Neuromorphic computing with multi-memristive synapses

Electrical switching properties of Ag2S/Cu3P under light and heat excitation

In this paper, we prepared and investigated the electrical switching behaviors of Cu3P/Ag2S heterojunction in the absence/presence of light/heat excitation. The structure exhibited bipolar memristor characteristics. The resistive switching mechanism is due to the formation of Ag conductive filaments and phase transition in Cu3P. We found that the resistance ratio (ROFF/RON) increased by a factor of 1.4/1.8 after light/heat excitation. The underlying mechanism was due to the photoelectric effect/Seebeck effect. Our results are helpful for the understanding of the resistive switching performance of Cu3P/Ag2S junctions, providing valuable insights into the factors influencing resistive switching performance and a clue for the enhancement of the memristor performance.

The Nonvolatile Memory and Neuromorphic Simulation of ReS2/h-BN/Graphene Floating Gate Devices Under Photoelectrical Hybrid Modulations

The floating gate devices, as a kind of nonvolatile memory, obtain great application potential in logic-in-memory chips. The 2D materials have been greatly studied due to atomically flat surfaces, higher carrier mobility, and excellent photoelectrical response. The 2D ReS2 flake is an excellent candidate for channel materials due to thickness-independent direct bandgap and outstanding optoelectronic response. In this paper, the floating gate devices are prepared with the ReS2/h-BN/Gr heterojunction. It obtains superior nonvolatile electrical memory characteristics, including a higher memory window ratio (81.82%), tiny writing/erasing voltage (±8 V/2 ms), long retention (>1000 s), and stable endurance (>1000 times) as well as multiple memory states. Meanwhile, electrical writing and optical erasing are achieved by applying electrical and optical pulses, and multilevel storage can easily be achieved by regulating light pulse parameters. Finally, due to the ideal long-time potentiation/depression synaptic weights regulated by light pulses and electrical pulses, the convolutional neural network (CNN) constructed by ReS2/h-BN/Gr floating gate devices can achieve image recognition with an accuracy of up to 98.15% for MNIST dataset and 91.24% for Fashion-MNIST dataset. The research work adds a powerful option for 2D materials floating gate devices to apply to logic-in-memory chips and neuromorphic computing.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

The Investigation of Neuromimetic Dynamics in Ferroelectrics via In Situ TEM

The core task of neuromorphic devices is to effectively simulate the behavior of neurons and synapses. Based on the functionality of ferroelectric domains with the advantages of low power consumption and high-speed response, great progress has been made in realizing neuromimetic behaviors such as ferroelectric synaptic devices. However, the correlation between the ferroelectric domain dynamics and neuromimetic behavior remains unclear. Here, we reveal the correlation between domain/domain wall dynamics and neuromimetic behaviors from a microscopic perspective in real-time by using high temporal and spatial resolution in situ transmission electron microscopy. Furthermore, we propose utilizing ferroelectric microstructures for the simultaneous simulation of neuronal and synaptic plasticity, which is expected to improve the integration and performance of ferroelectric neuromorphic devices. We believe that this work to study neuromimetic behavior from the perspective of domain dynamics is instructive for the development of ferroelectric neuromorphic devices.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25 ns/60 ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

Multicolor vision perception of flexible optoelectronic synapse with high sensitivity for skin sunburn warning

The development of flexible synaptic devices with multicolor signal response is important to exploit advanced artificial visual perception systems. The Sn vacancy-dominant memory and narrow gap characteristics of PEA2SnI4 make it suitable as a functional layer in ultraviolet-visible (UV-Vis) light-stimulated synaptic devices. However, such device tends to have high dark current and poor sensitivity, which is not conducive to subsequent information processing. Here, we proposed a self-powered flexible optoelectronic synapse based on PEA2SnI4 films. By introducing the electron transport layer (ETL), the dark current of the device is decreased by 5 orders of magnitude as compared to the Au/PEA2SnI4/ITO device, and the sensitivity is increased from 10.3% to 99.2% at 1.25 mW cm-2 light illumination (520 nm), indicating the vital role of the introduced ETL in promoting the separation of excitons in the interface and inhibiting the free carrier transfer. On this basis, the optoelectronic synaptic functions with integrated sensing, recognition, and memory features were realized. The array device exhibits UV-Vis light sensitivity and tunable synaptic plasticity, enabling its application for multicolor visual sensing and skin sunburn warning. This work provides an effective strategy for fabricating multicolor intelligent sensors and artificial vision systems, which facilitate the practical application of artificial optoelectronic synapses.

Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing

Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function

There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin-Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin-Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.

Effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices: the role of ZnO grain boundaries

The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 °C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 °C to 700 °C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 °C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution σ/μ, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.

Multistate Compound Magnetic Tunnel Junction Synapses for Digital Recognition

The quest to mimic the multistate synapses for bioinspired computing has triggered nascent research that leverages the well-established magnetic tunnel junction (MTJ) technology. Early works on the spin transfer torque MTJ-based artificial neural network (ANN) are susceptible to poor thermal reliability, high latency, and high critical current densities. Meanwhile, work on spin-orbit torque (SOT) MTJ-based ANN mainly utilized domain wall motion, which yields negligibly small readout signals differentiating consecutive states and has designs that are incompatible with technological scale-up. Here, we propose a multistate device concept built upon a compound MTJ consisting of multiple SOT-MTJs (number of MTJs, n = 1-4) on a shared write channel, mimicking the spin-based ANN. The n + 1 resistance states representing varying synaptic weights can be tuned by varying the voltage pulses (±1.5-1.8 V), pulse duration (100-300 ns), and applied in-plane fields (5.5-10.5 mT). A large TMR difference of more than 13.6% is observed between two consecutive states for the 4-cell compound MTJ, a 4-fold improvement from reported state-of-the-art spin-based synaptic devices. The ANN built upon the compound MTJ shows high learning accuracy for digital recognition tasks with incremental states and retraining, achieving test accuracy as high as 95.75% in the 4-cell compound MTJ. These results provide an industry-compatible platform to integrate these multistate SOT-MTJ synapses directly into neuromorphic architecture for in-memory and unconventional computing applications.

Programming memristor arrays with arbitrarily high precision for analog computing

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Aqueous chemimemristor based on proton-permeable graphene membranes

Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.

Unravelling the amorphous structure and crystallization mechanism of GeTe phase change memory materials

The reversible phase transitions in phase-change memory devices can switch on the order of nanoseconds, suggesting a close structural resemblance between the amorphous and crystalline phases. Despite this, the link between crystalline and amorphous tellurides is not fully understood nor quantified. Here we use in-situ high-temperature x-ray absorption spectroscopy (XAS) and theoretical calculations to quantify the amorphous structure of bulk and nanoscale GeTe. Based on XAS experiments, we develop a theoretical model of the amorphous GeTe structure, consisting of a disordered fcc-type Te sublattice and randomly arranged chains of Ge atoms in a tetrahedral coordination. Strikingly, our intuitive and scalable model provides an accurate description of the structural dynamics in phase-change memory materials, observed experimentally. Specifically, we present a detailed crystallization mechanism through the formation of an intermediate, partially stable 'ideal glass' state and demonstrate differences between bulk and nanoscale GeTe leading to size-dependent crystallization temperature.

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3, H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveils a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

Tailoring chemical bonds to design unconventional glasses

Glasses are commonly described as disordered counterparts of the corresponding crystals; both usually share the same short-range order, but glasses lack long-range order. Here, a quantification of chemical bonding in a series of glasses and their corresponding crystals is performed, employing two quantum-chemical bonding descriptors, the number of electrons transferred and shared between adjacent atoms. For popular glasses like SiO2, GeSe2, and GeSe, the quantum-chemical bonding descriptors of the glass and the corresponding crystal hardly differ. This explains why these glasses possess a similar short-range order as their crystals. Unconventional glasses, which differ significantly in their short-range order and optical properties from the corresponding crystals are only found in a distinct region of the map spanned by the two bonding descriptors. This region contains crystals of GeTe, Sb2Te3, and GeSb2Te4, which employ metavalent bonding. Hence, unconventional glasses are only obtained for solids, whose crystals employ theses peculiar bonds.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

WOx channel engineering of Cu-ion-driven synaptic transistor array for low-power neuromorphic computing

The multilevel current states of synaptic devices in artificial neural networks enable next-generation computing to perform cognitive functions in an energy-efficient manner. Moreover, considering large-scale synaptic arrays, multiple states programmed in a low-current regime may be required to achieve low energy consumption, as demonstrated by simple numerical calculations. Thus, we propose a three-terminal Cu-ion-actuated CuOx/HfOx/WO3 synaptic transistor array that exhibits analogously modulated channel current states in the range of tens of nanoamperes, enabled by WO3 channel engineering. The introduction of an amorphous stoichiometric WO3 channel formed by reactive sputtering with O gas significantly lowered the channel current but left it almost unchanged with respect to consecutive gate voltage pulses. An additional annealing process at 450 °C crystallized the WO3, allowing analog switching in the range of tens of nanoamperes. The incorporation of N gas during annealing induced a highly conductive channel, making the channel current modulation negligible as a function of the gate pulse. Using this optimized gate stack, Poole-Frenkel conduction was identified as a major transport characteristic in a temperature-dependent study. In addition, we found that the channel current modulation is a function of the gate current response, which is related to the degree of progressive movement of the Cu ions. Finally, the synaptic characteristics were updated using fully parallel programming and demonstrated in a 7 × 7 array. Using the CuOx/HfOx/WO3 synaptic transistors as weight elements in multilayer neural networks, we achieved a 90% recognition accuracy on the Fashion-MNIST dataset.

Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device's conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Engineering the structural and electrical interplay of nanostructured Au resistive switching networks by controlling the forming process

Networks of random-assembled gold clusters produced in the gas phase show resistive switching (RS) activity at room temperature and they are suitable for the fabrication of devices for neuromorphic data processing and classification. Fully connected cluster-assembled nanostructured Au films are characterized by a granular structure rich of interfaces, grain boundaries and crystalline defects. Here we report a systematic characterization of the electroforming process of the cluster-assembled films demonstrating how this process affects the interplay between the nano- and mesoscale film structure and the neuromorphic characteristics of the resistive switching activity. The understanding and the control of the influence of the resistive switching forming process on the organization of specific structures at different scales of the cluster-assembled films, provide the possibility to engineer random-assembled neuromorphic architectures for data processing task.

Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things

The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1-100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nano-things (BNT) offer external actuation of in-body molecular communication (MC) for targeted drug delivery to otherwise inaccessible parts of the human tissue. BNTs are inter-connected using chemical diffusion channels, forming an in vivo bio-nano network, connected to an external ex vivo environment such as the Internet using bio-cyber interfaces. Bio-luminescent bio-cyber interfacing (BBI) has proven to be promising in realizing IoBNT systems due to their non-obtrusive and low-cost implementation. BBI security, however, is a key concern during practical implementation since Internet connectivity exposes the interfaces to external threat vectors, and accurate classification of anomalous BBI traffic patterns is required to offer mitigation. However, parameter complexity and underlying intricate correlations among BBI traffic characteristics limit the use of existing machine-learning (ML) based anomaly detection methods typically requiring hand-crafted feature designing. To this end, the present work investigates the employment of deep learning (DL) algorithms allowing dynamic and scalable feature engineering to discriminate between normal and anomalous BBI traffic. During extensive validation using singular and multi-dimensional models on the generated dataset, our hybrid convolutional and recurrent ensemble (CNN + LSTM) reported an accuracy of approximately ~93.51% over other deep and shallow structures. Furthermore, employing a hybrid DL network allowed automated extraction of normal as well as temporal features in BBI data, eliminating manual selection and crafting of input features for accurate prediction. Finally, we recommend deployment primitives of the extracted optimal classifier in conventional intrusion detection systems as well as evolving non-Von Neumann architectures for real-time anomaly detection.

Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model

The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously. However, the issues of irregular Pareto front, objective discontinuity, and population degeneration strongly limit the performances of conventional multiobjective evolutionary algorithms (MOEAs) on the specific problem. Therefore, a novel competitive decomposition-based MOEA is proposed in this study, which decomposes the original problem into several constrained subproblems, with neighboring subproblems sharing overlapping regions in the objective space. The solutions in the overlapping regions participate in environmental selection for the neighboring subproblems and then propagate the selection pressure throughout the entire population. Experimental results demonstrate that the proposed algorithm can possess a more powerful optimization ability than the state-of-the-art MOEAs. Furthermore, both the DNM itself and its hardware implementation can achieve very competitive classification performances when trained by the proposed algorithm, compared with numerous widely used machine-learning approaches.

Chromatic Plasmonic Polarizer-Based Synapse for All-Optical Convolutional Neural Network

Emerging memory devices have been demonstrated as artificial synapses for neural networks. However, the process of rewriting these synapses is often inefficient, in terms of hardware and energy usage. Herein, we present a novel surface plasmon resonance polarizer-based all-optical synapse for realizing convolutional filters and optical convolutional neural networks. The synaptic device comprises nanoscale crossed gold arrays with varying vertical and horizontal arms that respond strongly to the incident light's polarization angle. The presented synapse in an optical convolutional neural network achieved excellent performance in four different convolutional results for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digit data set. After training on 1,000 images, the network achieved a classification accuracy of over 98% when tested on a separate set of 10,000 images. This presents a promising approach for designing artificial neural networks with efficient hardware and energy consumption, low cost, and scalable fabrication.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

Emerging Opportunities for 2D Materials in Neuromorphic Computing

Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

Graphene-based RRAM devices for neural computing

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

Tuning Electrochemical Stability of 5,10-Ditolylphenazine-Based Antiaromatic Materials for Unipolar Memristor toward Artificial Synapses Application

Three organic conjugated small molecules, DTA-DTPZ, Cz-DTPZ, and DTA-me-DTPZ comprising an antiaromatic 5,10-ditolylphenazine (DTPZ) core and electron-donating peripheral substituents with high HOMOs (-4.2 to -4.7 eV) and multiple reversible oxidative potentials are reported. The corresponding films sandwiched between two electrodes show unipolar and switchable hysteresis current-voltage (I-V) characteristics upon voltage sweeping, revealing the prominent features of nonvolatile memristor behaviors. The numerical simulation of the I-V curves suggests that the carriers generated by the oxidized molecules lead to the increment of conductance. However, the accumulated carriers tend to deteriorate the device endurance. The electroactive sites are fully blocked in the dimethylated molecule DTA-me-DTPZ, preventing the irreversible electrochemical reaction, thereby boosting the endurance of the memristor device over 300 cycles. Despite the considerable improvement in endurance, the decrement of on/off ratio from 105 to 101 after 250 cycles suggests that the excessive charge carriers (radical cations) remains a problem. Thus, a new strategy of doping an electron-deficient material, CN-T2T, into the unipolar active layer was introduced to further improve the device stability. The device containing DTA-me-DTPZ:CNT2T (1:1) blend as the active layer retained the endurance and on/off ratio (∼104) upon sweeping 300 cycles. The molecular designs and doping strategy demonstrate effective approaches toward more stable metal-free organic conjugated small-molecule memristors.

Thermal Management in Neuromorphic Materials, Devices, and Networks

Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine-learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware-based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy-efficient implementation of neuromorphic devices are considered. The main features of brain-inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching-based neuromorphic devices are discussed, the energy and electrical considerations for spiking-based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy-efficient computing are described.

Microstructure characterization, phase transition, and device application of phase-change memory materials

Phase-change memory (PCM), recently developed as the storage-class memory in a computer system, is a new non-volatile memory technology. In addition, the applications of PCM in a non-von Neumann computing, such as neuromorphic computing and in-memory computing, are being investigated. Although PCM-based devices have been extensively studied, several concerns regarding the electrical, thermal, and structural dynamics of phase-change devices remain. In this article, aiming at PCM devices, a comprehensive review of PCM materials is provided, including the primary PCM device mechanics that underpin read and write operations, physics-based modeling initiatives and experimental characterization of the many features examined in nanoscale PCM devices. Finally, this review will propose a prognosis on a few unsolved challenges and highlight research areas of further investigation.

