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.

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.

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.

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.

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.

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. Electronic components such as transistors, inverters, ring oscillators, and thermocouples are integrated together onto the outer surface of a fibre substrate with precise semiconductor and electrode patterns. Our results show that electronic components can be integrated on a single fibre with reliable operation. We evaluate the electronic properties of the chip on the fibre as a multifunctional electronic textile platform by testing their switching and data processing, as well as sensing or transducing units for detecting optical/thermal signals. The demonstration of the electronic fibre suggests significant proof of concepts for the realization of high performance with wearable electronic textile systems.

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.

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. A suspended gate of an elastomeric composite with superparamagnetic particles laminated with an electrically conductive polymer is mechanically deformed under a magnetic field, facilitating control of the magnetic-field-dependent contact area of the suspended gate with an underlying ferroelectric layer. The remanent polarization of the ferroelectric layer is electrically programmed with the deformed suspended gate, resulting in analog conductance modulation as a function of the magnitude, number, and time interval of the input magnetic pulses. The proposed extrasensory magnetoreceptive synapse may be used as an artificially intelligent synaptic compass that facilitates barrier-adaptable navigation and mapping of a moving object.

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 (1L -MoS2 or 1L -WSe2 ). The molecular dipole moment direction determines the direction of the band bending of the 2D semiconductors, affecting the dominant transport pathways upon voltage application. Notably, in the molecular heterostructure with 1L -WSe2 , the opposite rectification direction is observed depending on the molecular dipole moment direction, which does not hold for the case with 1L -MoS2 . In addition, the nonlinearity of the molecular heterojunction selector can be significantly affected by the molecular dipole moment direction, type of 2D semiconductor, and metal work function. According to the choice of these heterojunction constituents, the nonlinearity is widely tuned from 1.0 × 101 to 3.6 × 104 for the read voltage scheme and from 0.4 × 101 to 2.0 × 105 for the half-read voltage scheme, which can be scaled up to an ≈482 Gbit crossbar array.

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. Using the filter evaluation selected via the difference among common features of classified images, the recognition accuracy achieved for three types of shoe image data sets reached ~ 82.03%, outperforming the maximum accuracy of ~ 79.23% obtained via the sequence-based decision rule in a fully connected single layer network. This training algorithm with an independent filter can precisely supply the output class in the decision step of the fully connected network.

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. To test the e-skin, tactile pressure is applied to a dome-shaped top-gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure-spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long-term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high-performance and robust tactile perception learning. The 4 × 4  device array is also able to recognize different handwritten patterns using 2-dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.

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. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

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. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to ~90 and ~70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.

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 (MoS2 and WSe2) is used as a rectifying designer at the alkyl or conjugated molecule/Au interface. From the adjustment of band alignment at molecules/two-dimensional semiconductor interface that can activate different transport pathways depending on the voltage polarity, the rectifying characteristics can be implemented and controlled. The rectification ratio could be widely tuned from 1.24 to 1.83 × 104 by changing the molecular species and type and the number of layers of the two-dimensional semiconductors in the heterostructure molecular junction. Our work sets a design rule for implementing tailored-diode function in a molecular heterojunction structure with non-functionalized molecular systems.

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. Such incompatibilities are a crucial barrier to developing an all-in-one multifunctional device capable of driving each function individually. In this study, we focus on the decoupling of electric switching and data storage functions in an organic ferroelectric memory transistor. To overcome the incompatibility of each function, the high permittivity needed for electrical switching and the ferroelectricity needed for data storage become compatible by restricting the motion of poly(vinylidene fluoride-trifluoroethylene) via photocrosslinking with bis-perfluorobenzoazide. The two-in-one device consisting of a photocrosslinked ferroelectric layer exhibits reversible and individual dual-functional operation as a typical transistor with nonvolatile memory. Moreover, a p-MOS depletion load inverter composed of the two transistors with different threshold voltages is also demonstrated by simply changing only one of the threshold voltages by polarization switching. We believe that the two-in-one device will be considered a potential component of integrated organic logic circuits, including memory, in the future.

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. Our simple dry peel-off process allows integration of the freestanding FONTs with an extremely thin film that is transferable to various conformable substrates. The FONTs exhibit excellent and reliable synaptic functions, which can be modulated by diverse electrical stimuli and relative timing (or temporal order) between the pre- and postsynaptic spikes. Furthermore, the FONTs show sustainable synaptic plasticity even under folded condition ( R = 50 µm, ε = 0.48%) for more than 6000 input spikes. Our study suggests that the ultrathin conformable organic artificial synapse platforms are considered as one of key technologies for realization of wearable intelligent electronics in the future.

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. Owing to the electrostatically controlled barrier height in the ultrathin van der Waals heterostructure, the device exhibits gate-controlled memristive switching characteristics with tunable programming voltages of 0.2-0.5 V. Notably, by electrostatic tuning with a gate terminal, it can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. Such gate tunability cannot be accomplished by previously reported synaptic devices such as memristors and synaptic transistors only mimicking the two-neuronal-based synapse. These capabilities eventually enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks.