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.

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.

Heterosynaptic MoS 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.

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 WSe is reported. In particular, two non-functionalized molecular species used herein (i.

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.

Integration of multiple electronic components on a microfibre towards an emerging electronic textile platform.

Electronic fibres have been considered one of the desired device platforms due to their dimensional compatibility with fabrics by weaving with yarns. However, a precise connecting process between each electronic fibre is essential to configure the desired electronic circuits or systems. Here, we present an integrated electronic fibre platform by fabricating electronic devices onto a one-dimensional microfibre substrate.

A Learning-Rate Modulable and Reliable TiO 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 (TiO ) 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 TiO 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.

Flexible Neural Network Realized by the Probabilistic SiO 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 SiO memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated.

Bird-Inspired Self-Navigating Artificial Synaptic Compass.

Extrasensory neuromorphic devices that can recognize, memorize, and learn stimuli imperceptible to human beings are of considerable interest in interactive intelligent electronics research. This study presents an artificially intelligent magnetoreceptive synapse inspired by the magnetocognitive ability used by birds for navigation and orientation. The proposed synaptic platform is based on arrays of ferroelectric field-effect transistors with air-suspended magneto-interactive top-gates.

Controllable SiO 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 SiO 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.

Tailoring the Interfacial Band Offset by the Molecular Dipole Orientation for a Molecular Heterojunction Selector.

Understanding and designing interfacial band alignment in a molecular heterojunction provides a foundation for realizing its desirable electronic functionality. In this study, a tailored molecular heterojunction selector is implemented by controlling its interfacial band offset between the molecular self-assembled monolayer with opposite dipole orientations and the 2D semiconductor (1 -MoS or 1 -WSe ). The molecular dipole moment direction determines the direction of the band bending of the 2D semiconductors, affecting the dominant transport pathways upon voltage application.

Run-off election-based decision method for the training and inference process in an artificial neural network.

Generally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector-matrix multiplication between the input bias signal and the analog synaptic weight quantity of each node in a matrix array. Although a sequence-based decision rule can efficiently extract a common feature in a large data set in a short time, it can occasionally fail to classify similar species because it does not intrinsically consider other quantitative configurations of the activation function that affect the synaptic weight update. In this work, we implemented a simple run-off election-based decision rule via an additional filter evaluation to mitigate the confusion from proximity of output activation functions, enabling the improved training and inference performance of artificial neural network system.

Artificially Intelligent Tactile Ferroelectric Skin.

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human-interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e-skin) based on arrays of ferroelectric-gate field-effect transistors with dome-shape tactile top-gates, which can simultaneously sense and learn from a variety of tactile information, is introduced.

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing.

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network.

One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications.

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-μm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress.

Tunable rectification in a molecular heterojunction with two-dimensional semiconductors.

Until now, a specifically designed functional molecular species has been recognized as an absolute necessity for realizing the diode's behavior in molecular electronic junctions. Here, we suggest a facile approach for the implementation of a tailored diode in a molecular junction based on non-functionalized alkyl and conjugated molecular monolayers. A two-dimensional semiconductor (MoS and WSe) is used as a rectifying designer at the alkyl or conjugated molecule/Au interface.

Two-in-One Device with Versatile Compatible Electrical Switching or Data Storage Functions Controlled by the Ferroelectricity of P(VDF-TrFE) via Photocrosslinking.

Organic electronics demand new platforms that can make integrated circuits and undergo mass production while maintaining diverse functions with high performance. The field-effect transistor has great potential to be a multifunctional device capable of sensing, data processing, data storage, and display. Currently, transistor-based devices cannot be considered intrinsic multifunctional devices because all installed functions are mutually coupled.

Ultrathin Conformable Organic Artificial Synapse for Wearable Intelligent Device Applications.

Ultrathin conformable artificial synapse platforms that can be used as on-body or wearable chips suggest a path to build next-generation, wearable, intelligent electronic systems that can mimic the synaptic operations of the human brain. So far, an artificial synapse architecture with ultimate mechanical flexibility in a freestanding form while maintaining its functionalities with high stability and accuracy on any conformable substrate has not been demonstrated yet. Here, we demonstrate the first ultrathin artificial synapse (∼500 nm total thickness) that features freestanding ferroelectric organic neuromorphic transistors (FONTs), which can stand alone without a substrate or an encapsulation layer.

Synaptic Barristor Based on Phase-Engineered 2D Heterostructures.

The development of energy-efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three-terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a 'synaptic barrister,' are reported. The device can implement essential synaptic characteristics, such as short-term plasticity, long-term plasticity, and paired-pulse facilitation.

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 ( F) 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 F dependence (i.

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.

Controllable Switching Filaments Prepared via Tunable and Well-Defined Single Truncated Conical Nanopore Structures for Fast and Scalable SiO 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 SiO memory devices. The filament is formed with a confined vertical nanoscale gap by using a well-defined single vertical truncated conical nanopore (StcNP) structure.

Low-Temperature-Grown KNbO Thin Films and Their Application to Piezoelectric Nanogenerators and Self-Powered ReRAM Device.

Amorphous KNbO (KN) film containing KN nanocrystals was grown on TiN/SiO/Si substrate at 350 °C. This KN film showed a dielectric constant (ε) and a piezoelectric strain constant (d) of 43 and 80 pm/V at 10 V, respectively, owing to the existence of KN nanocrystals. Piezoelectric nanogenerators (PNGs) were fabricated using KN films grown on the TiN/polyimide/poly(ethylene terephthalate) substrates.