Correlational Effects of the Molecular-Tilt Configuration and the Intermolecular van der Waals Interaction on the Charge Transport in the Molecular Junction.

Molecular conformation, intermolecular interaction, and electrode-molecule contacts greatly affect charge transport in molecular junctions and interfacial properties of organic devices by controlling the molecular orbital alignment. Here, we statistically investigated the charge transport in molecular junctions containing self-assembled oligophenylene molecules sandwiched between an Au probe tip and graphene according to various tip-loading forces ( FL) that can control the molecular-tilt configuration and the van der Waals (vdW) interactions. In particular, the molecular junctions exhibited two distinct transport regimes according to the FL dependence (i.e., FL-dependent and FL-independent tunneling regimes). In addition, the charge-injection tunneling barriers at the junction interfaces are differently changed when the FL ≤ 20 nN. These features are associated to the correlation effects between the asymmetry-coupling factor (η), the molecular-tilt angle (θ), and the repulsive intermolecular vdW force ( FvdW) on the molecular-tunneling barriers. A more-comprehensive understanding of these charge transport properties was thoroughly developed based on the density functional theory calculations in consideration of the molecular-tilt configuration and the repulsive vdW force between molecules.

2D Single-Crystalline Copper Nanoplates as a Conductive Filler for Electronic Ink Applications.

Large-scale 2D single-crystalline copper nanoplates (Cu NPLs) are synthesized by a simple hydrothermal method. The combination of a mild reductant, stabilizer, and shape modifier allows the dimensional control of the Cu nanocrystals from 1D nanowires (NWs) to 2D nanoplates. High-resolution transmission electron microscopy (HR-TEM) reveals that the prepared Cu NPLs have a single-crystalline structure. From the X-ray photoelectron spectroscopy (XPS) analysis, it is found that iodine plays an important role in the modification of the copper nanocrystals through the formation of an adlayer on the basal plane of the nanoplates. Cu NPLs with an average edge length of 10 µm are successfully synthesized, and these Cu NPLs are the largest copper 2D crystals synthesized by a solution-based process so far. The application of the metallic 2D crystals as a semitransparent electrode proves their feasibility as a conductive filler, exhibiting very low sheet resistance (0.4 Ω â–«-1 ) compared to Cu NWs and a transmittance near 75%. The efficient charge transport is due to the increased contact area between each Cu NPL, i.e., so-called plane contact (2D electrical contact). In addition, this type of contact enhances the current-carrying capability of the Cu NPL electrodes, implying that the large-size Cu NPLs are promising conductive fillers for printable electrode applications.

Controllable Switching Filaments Prepared via Tunable and Well-Defined Single Truncated Conical Nanopore Structures for Fast and Scalable SiOx Memory.

The controllability of switching conductive filaments is one of the central issues in the development of reliable metal-oxide resistive memory because the random dynamic nature and formation of the filaments pose an obstacle to desirable switching performance. Here, we introduce a simple and novel approach to control and form a single silicon nanocrystal (Si-NC) filament for use in SiOx memory devices. The filament is formed with a confined vertical nanoscale gap by using a well-defined single vertical truncated conical nanopore (StcNP) structure. The physical dimensions of the Si-NC filaments such as number, size, and length, which have a significant influence on the switching properties, can be simply engineered by the breakdown of an Au wire through different StcNP structures. In particular, we demonstrate that the designed SiOx memory junction with a StcNP of pore depth of ∼75 nm and a bottom diameter of ∼10 nm exhibited a switching speed of up to 6 ns for both set and reset process, significantly faster than reported SiOx memory devices. The device also exhibited a high ON-OFF ratio, multistate storage ability, acceptable endurance, and retention stability. The influence of the physical dimensions of the StcNP on the switching features is discussed based on the simulated temperature profiles of the Au wire and the nanogap size generated inside the StcNP structure during electromigration.

Growth and Transfer of Seamless 3D Graphene-Nanotube Hybrids.

Seamlessly connected graphene and carbon nanotube hybrids (GCNTs) have great potential as carbon platform structures in electronics due to their high conductivity and high surface area. Here, we introduce a facile method for making patterned GCNTs and their intact transfer onto other substrates. The mechanism for selective growth of vertically aligned CNTs (VA-CNTs) on the patterned graphene is discussed. The complete transfer of the GCNT pattern onto other substrates is possible because of the mechanical strength of the GCNT hybrids. Electrical conductivity measurements of the transferred GCNT structures show Ohmic contact through the VA-CNTs to graphene--evidence of its integrity after the transfer process.

Gate-dependent asymmetric transport characteristics in pentacene barristors with graphene electrodes.

We investigated the electrical characteristics and the charge transport mechanism of pentacene vertical hetero-structures with graphene electrodes. The devices are composed of vertical stacks of silicon, silicon dioxide, graphene, pentacene, and gold. These vertical heterojunctions exhibited distinct transport characteristics depending on the applied bias direction, which originates from different electrode contacts (graphene and gold contacts) to the pentacene layer. These asymmetric contacts cause a current rectification and current modulation induced by the gate field-dependent bias direction. We observed a change in the charge injection barrier during variable-temperature current-voltage characterization, and we also observed that two distinct charge transport channels (thermionic emission and Poole-Frenkel effect) worked in the junctions, which was dependent on the bias magnitude.

Graphene quantum dots as a highly efficient solution-processed charge trapping medium for organic nano-floating gate memory.

A highly efficient solution-processible charge trapping medium is a prerequisite to developing high-performance organic nano-floating gate memory (NFGM) devices. Although several candidates for the charge trapping layer have been proposed for organic memory, a method for significantly increasing the density of stored charges in nanoscale layers remains a considerable challenge. Here, solution-processible graphene quantum dots (GQDs) were prepared by a modified thermal plasma jet method; the GQDs were mostly composed of carbon without any serious oxidation, which was confirmed by x-ray photoelectron spectroscopy. These GQDs have multiple energy levels because of their size distribution, and they can be effectively utilized as charge trapping media for organic NFGM applications. The NFGM device exhibited excellent reversible switching characteristics, with an on/off current ratio greater than 10(6), a stable retention time of 10(4) s and reliable cycling endurance over 100 cycles. In particular, we estimated that the GQDs layer trapped ∼7.2 × 10(12) cm(-2) charges per unit area, which is a much higher density than those of other solution-processible nanomaterials, suggesting that the GQDs layer holds promise as a highly efficient nanoscale charge trapping material.

Three-Dimensional Networked Nanoporous Ta2O(5-x) Memory System for Ultrahigh Density Storage.

Oxide-based resistive memory systems have high near-term promise for use in nonvolatile memory. Here we introduce a memory system employing a three-dimensional (3D) networked nanoporous (NP) Ta2O5-x structure and graphene for ultrahigh density storage. The devices exhibit a self-embedded highly nonlinear I-V switching behavior with an extremely low leakage current (on the order of pA) and good endurance. Calculations indicated that this memory architecture could be scaled up to a ∼162 Gbit crossbar array without the need for selectors or diodes normally used in crossbar arrays. In addition, we demonstrate that the voltage point for a minimum current is systematically controlled by the applied set voltage, thereby offering a broad range of switching characteristics. The potential switching mechanism is suggested based upon the transformation from Schottky to Ohmic-like contacts, and vice versa, depending on the movement of oxygen vacancies at the interfaces induced by the voltage polarity, and the formation of oxygen ions in the pores by the electric field.

Nanoporous silicon oxide memory.

Oxide-based two-terminal resistive random access memory (RRAM) is considered one of the most promising candidates for next-generation nonvolatile memory. We introduce here a new RRAM memory structure employing a nanoporous (NP) silicon oxide (SiOx) material which enables unipolar switching through its internal vertical nanogap. Through the control of the stochastic filament formation at low voltage, the NP SiOx memory exhibited an extremely low electroforming voltage (∼ 1.6 V) and outstanding performance metrics. These include multibit storage ability (up to 9-bits), a high ON-OFF ratio (up to 10(7) A), a long high-temperature lifetime (≥ 10(4) s at 100 °C), excellent cycling endurance (≥ 10(5)), sub-50 ns switching speeds, and low power consumption (∼ 6 × 10(-5) W/bit). Also provided is the room temperature processability for versatile fabrication without any compliance current being needed during electroforming or switching operations. Taken together, these metrics in NP SiOx RRAM provide a route toward easily accessed nonvolatile memory applications.

Flexible three-dimensional nanoporous metal-based energy devices.

A flexible three-dimensional (3-D) nanoporous NiF2-dominant layer on poly(ethylene terephthalate) has been developed. The nanoporous layer itself can be freestanding without adding any supporting carbon materials or conducting polymers. By assembling the nanoporous layer into two-electrode symmetric devices, the inorganic material delivers battery-like thin-film supercapacitive performance with a maximum capacitance of 66 mF cm(-2) (733 F cm(-3) or 358 F g(-1)), energy density of 384 Wh kg(-1), and power density of 112 kW kg(-1). Flexibility and cyclability tests show that the nanoporous layer maintains its high performance under long-term cycling and different bending conditions. The fabrication of the 3-D nanoporous NiF2 flexible electrode could be easily scaled.

Large hexagonal bi- and trilayer graphene single crystals with varied interlayer rotations.

Bi- and trilayer graphene have attracted intensive interest due to their rich electronic and optical properties, which are dependent on interlayer rotations. However, the synthesis of high-quality large-size bi- and trilayer graphene single crystals still remains a challenge. Here, the synthesis of 100 µm pyramid-like hexagonal bi- and trilayer graphene single-crystal domains on Cu foils using chemical vapor deposition is reported. The as-produced graphene domains show almost exclusively either 0° or 30° interlayer rotations. Raman spectroscopy, transmission electron microscopy, and Fourier-transformed infrared spectroscopy were used to demonstrate that bilayer graphene domains with 0° interlayer stacking angles were Bernal stacked. Based on first-principle calculations, it is proposed that rotations originate from the graphene nucleation at the Cu step, which explains the origin of the interlayer rotations and agrees well with the experimental observations.

3D-integrated multilayered physical reservoir array for learning and forecasting time-series information.

A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

Boltzmann Switching MoS2 Metal-Semiconductor Field-Effect Transistors Enabled by Monolithic-Oxide-Gapped Metal Gates at the Schottky-Mott Limit.

A gate stack that facilitates a high-quality interface and tight electrostatic control is crucial for realizing high-performance and low-power field-effect transistors (FETs). However, when constructing conventional metal-oxide-semiconductor structures with two-dimensional (2D) transition metal dichalcogenide channels, achieving these requirements becomes challenging due to inherent difficulties in obtaining high-quality gate dielectrics through native oxidation or film deposition. Here, a gate-dielectric-less device architecture of van der Waals Schottky gated metal-semiconductor FETs (vdW-SG MESFETs) using a molybdenum disulfide (MoS2) channel and surface-oxidized metal gates such as nickel and copper is reported. Benefiting from the strong SG coupling, these MESFETs operate at remarkably low gate voltages, <0.5 V. Notably, they also exhibit Boltzmann-limited switching behavior featured by a subthreshold swing of ≈60 mV dec-1 and negligible hysteresis. These ideal FET characteristics are attributed to the formation of a Fermi-level (EF) pinning-free gate stack at the Schottky-Mott limit. Furthermore, authors experimentally and theoretically confirm that EF depinning can be achieved by suppressing both metal-induced and disorder-induced gap states at the interface between the monolithic-oxide-gapped metal gate and the MoS2 channel. This work paves a new route for designing high-performance and energy-efficient 2D electronics.

Hexagonal graphene onion rings.

Precise spatial control of materials is the key capability of engineering their optical, electronic, and mechanical properties. However, growth of graphene on Cu was revealed to be seed-induced two-dimensional (2D) growth, limiting the synthesis of complex graphene spatial structures. In this research, we report the growth of onion ring like three-dimensional (3D) graphene structures, which are comprised of concentric one-dimensional hexagonal graphene ribbon rings grown under 2D single-crystal monolayer graphene domains. The ring formation arises from the hydrogenation-induced edge nucleation and 3D growth of a new graphene layer on the edge and under the previous one, as supported by first principles calculations. This work reveals a new graphene-nucleation mechanism and could also offer impetus for the design of new 3D spatial structures of graphene or other 2D layered materials. Additionally, in this research, two special features of this new 3D graphene structure were demonstrated, including nanoribbon fabrication and potential use in lithium storage upon scaling.

Filament-free memristors for computing.

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics.

Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.

Advances of Various Heterogeneous Structure Types in Molecular Junction Systems and Their Charge Transport Properties.

Molecular electronics that can produce functional electronic circuits using a single molecule or molecular ensemble remains an attractive research field because it not only represents an essential step toward realizing ultimate electronic device scaling but may also expand our understanding of the intrinsic quantum transports at the molecular level. Recently, in order to overcome the difficulties inherent in the conventional approach to studying molecular electronics and developing functional device applications, this field has attempted to diversify the electrical characteristics and device architectures using various types of heterogeneous structures in molecular junctions. This review summarizes recent efforts devoted to functional devices with molecular heterostructures. Diverse molecules and materials can be combined and incorporated in such two- and three-terminal heterojunction structures, to achieve desirable electronic functionalities. The heterojunction structures, charge transport mechanisms, and possible strategies for implementing electronic functions using various hetero unit materials are presented sequentially. In addition, the applicability and merits of molecular heterojunction structures, as well as the anticipated challenges associated with their implementation in device applications are discussed and summarized. This review will contribute to a deeper understanding of charge transport through molecular heterojunction, and it may pave the way toward desirable electronic functionalities in molecular electronics applications.

Molecular Van Der Waals Heterojunction Photodiodes Enabling Dipole-Induced Polarity Switching.

Solid-state devices capable of controlling light-responsive charge transport at the molecular scale are essential for developing molecular optoelectronic technology. Here, a solid-state molecular photodiode device constructed by forming van der Waals (vdW) heterojunctions between standard molecular self-assembled monolayers and two-dimensional semiconductors such as WSe2 is reported. In particular, two non-functionalized molecular species used herein (i.e., tridecafluoro-1-octanethiol and 1-octanethiol) enable bidirectional modulation of the interface band alignment with WSe2 , depending on their dipole orientations. This dipole-induced band modulation at the vdW heterointerface leads to the opposite change of both photoswitching polarity and rectifying characteristics. Furthermore, compared with other molecular or 2D photodiodes at a similar scale, these heterojunction devices exhibit significantly enhanced photo-responsive performances in terms of photocurrent magnitude, open-circuit potential, and switching speed. This study proposes a novel concept of the solid-state molecular optoelectronic device with controlled functions and enhanced performances.

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing.

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiOx ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 × 25 TiOx memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiOx memristor performance such as threshold uniformity (≈2.7%), device yield (> 99%), repetitive stability (≈3000 spikes), low asymmetry value of ≈1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at ≈95.2% of classification accuracy.

Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning.

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct ) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop-connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.

Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference.

Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation.