dimensional (2D) transition-metal dichalcogenide (TMD) materials have aroused noticeable interest due to their distinguished electronic and optical properties. However, little is known about their complex exciton properties together with the exciton dynamics process which have been expected to influence the performance of optoelectronic devices. The process of fluorescence can well reveal the process of exciton transition after excitation. In this work, the room-temperature layer-dependent exciton dynamics properties in layered WSe 2 are investigated by the fluorescence lifetime imaging microscopy (FLIM) for the first time. This paper focuses on two mainly kinds of excitons including the direct transition neutral excitons and trions. Compared with the lifetime of neutral excitons (< 0.3 ns within four-layer), trions possess a longer lifetime (~ 6.6 ns within four-layer) which increases with the number of layers. We attribute the longer-lived lifetime to the increasing number of trions as well as the varieties of trion configurations in thicker WSe 2 . Besides, the whole average lifetime increases over 10% when WSe 2 flakes added up from monolayer to four-layer. This paper provides a novel tuneable layer-dependent method to control the exciton dynamics process and finds a relatively longer transition lifetime of trions at room temperature, enabling to investigate in the charge transport in TMD-based optoelectronics devices in the future.

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO 3 . The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

Electrochemical metallization (ECM) memories are among the various emerging non-volatile memory technologies, contending to replace DRAM and Flash and enabling novel neuromorphic computing applications. Typically, the operation of ECM cell is based on the electrochemical redox reactions of the cation supplying active electrode (e.g., Ag, Cu). Although extensively investigated, the possibility of utilizing new materials for the active electrode remains largely undiscussed. In this paper, an ECM cell with a Te active electrode is fabricated. It is found that the SET operation of the device occurs under negative voltage on the active electrode, which is opposite to that of the device with Ag electrode, indicating that the Te electrode supplies Te 2− anions by electrochemical reduction. The influence of the electrolyte material on the switching properties is also found to be more significant for devices with Te electrodes. For Pt/GeS/Te and Pt/Ge 2 Sb 2 Te 5 /Te cells, repeatable unipolar and bipolar resistive switching are observed, respectively, which can be attributed to the rupture of the filament by Joule heating for the former and by ECM for the latter in the RESET process. The semiconducting properties of Te, the reversed operating polarity and the electrolyte dependent switching characteristics open up unprecedented prospects for ECM cells.

The understanding of neural networks as neuron-synapse binaries has been the foundation of neuroscience, and therefore, the emerging neuromorphic computing technology that takes inspiration from the brain. This dogma, however, has been increasingly challenged by recent neuroscience research in which the downplayed dendrites were found to be active, dynamically unique and computationally powerful. To date, research on artificial dendrites is scarce and the few existing devices are still far from versatile or (and) bio-realistic. A breakthrough is hampered by the limited available physical mechanisms in mainstream device architectures. Here, we experimentally demonstrate a bio-realistic and versatile artificial dendrite made of WSe 2 /MoS 2 heterojunction-channel anti-ambipolar (AAB) transistor, which can closely mimic the experimentally recorded non-monotonic dendritic Ca 2+ action potential that underpins various sophisticated computations. Spiking neural network simulations reveal that the incorporation of this nonconventional but bio-realistic dendritic activation enhances the robustness and representational capability of the network in non-stationary environments. By further exploiting the memristive effect, dendritic AAB transistor can naturally mimic Ca 2+ -mediated regulation of synaptic plasticity (meta-plasticity) at dendritic spine, an important homeostatic phenomenon due to dendritic modulation. The invention of dendritic AAB transistor enriches the family of neuromorphic transistors and represents a major advance in diversifying the functionality of transistors.