A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses

Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.

Memristive neural network circuit implementation of associative learning with overshadowing and blocking

In the field of second language acquisition, overshadowing and blocking by cue competition effects in classical conditioning affect the learning and expression of human cognitive associations. In this work, a memristive neural network circuit based on neurobiological mechanisms is proposed, which consists of synapse module, neuron module, and control module. In particular, the designed network introduces an inhibitory interneuron to divide memristive synapses into excitatory and inhibitory memristive synapses, so as to mimic synaptic plasticity better. In addition, the proposed circuit can implement six functions of second language acquisition conditioning, including learning, overshadowing, blocking, recovery from overshadowing, recovery from blocking, and long-term effect of overshadowing over time leading to blocking. Overshadowing, which denotes that the more salient stimulus overshadows the learning of the less salient stimulus when two stimuli differ in salience, reduces the associative strength acquired by the less salient stimulus. Blocking, which indicates that pretraining on one stimulus blocks learning about a second stimulus, inhibits the associative strength acquired by a second stimulus. Finally, the correctness and effectiveness of implementing functions mentioned above are verified by the simulation results in PSPICE. Through further research, the proposed circuit is applied to bionic devices such as social robots or educational robots, which can address language and cognitive disorders via assisted learning and training.

High Amplitude Spike Generator in Au Nanodot-Incorporated NbOx Mott Memristor

NbO_x-based Mott memristors exhibit fast threshold switching behaviors, making them suitable for spike generators in neuromorphic computing and stochastic clock generators in security devices. In these applications, a high output spike amplitude is necessary for threshold level control and accurate signal detection. Here, we propose a materialwise solution to obtain the high amplitude spikes by inserting Au nanodots into the NbO_x device. The Au nanodots enable increasing the threshold voltage by modulating the oxygen contents at the electrode-oxide interface, providing a higher ON current compared to nanodot-free NbO_x devices. Also, the reduction of the local switching region volume decreases the thermal capacitance of the system, allowing the maximum spike amplitude generation. Consequently, the Au nanodot incorporation increases the spike amplitude of the NbO_x device by 6 times, without any additional external circuit elements. The results are systematically supported by both a numerical model and a finite-element-method-based multiphysics model.

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.

Double-Floating-Gate van der Waals Transistor for High-Precision Synaptic Operations

Two-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (∼10⁷), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (Fashion-MNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing.

Proton-Gated Synaptic Transistors, Based on an Electron-Beam Patterned Nafion Electrolyte

Neuromorphic processors using artificial neural networks are the center of attention for energy-efficient analog computing. Artificial synapses act as building blocks in such neural networks for parallel information processing and data storage. Herein we describe the fabrication of a proton-gated synaptic transistor using a Nafion electrolyte thin film, which is patterned by electron-beam lithography (EBL). The device has an active channel of indium-zinc-oxide (IZO) between the source and drain electrodes, which shows Ohmic behavior with a conductance level on the order of 100 μS. Under voltage applications to the gate electrode, the channel conductance is changed due to the injection and extraction of protons between the IZO channel and the Nafion electrolyte, emulating various synaptic functions with short-term and long-term plasticity. When positive (negative) gate voltage pulses are consecutively applied, the device exhibits long-term potentiation (depression) at the same number of steps as the number of input pulses. Based on these characteristics, an artificial neural network using this transistor shows ∼84% image recognition accuracy for handwritten digits. The subject transistor also successfully mimics paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. Finally, dynamical pattern image memorization is demonstrated in a 5 × 5 array of these synaptic transistors. The results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.

Realizing Electronic Synapses by Defect Engineering in Polycrystalline Two-Dimensional MoS2 for Neuromorphic Computing

Neuromorphic computing based on two-dimensional transition-metal dichalcogenides (2D TMDs) has attracted significant attention recently due to their extraordinary properties generated by the atomic-thick layered structure. This study presents sulfur-defect-assisted MoS₂ artificial synaptic devices fabricated by a simple sputtering process, followed by a precise sulfur (S) vacancy-engineering process. While the as-sputtered MoS₂ film does not show synaptic behavior, the S vacancy-controlled MoS₂ film exhibits excellent synapse with remarkable nonvolatile memory characteristics such as a high switching ratio (∼10³), a large memory window, and long retention time (∼10⁴ s) in addition to synaptic functions such as paired-pulse facilitation (PPF) and long-term potentiation (LTP)/depression (LTD). The synaptic device working mechanism of Schottky barrier height modulation by redistributing S vacancies was systemically analyzed by electrical, physical, and microscopy characterizations. The presented MoS₂ synaptic device, based on the precise defect engineering of sputtered MoS₂, is a facile, low-cost, complementary metal-oxide semiconductor (CMOS)-compatible, and scalable method and provides a procedural guideline for the design of practical 2D TMD-based neuromorphic computing.

Controllable digital and analog resistive switching behavior of 2D layered WSe2 nanosheets for neuromorphic computing

Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe₂ nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 10⁴. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe₂ nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.

Modulation of Switching Characteristics in a Single VO2 Nanobeam with Interfacial Strain via the Interconnection of Multiple Nanoscale Channels

We demonstrate the modulation of electrical switching properties through the interconnection of multiple nanoscale channels (∼600 nm) in a single VO₂ nanobeam with a coexisting metal-insulator (M-I) domain configuration during phase transition. The Raman scattering characteristics of the synthesized VO₂ nanobeams provide evidence that substrate-induced interfacial strain can be inhomogeneously distributed along the length of the nanobeam. Interestingly, the nanoscale VO₂ devices with the same channel length and width exhibit distinct differences in hysteric current-voltage characteristics, which are explained by theoretical calculations of resistance change combined with Joule heating simulations of the nanoscale VO₂ channels. The observed results can be attributed to the difference in the spatial distribution and fraction ratios of M-I domains due to interfacial strain in the nanoscale VO₂ channels during the metal-insulator transition process. Moreover, we demonstrate the electrically activated resistive switching characteristics based on the hysteresis behaviors of the interconnected nanoscale channels, implying the possibility of manipulating multiple resistive states. Our results may offer insights into the nanoscale engineering of correlated phases in VO₂ as the key materials of neuromorphic computing for which nonlinear conductance is essential.

Echo state graph neural networks with analogue random resistive memory arrays

Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conventional digital hardware, including the slowdown of Moore’s law due to transistor scaling limits and the von Neumann bottleneck incurred by physically separated memory and processing units, as well as a high training cost. Here we present a hardware–software co-design to address these challenges, by designing an echo state graph neural network based on random resistive memory arrays, which are built from low-cost, nanoscale and stackable resistors for efficient in-memory computing. This approach leverages the intrinsic stochasticity of dielectric breakdown in resistive switching to implement random projections in hardware for an echo state network that effectively minimizes the training complexity thanks to its fixed and random weights. The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 2.16×, 35.42× and 40.37× improvements in energy efficiency for a projected random resistive memory-based hybrid analogue–digital system over a state-of-the-art graphics processing unit and 99.35%, 99.99% and 91.40% reductions of backward pass complexity compared with conventional graph learning. The results point to a promising direction for next-generation artificial intelligence systems for graph learning.

Improving the linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistors via CdSe quantum dots decoration

The linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistor (SWCNT FET) was improved by CdSe quantum dots decoration. The linearity of synaptic plasticity in SWCNT FET with decorating QDs was further improved by reducing the P-type doping level from the atmosphere. The synaptic behavior of SWCNT FET is found to be dominated by the charging and discharging processes of interface traps and surface traps, which are predominantly composed of H₂O/O₂redox couples. The improved synaptic behavior is mainly due to the reduction of the interface trap charging process after QDs decoration. The inherent correlation between the device synaptic behavior and the electron capture process of the traps are investigated through charging-based trap characterization. This study provides an effective scheme for improving linearity and designing new-type SWCNT synaptic devices.

Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices

A three-terminal synaptic transistor enables more accurate controllability over the conductance compared with traditional two-terminal synaptic devices for the synaptic devices in hardware-oriented neuromorphic systems. In this work, we fabricated IGZO-based three-terminal devices comprising HfAlO_x and CeO_x layers to demonstrate the synaptic operations. The chemical compositions and thicknesses of the devices were verified by transmission electron microscopy and energy dispersive spectroscopy in cooperation. The excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), and short-term depression (STD) of the synaptic devices were realized for the short-term memory behaviors. The IGZO-based three-terminal synaptic transistor could thus be controlled appropriately by the amplitude, width, and interval time of the pulses for implementing the neuromorphic systems.

Energy Efficient Neuro-Inspired Phase-Change Memory Based on Ge4 Sb6 Te7 as a Novel Epitaxial Nanocomposite

Phase-change memory (PCM) is a promising candidate for neuro-inspired, data-intensive artificial intelligence applications, which relies on the physical attributes of PCM materials including gradual change of resistance states and multilevel operation with low resistance drift. However, achieving these attributes simultaneously remains a fundamental challenge for PCM materials such as Ge₂ Sb₂ Te₅ , the most commonly used material. Here bi-directional gradual resistance changes with ≈10× resistance window using low energy pulses are demonstrated in nanoscale PCM devices based on Ge₄ Sb₆ Te₇ , a new phase-change nanocomposite material . These devices show 13 resistance levels with low resistance drift for the first 8 levels, a resistance on/off ratio of ≈1000, and low variability. These attributes are enabled by the unique microstructural and electro-thermal properties of Ge₄ Sb₆ Te₇ , a nanocomposite consisting of epitaxial SbTe nanoclusters within the Ge-Sb-Te matrix, and a higher crystallization but lower melting temperature than Ge₂ Sb₂ Te₅ . These results advance the pathway toward energy-efficient analog computing using PCM.

Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing

With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.

Memristive Circuit Design of Brain-Like Emotional Learning and Generation

In this work, a bionic memristive circuit with the functions of emotional learning and generation is proposed, which can perform brain-like emotional learning and generation based on various types of input information. The proposed circuit is designed based on the brain emotional learning theory in the limbic system, which mainly includes three layers of design: 1) the bottom layer is the design of the basic unit modules, such as neuron and synapse; 2) the middle layer is the design of the functional modules related to emotional learning in the limbic system, such as the amygdala, thalamus, and so on; and 3) the top layer is the design of the overall circuit, which is used to realize the function of the emotional generation. A 2-D emotional space composed of valence and arousal signals is adopted. According to the above bottom-up circuit design method, the valence and arousal signals can be generated, respectively, by designing corresponding emotional learning circuits, so as to form continuous emotions. The volatile and nonvolatile memristors are mainly used to mimic the functions of the neuron and synapse at the bottom layer of the circuit to achieve the core emotional learning function of the middle layer, thereby constructing a brain-like information processing architecture to realize the function of the emotional generation in the top layer. The simulation results in PSPICE show that the proposed circuit can learn and generate emotions like humans. If the proposed circuit is applied to a humanoid robot platform through further research, the robot may have the ability of personalized emotional interaction with humans, so that it can be effectively used in emotional companionship and other aspects.

Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning. Delightedly, rapid developments in neuromorphic computing provide a new paradigm to solve this dilemma. Furthermore, neuromorphic devices that can realize synaptic plasticity and neuromorphic function have extraordinary significance for neuromorphic system. A three-terminal neuromorphic transistor is one of the typical representatives. In addition, human body has five senses, including vision, touch, auditory sense, olfactory sense and gustatory sense, providing abundant information for brain. Inspired by the human perception system, developments in artificial perception system will give new vitality to intelligent robots. This review discusses the operation mechanism, function and application of neuromorphic transistors. The latest progresses in artificial perception systems based on neuromorphic transistors are provided. Finally, the opportunities and challenges of artificial perception systems are summarized.