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

Advances in memristive gas sensors: A review

With the development of gas sensing technology, traditional semiconductor-based gas sensors are difficult to meet higher performance requirements. To this end, the essence of gas sensor performance improvement depends on the innovation of gas-sensing mechanism. Gas sensors based on the memristor structure (gasistors) have been proposed in recent years, which brings new research ideas for further gas sensors development. Here, we demonstrate a comprehensive overview of the gasistor structures, fabrication, performance, applications and mechanisms. Gasistor structures are compatible with memristors and gas sensors, ranging from typical sandwich structures to those with modified electrodes and porous resistive layers aimed to balance resistive switching and gas sensing functions. Meanwhile, the fabrication process involves common materials such as metals and metal oxides, while novel materials are being explored to optimize performance. It is worth noting that gasistors exhibit unique performance including room temperature sensing, variable gas selectivity, tunable recovery and self-heating against humidity. In applications, apart from gas monitoring, gasistors are used as gas-triggered switches for accident recording, and as olfactory synapses for learning memory. The gas-sensing mechanism is respectively elucidated on the molecular and atomic scales, breaking through the surface conductivity-type mechanism. Finally, the prospects and challenges of gasistors are discussed.

Effect of grain boundaries on metal atom migration and electronic transport in 2D TMD-based resistive switches

Atomic migration from metallic contacts, and subsequent filament formation, is recognised as a prevailing mechanism leading to resistive switching in memristors based on two-dimensional materials (2DMs). This study presents a detailed atomistic examination of the migration of different metal atoms across the grain boundaries (GBs) of 2DMs, employing density functional theory in conjunction with non-equilibrium Green's function transport simulations. Various types of metallic atoms, such as Au, Cu, Al, Ni, and Ag, are examined, focusing on their migration both in the out-of-plane direction through a MoS2 layer and along the surface of the MoS2 layer, pertinent to filament formation in vertical and lateral memristors, respectively. Different types of GBs usually present in MoS2 are considered to assess their influence on the diffusion of metal atoms. The findings are compared with those for structures based on pristine MoS2 and those with mono-sulfur vacancies, aiming to understand the key elements that affect the switching performance of memristors. Furthermore, transport simulations are carried out to evaluate the effects of GBs on both out-of-plane and in-plane electron conductance, providing valuable insights into the resistive switching ratio.

2D MoTe2 memristors for energy-efficient artificial synapses and neuromorphic applications

The potential of two-dimensional (2D) transition metal dichalcogenides (TMDs), especially molybdenum telluride (MoTe2), in sophisticated electrical and low-energy neuromorphic applications, has attracted a lot of interest. The creation, characteristics, and uses of MoTe2-based memristive devices are summarized in this review paper, with an emphasis on their potential as artificial synapses for neuromorphic computing. We thoroughly examine the special properties of MoTe2, such as its remarkable resistance switching response, excellent linearity in synaptic potentiation, and customizable phase states. These characteristics make it possible to implement basic computational functions with minimal energy consumption, including decimal arithmetic operations and the commutative principles of addition and multiplication. In addition to simulating intricate synaptic processes such as long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), the article emphasizes the experimental performances of MoTe2 memristors, which include their capacity to execute exact decimal arithmetic operations. The demonstration of centimeter-scale 2D MoTe2 film-based memristor arrays attaining over 90% recognition accuracy in handwritten digit identification tests further demonstrates the devices' great scalability, stability, and incorporation capabilities. Notwithstanding these developments, issues such as poor environmental robustness, phase transition sensitivity, and low thermal stability still exist. The creation of hybrid or composite materials, doping, and structural alteration are some of the methods to get beyond these obstacles that are covered in the paper. The need for scalable, economical synthesis techniques and a better comprehension of the material's mechanical, optical, and electrical properties through modeling and experiments are emphasized.

Ultralow Powered 2D MoS2-Based Memristive Crossbar Array for Synaptic Applications

Two-dimensional materials are increasingly integral to beyond-CMOS electronics, facilitating the development of emerging memristive device technology for information storage and neuromorphic computing. Despite their emergence, some critical challenges including low device yield, substantial device-to-device (D2D), and cycle-to-cycle (C2C) variability factors hinder the development of high-density memristive devices for future low-power electronic applications. Here, we demonstrate a memristive crossbar array (MCA) in which multilayer 2D MoS2 acts as a resistive switching layer that offers lower switching voltages with a few microseconds pulse width. Additionally, the use of 2D MoS2 further excels in integration density and energy efficiency, which significantly helps to achieve a device yield of 94%. Moreover, the 2D MoS2 controlled growth process ensures the uniformity of MoS2 layers across a (10 × 10) crossbar array that enhances the stability of fabricated MCA's having minimal variability in device switching voltages (VSET: 4.16% and VRESET: 3.60%). The fabricated devices show excellent endurance (∼24,000 cycles) and retention (1.6 × 106 s). Furthermore, due to lower switching voltages and fast switching speed, the fabricated devices consume 53 pW power and 53 aJ energy, making them more energy-efficient and achieving an impressive 97.79% accuracy in MNIST digit recognition through synaptic behavior simulation.

Enhancing stability and iterative learning in neuromorphic memristor via TiN/SiOx/TiN interface engineering

In this study, we fabricated SiOx-based interface-type resistive random-access memory (ReRAM) devices and demonstrated their superior performance. The device was operated at voltages below 3 V with a maximum current of less than 1 mA. It exhibited an on/off ratio of approximately 10, with a set speed of 1 μs at 3 V and a reset speed of 1 μs at -4.5 V. Notably, the retention time at 85 °C reached 104 s. The interface-type ReRAM displayed significant linearity owing to gradual operation, which is characteristic of long-term potentiation and long-term depression. This high linearity facilitates an impressive modified national institute of standards and technology database (MNIST) digit recognition accuracy of 92.21%. To further understand the influence of endurance on learning performance, we evaluated the impact of synaptic weight degradation by comparing both TiN/SiOx/TiN and Pt/SiOx/Pt devices. This approach allowed us to assess how degradation directly affects synaptic behavior and learning efficiency in neuromorphic applications. The TiN/SiOx/TiN configuration exhibited superior endurance, as the presence of an oxygen reservoir improved synaptic performance and stability, which aligns with the gradual switching dynamics observed in our experiments and contributes to the overall device robustness and efficiency.

Unraveling the Interplay Between Memristive and Magnetoresistive Behaviors in LaCoO3/SrTiO3 Superlattice-Based Neural Synaptic Devices

Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO3/SrTiO3)n/LaCoO3/Nb:SrTiO3 (Pt/(LCO/STO)n/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively. The results show that both memristive and magnetoresistive properties can coexist without any compromise in performance, and the values of ROFF/RON and tunnel magnetoresistance (TMR) ratio are both improved by ≈1000% compared to a single-period heterostructure. Notably, the Pt/(LCO/STO)5/LCO/NSTO device demonstrates superior multilevel storage performance, characterized by extended endurance, reliable retention, high ROFF/RON ratio, significant TMR ratio, and fundamental synaptic behaviors. Furthermore, density functional theory (DFT) is employed to calculate the changes in oxygen vacancies, affecting the overall energy bands and magnetic moments in the monolayer and multi-periodic structures. Simulations using the handwritten digit recognition classification achieve the highest accuracy of 94.38%. These attributes suggest that the devices hold considerable promise for application in data storage and neuromorphic computing, offering a platform for high-density neural circuits in intelligent electronic devices.

Ternary Heterojunction Synaptic Transistors Based on Perovskite Quantum Dots

The traditional von Neumann architecture encounters significant limitations in computational efficiency and energy consumption, driving the development of neuromorphic devices. The optoelectronic synaptic device serves as a fundamental hardware foundation for the realization of neuromorphic computing and plays a pivotal role in the development of neuromorphic chips. This study develops a ternary heterojunction synaptic transistor based on perovskite quantum dots to tackle the critical challenge of synaptic weight modulation in organic synaptic devices. Compared to binary heterojunction synaptic transistor, the ternary heterojunction synaptic transistor achieves an enhanced hysteresis window due to the synergistic charge-trapping effects of acceptor material and perovskite quantum dots. The memory window decreases with increasing source-drain voltage (VDS) but expands with prolonged program/erase time, demonstrating effective carrier trapping modulation. Furthermore, the device successfully emulates typical photonic synaptic behaviors, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). This work provides a simplified strategy for high-performance optoelectronic synaptic transistors, showcasing significant potential for neuromorphic computing and adaptive intelligent systems.

Electric Field-Driven Conformational Changes in Molecular Memristor and Synaptic Behavior

This paper demonstrates the use of molecular artificial synapses in neuromorphic computing systems designed for low energy consumption. A molecular junction, based on self-assembled monolayers (SAMs) of alkanethiolates terminated with 2,2'-bipyridine complexed with cobalt chloride, exhibits synaptic behaviors with an energy consumption of 8.0 pJ µm-2. Conductance can be modulated simply by applying pulses in the incoherent charge transport (CT) regime. Charge injection in this regime allows molecules to overcome the low energy barrier for C─C bond rotations, resulting in conformational changes in the SAMs. The reversible potentiation/depression process of conductance achieves 90% accuracy in recognizing patterns from the Modified National Institute of Standards and Technology (MNIST) handwritten digit database. The molecular junction further exhibits both rectifying and conductance hysteresis behaviors, showing potential for use in selector-free synaptic arrays that efficiently suppress sneak currents.

High-Resolution Full-Field Structural Microscopy of the Voltage-Induced Filament Formation in VO2-Based Neuromorphic Devices

In order to make neuromorphic functions in memristive devices more efficient, information about the structural properties of filament formation at the micro- and mesoscopic scales is necessary. Despite extensive research on VO2, a key material due to its filament formation, local operando structural measurements remain challenging and often involve destructive specimen preparation and long rastering times, greatly limiting the scope of experimental studies. Utilizing dark-field X-ray microscopy (DFXM), a full-field imaging modality, structural signatures of the filament formation process operando are revealed in VO2 devices. DFXM experiments illustrate that rutile filaments contain isolated monoclinic clusters, indicating structural nonuniformity interior to the filament. The formation of the rutile phase beneath device electrodes was shown to precede filament development, followed by the formation of filament paths guided by nucleation sites within the device. Finally, a medium-term (<30 min) memory mechanism is observed in VO2, mediated by sites within the device gap that tend to switch at significantly lower voltages after electrical cycling, a tendency that persists through a brief thermal reset. High spatial resolution, large field-of-view, structure selectivity, and fast signal acquisition of DFXM provided insight into structural features of the filamentary channel and surrounding regions during voltage cycling.

Neuromorphic Floating-Gate Memory Based on 2D Materials

In recent years, the rapid progression of artificial intelligence and the Internet of Things has led to a significant increase in the demand for advanced computing capabilities and more robust data storage solutions. In light of these challenges, neuromorphic computing, inspired by human brain's architecture and operation principle, has surfaced as a promising answer to the growing technological demands. This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. Two-dimensional (2D) materials, distinguished by their atomic thickness and tunable physical properties, exhibit substantial potential in emulating synaptic plasticity and find broad applications in neuromorphic computing. With respect to device architecture, memory devices based on floating-gate (FG) structures demonstrate robust data retention capabilities and have been widely used in the realm of flash memory. This review begins with a succinct introduction to 2D materials and FG transistors, followed by an in-depth discussion on remarkable research progress in the integration of 2D materials with FG transistors for applications in neuromorphic computing and memory. This paper offers a thorough review of the existing research landscape, encapsulating the notable progress in swiftly expanding field. In conclusion, it addresses the constraints encountered by FG transistors using 2D materials and delineates potential future trajectories for investigation and innovation within this area.

Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices

Upconversion nanoparticles (UCNPs), incorporating lanthanide (Ln) dopants, can convert low-energy near-infrared photons into higher-energy visible or ultraviolet light through nonlinear energy transfer processes. This distinctive feature has attracted considerable attention in both fundamental research and advanced optoelectronics. Challenges such as low energy-conversion efficiency and nonradiative losses limit the performance of UCNP-based optoelectronic devices. Recent advancements including optimized core-shell structures, tailed Ln-doping concentration, and surface modifications show significant promise for improving the efficiency and stability. In addition, combining UCNPs with functional materials can broaden their applications and improve device performance, paving the way for the innovation of next-generation optoelectronics. This paper first categorizes and elaborates on various upconversion mechanisms in UCNPs, focusing on strategies to boost energy transfer efficiency and prolong luminescence. Subsequently, an in-depth discussion of the various materials that can enhance the efficiency of UCNPs and expand their functionality is provided. Furthermore, a wide range of UCNP-based optoelectronic devices is explored, and multiple emerging applications in UCNP-based neuromorphic computing are highlighted. Finally, the existing challenges and potential solutions involved in developing practical UCNPs optoelectronic devices are considered, as well as an outlook on the future of UCNPs in advanced technologies is provided.

Recent Progress on Heterojunction-Based Memristors and Artificial Synapses for Low-Power Neural Morphological Computing

Memristors and artificial synapses have attracted tremendous attention due to their promising potential for application in the field of neural morphological computing, but at the same time, continuous optimization and improvement in energy consumption are also highly desirable. In recent years, it has been demonstrated that heterojunction is of great significance in improving the energy consumption of memristors and artificial synapses. By optimizing the material composition, interface characteristics, and device structure of heterojunctions, energy consumption can be reduced, and performance stability and durability can be improved, providing strong support for achieving low-power neural morphological computing systems. Herein, we review the recent progress on heterojunction-based memristors and artificial synapses by summarizing the working mechanisms and recent advances in heterojunction memristors, in terms of material selection, structure design, fabrication techniques, performance optimization strategies, etc. Then, the applications of heterojunction-based artificial synapses in neuromorphological computing and deep learning are introduced and discussed. After that, the remaining bottlenecks restricting the development of heterojunction-based memristors and artificial synapses are introduced and discussed in detail. Finally, corresponding strategies to overcome the remaining challenges are proposed. We believe this review may shed light on the development of high-performance memristors and artificial synapse devices.

Self-Rectifying Dynamic Memristor Circuits for Periodic LIF Refractory Period Emulation and TTFS/Rate Signal Encoding

Spiking Neural Networks (SNNs) have gained attention due to their potential to improve computational efficiency compared to traditional artificial neural networks. This study investigates the use of dynamic memristors Ta/IGZO/TaOx/Pt combined with peripheral circuits to emulate the leaky integrate-and-fire behavior of neurons, focusing on incorporating a refractory period. The refractory period is crucial as it prevents neurons from becoming overactive and ensures precise timing in signal processing. This improvement allows the memristor to mimic biological neuron behavior more accurately. The memristor's transient resistance exhibits nonlinear I-V hysteresis and changes in response to pulses, enabling functions of integration, leakage, and firing. Additionally, the memristor is configured as an encoder, converting external signals into voltage pulse sequences. Using coding methods, including rate coding and time-to-first-spike (TTFS) coding, the encoder demonstrates improved signal processing, with TTFS occurring within 21 to 62 ms and encoder frequencies from 2500 to 9500 Hz. Experimental results show that this approach enhances SNN performance, making it more suitable for real-time applications and complex temporal signal processing tasks. This research highlights the potential of dynamic memristors to bridge the gap between neurons and artificial neurons, paving the way for more efficient neuromorphic computing systems.

Emerging Liquid-Based Memristive Devices for Neuromorphic Computation

To mimic the neural functions of the human brain, developing hardware with natural similarities to the human nervous system is crucial for realizing neuromorphic computing architectures. Owing to their capability to emulate artificial neurons and synapses, memristors are widely regarded as a leading candidate for achieving neuromorphic computing. However, most current memristor devices are solid-state. In contrast, biological nervous systems operate within an aqueous environment, and the human brain accomplishes intelligent behaviors such as information generation, transmission, and memory by regulating ion transport in neuronal cells. To achieve computing systems that are more analogous to biological systems and more energy-efficient, memristor devices based on liquid environments are developed. In contrast to traditional solid-state memristors, liquid-based memristors possess advantages such as anti-interference, low energy consumption, and low heat generation. Simultaneously, they demonstrate excellent biocompatibility, rendering them an ideal option for the next generation of artificial intelligence systems. Numerous experimental demonstrations of liquid-based memristors are reported, showcasing their unique memristive properties and novel neuromorphic functionalities. This review focuses on the recent developments in liquid-based memristors, discussing their operating mechanisms, structures, and functional characteristics. Additionally, the potential applications and development directions of liquid-based memristors in neuromorphic computing systems are proposed.

Emerging Materials and Computing Paradigms for Temporal Signal Analysis

In the era of relentless data generation and dynamic information streams, the demand for efficient and robust temporal signal analysis has intensified across diverse domains such as healthcare, finance, and telecommunications. This perspective study explores the unfolding landscape of emerging materials and computing paradigms that are reshaping the way temporal signals are analyzed and interpreted. Traditional signal processing techniques often fall short when confronted with the intricacies of time-varying data, prompting the exploration of innovative approaches. The rise of emerging materials and devices empowers real-time analysis by processing temporal signals in situ, mitigating latency concerns. Through this perspective, the untapped potential of emerging materials and computing paradigms for temporal signal analysis is highlighted, offering valuable insights into both challenges and opportunities. Standing on the cusp of a new era in computing, understanding and harnessing these paradigms is pivotal for unraveling the complexities embedded within the temporal dimensions of data, propelling signal analysis into realms previously deemed inaccessible.

Electrochemiluminescent tactile visual synapse enabling in situ health monitoring

Tactile visual synapses combine the functionality of tactile artificial synapses with the ability to visualize their activity in real time and provide a direct and intuitive visualization of the activity, offering an efficient route for in situ health monitoring. Herein we present a tactile visual synapse that enables in situ monitoring of finger rehabilitation and electrocardiogram analysis. Repetitive finger flexion and various arrhythmias are monitored and visually guided using the developed tactile visual synapse combined with an electrical and optical output feedback algorithm. The tactile visual synapse has the structure of an electrochemical transistor comprising an elastomeric top gate as a tactile receptor and an electrochemiluminescent ion gel as a light-emitting layer stacked on a polymeric semiconductor layer, forming an electrical synaptic channel between source and drain electrodes. The low-power (~34 μW) visualization of the tactile synaptic activity associated with the repetitive motions of fingers and heartbeats enables the development of a convenient and efficient personalized healthcare system.

HfO2-based ferroelectric synaptic devices: challenges and engineering solutions

HfO2-based ferroelectric memories have garnered significant attention for their potential to serve as artificial synaptic devices owing to their scalability and CMOS compatibility. This review examines the key material properties and challenges associated with HfO2-based ferroelectric artificial synaptic devices as well as the recent advancements in engineering strategies to improve their synaptic performance. The fundamental physics and material properties of HfO2-based ferroelectrics are reviewed to understand the theoretical origin of the aforementioned technical issues in ferroelectric HfO2-based synaptic devices. Based on the understanding, strategies to resolve the various technical issues from the device to array level are discussed, along with reviewing important progresses in recent studies. Based on these recent technical advancements, new perspectives to achieve high performance and highly reliable HfO2-based ferroelectric synaptic devices and their array are provided.

Volatile Resistive Switching and Short-Term Synaptic Plasticity in a Ferroelectric-Modulated SrFeOx Memristor

SrFeOx (SFO) offers a topotactic phase transformation between an insulating brownmillerite SrFeO2.5 (BM-SFO) phase and a conductive perovskite SrFeO3 (PV-SFO) phase, making it a competitive candidate for use in resistive memory and neuromorphic computing. However, most of existing SFO-based memristors are nonvolatile devices which struggle to achieve short-term synaptic plasticity (STP). To address this issue and realize STP, we propose to leverage ferroelectric polarization to effectively draw ions across the interface so that the PV-SFO conductive filaments (CFs) can be ruptured in absence of an external field. As a proof of concept, we fabricate ferroelectric Pb(Zr0.2Ti0.8)O3 (PZT)/BM-SFO bilayer films with Au top electrodes and SrRuO3 bottom electrodes. The device exhibits the desired volatile resistive switching behavior, with its low resistance state decaying over time. Such volatility is attributed to the positive polarization charge near the PZT/SFO interface, which can attract the oxygen ions from SFO to PZT and hence lead to the rupture of CFs. Moreover, this volatile device successfully emulates STP-related synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, learning-experience behavior, associative learning, and reservoir computing. Our study showcases an effective method for achieving volatile resistive switching and STP, which may be applied to various systems beyond SFO-based memristors.

Multifunctional Artificial Electric Synapse of MoSe2-Based Memristor toward Neuromorphic Application

Research on memristive devices to seamlessly integrate and replicate the dynamic behaviors of biological synapses will illuminate the mechanisms underlying parallel processing and information storage in the human brain, thereby affording novel insights for the advancement of artificial intelligence. Here, an artificial electric synapse is demonstrated on a one-step Mo-selenized MoSe2 memristor, having not only long-term stable resistive switching characteristics (reset 0.51 ± 0.01 V, on/off ratio > 30, retention > 103 s) but also diverse electrically adjustable synaptic behaviors, including multilevel conductance (synaptic weight), excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/D), spike-timing-dependent plasticity (STDP), and especially activity-dependent synaptic plasticity (ADSP). More significantly, neuromorphic functions of both image edge extraction and biological perception imitation have been successfully achieved. These results present a promising design toward synaptic devices for advancing neuromorphic systems with integrated brain-like neural sensing, memory, and recognition.

Achieving neuronal dynamics with spike encoding and spatial-temporal summation in vanadium-based threshold switching memristor for asynchronous signal integration

Artificial neuronal devices that emulate the dynamics of biological neurons are pivotal for advancing brain emulation and developing bio-inspired electronic systems. This paper presents the design and demonstration of an artificial neuron circuit based on a Pt/V/AlOx/Pt threshold switching memristor (TSM) integrated with an external resistor. By applying voltage pulses, we successfully exhibit the leaky integrate-and-fire (LIF) behavior, as well as both spatial and spatiotemporal summation capabilities, achieving the asynchronous signal integration. Notably, the Pt/V/AlOx/Pt TSM demonstrates ultrafast switching speeds (on/off times ∼165 ns/310 ns) and remarkable stability (endurance >102 cycles with cycle-to-cycle variations <2.5%). These attributes render the circuit highly suitable as a spike generator in neuromorphic computing applications. The Pt/V/AlOx/Pt TSM-based spike encoder can output current spikes at frequencies ranging from approximately 200 kHz to 800 kHz. The modulation of output spike frequency is achievable by adjusting the external resistor and capacitor within the spike encoder circuit, providing considerable operational flexibility. Additionally, the Pt/V/AlOx/Pt TSM boasts a lower threshold voltage (Vth ∼ 0.84 V) compared to previously reported VOx-based TSMs, leading to significantly reduced energy consumption for spike generation (∼2.75 nJ per spike).

A Self-Formed Ag Nanostructure Based Neuromorphic Device Performing Arithmetic Computation and Area Integration: Influence of Presynaptic Pulsing Scheme on Mathematical Precision

A material equivalent of a biosynapse is the key to neuromorphic architecture. Here we report a self-forming labyrinthine Ag nanostructure activated with a few pulses of 0.5 V, width and interval set at 50 ms, at current compliance (ICC) of 400 nA, serving as the active material for a highly stable device with programmable volatility. Both the conductance (G) and its retention time (tr) in the potentiated state are found to vary linearly with the pulse number for pulses of positive and negative polarities, with the nonlinearity factors being noticeably small, ∼0.03 for G during potentiation and ∼0.08 during depression. This was tested for over 200 days, and the results were highly reproducible. Relying on the high linearity, arithmetic operations involving counting of positive and negative integers were realized using pulses of both polarities, often by mixing them in the feeding sequence. The observed outcomes based on G and independently from tr are highly accurate, with deviations being typically less than ∼1.5% from the expected results. Notably, the way the pulse polarities are mixed is found to have an influence, with random sequences producing relatively larger deviations in integer estimation. However, deviations decreased with higher ICC values, which promoted stronger filament formation in the percolation networks. Besides, the G and tr values were found to vary with the pulse amplitude as well, which enabled the calculation of the area under a curve. Further, the device exhibited a simulation-based image classification accuracy of 94.95%, close to the ideal value (96.05%). Simulations utilizing the finite element method have showcased the uniqueness of the labyrinthine morphology, giving rise to field intensification along potential percolative paths.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

Robust Sodium Carboxymethyl Cellulose-Based Neuromorphic Device with High Biocompatibility Engineered through Molecular Polarization for the Emulation of Learning Behaviors in the Human Brain

Sodium carboxymethyl cellulose (CMC-Na), derived from natural cellulose and frequently employed as a biocompatible coating, thus renders it an ideal component for the construction of highly biocompatible neuromorphic devices aimed at biomachine interfaces. Here, an array of Mo/CMC-Na/ITO neuromorphic devices is fabricated, with CMC-Na serving as the functional layer. The devices exhibit capabilities to emulate various synaptic learning rules and demonstrate high endurance performance among biomaterial-based electronics, achieving stability over 2 × 104 pulses. Then, simulations of human brain-inspired learning and forgetting paradigms are conducted, highlighting the versatility of the device array in mimicking learning processes. Applications in pattern recognition leverage "learning-forgetting" paradigms, showcasing the potential of the device in cognitive tasks. Electrical measurements elucidate the mechanism of molecular polarization rotation, which offers insights into the modulation of synaptic weights within biocompatible biomaterial-based devices. Furthermore, the biocompatible properties of the devices are evaluated using human embryonic kidney 293 cells, confirming their excellent biocompatibility. The biodegradability of the devices is assessed by using physical transient tests to evaluate their sustainability in biomedical applications. Such advances represent pivotal improvements in implantable bioinspired electronics and show potential in biomachine interface and cognitive computing applications.

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

High-Performance Memristive Synapse Based on Space-Charge-Limited Conduction in LiNbO3

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO3/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (VO-VCM). The voltage-controlled VO-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of VO-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the VO-VCM-based Al/LiNbO3/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device.

Two-Dimensional Electrically Conductive Metal-Organic Framework Boosts Synaptic Plasticity for Dynamic Image Refresh, Classification, and Efferent Neuromuscular Systems

We present a two-dimensional (2D) electrically conductive metal-organic framework (EC-MOF)-based artificial synapse. The intrinsic electronic conductivity and subnanometer channels of the EC-MOF facilitate efficient ion diffusion, enable a high density of active redox centers, and significantly enhance capacitance within the artificial synapse. As a result, the synapse operates at an ultralow voltage of 10 mV and exhibits a remarkably low power consumption of approximately 1 fW, along with the longest retention time recorded for two-terminal electrolyte-type artificial synapses to date. The alignment of the quantum size of the subnanometer pores in the EC-MOF with various cations allows for versatile synaptic plasticity. This capability is applied to image refresh, classification, and efferent signal transmission for controlling artificial muscles, thereby offering a methodology for achieving tunable neuromorphic properties. These findings suggest the potential application of metal-organic frameworks in artificial nervous systems for future brain-inspired computation, peripheral interfaces, and neurorobotics.

A stochastic photo-responsive memristive neuron for an in-sensor visual system based on a restricted Boltzmann machine

In-sensor computing has gained attention as a solution to overcome the von Neumann computing bottlenecks inherent in conventional sensory systems. This attention is due to the ability of sensor elements to directly extract meaningful information from external signals, thereby simplifying complex data. The advantage of in-sensor computing can be maximized with the sampling principle of a restricted Boltzmann machine (RBM) to extract significant features. In this study, a stochastic photo-responsive neuron is developed using a TiN/In-Ga-Zn-O/TiN optoelectronic memristor and an Ag/HfO2/Pt threshold-switching memristor, which can be configured as an input neuron in an in-sensor RBM. It demonstrates a sigmoidal switching probability depending on light intensity. The stochastic properties allow for the simultaneous exploration of various neuron states within the network, making identifying optimal features in complex images easier. Based on semi-empirical simulations, high recognition accuracies of 90.9% and 95.5% are achieved using handwritten digit and face image datasets, respectively. In addition, the in-sensor RBM effectively reconstructs abnormal face images, indicating that integrating in-sensor computing with probabilistic neural networks can lead to reliable and efficient image recognition under unpredictable real-world conditions.

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Neural Functions Enabled by a Polarity-Switchable Nanofluidic Memristor

Reproducing neural functions with artificial nanofluidic systems has long been an aspirational goal for neuromorphic computing. In this study, neural functions, such as neural activation and synaptic plasticity, are successfully accomplished with a polarity-switchable nanofluidic memristor (PSNM), which is based on the anodized aluminum oxide (AAO) nanochannel array. The PSNM has unipolar memristive behavior at high electrolyte concentrations and bipolar memristive behavior at low electrolyte concentrations, which can emulate neural activation and synaptic plasticity, respectively. The mechanisms for the unipolar and bipolar memristive behaviors are related to the polyelectrolytic Wien (PEW) effect and ion accumulation/depletion effect, respectively. These findings are beneficial to the advancement of neuromorphic computing on nanofluidic platforms.

High-Performance 2D Ambipolar MoTe2 Lateral Memristors by Mild Oxidation

2D transition metal dichalcogenides (TMDCs) have been intensively explored in memristors for brain-inspired computing. Oxidation, which is usually unavoidable and harmful in 2D TMDCs, could also be used to enhance their memristive performances. However, it is still unclear how oxidation affects the resistive switching behaviors of 2D ambipolar TMDCs. In this work, a mild oxidation strategy is developed to greatly enhance the resistive switching ratio of ambipolar 2H-MoTe2 lateral memristors by more than 10 times. Such an enhancement results from the amplified doping due to O2 and H2O adsorption and the optimization of effective gate voltage distribution by mild oxidation. Moreover, the ambipolarity of 2H-MoTe2 also enables a change of resistive switching direction, which is uncommon in 2D memristors. Consequently, as an artificial synapse, the MoTe2 device exhibits a large dynamic range (≈200) and a good linearity (1.01) in long-term potentiation and depression, as well as a high-accuracy handwritten digit recognition (>96%). This work not only provides a feasible and effective way to enhance the memristive performance of 2D ambipolar materials, but also deepens the understanding of hidden mechanisms for RS behaviors in oxidized 2D materials.

High-Linearity Ta2O5 Memristor and Its Application in Gaussian Convolution Image Denoising

In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta2O5/Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta2O5/ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta2O5/ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Multimodal Artificial Synapses for Neuromorphic Application

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks

Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

An Artificial Universal Tactile Nociceptor Based on 2D Polymer Film Memristor Arrays with Tunable Resistance Switching Behaviors

Nociceptor is an important receptor in the organism's sensory system; it can perceive harmful stimuli and send signals to the brain in order to protect the body in time. The injury degree of nociceptor can be divided into three stages: self-healing injury, treatable injury, and permanent injury. However, the current studies on nociceptor simulation are limited to the self-healing stage due to the limitation of the untunable resistance switching behavior of memristors. In this study, we constructed Al/2DPTPAK+TAPB/Ag memristor arrays with adjustable memory behaviors to emulate the nociceptor of biological neural network of all three stages. For this purpose, a PDMS/AgNWs/ITO/PET pressure sensor was assembled to mimic the tactile perception of the skin. The memristor arrays can not only simulate all the response of nociceptor, i.e., the threshold, relaxation, no adaptation, and sensitization with the self-healing injury, but can also simulate the treatable injury and the permanent injury. These behaviors are both demonstrated with a single memristor and in the form of pattern mapping of the memristor array.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

Neuromorphic Computing with Emerging Antiferromagnetic Ordering in Spin-Orbit Torque Devices

Field-free switching (FFS) and spin-orbit torque (SOT)-based neuromorphic characteristics were realized in a W/Pt/Co/NiO/Pt heterostructure with a perpendicular exchange bias (HEB) for brain-inspired neuromorphic computing (NC). Experimental results using NiO-based SOT devices guided the development of fully spin-based artificial synapses and sigmoidal neurons for implementation in a three-layer artificial neural network. This system achieved impressive accuracies of 91-96% when applied to the Modified National Institute of Standards and Technology (MNIST) image data set and 78.85-81.25% when applied to Fashion MNIST images, due presumably to the emergence of robust NiO antiferromagnetic (AFM) ordering. The emergence of AFM ordering favored the FFS with an enhanced HEB, which suppressed the memristivity and reduced the recognition accuracy. This indicates a trade-off between the requirements for solid-state memory and those required for brain-inspired NC devices. Nonetheless, our findings revealed opportunities by which the two technologies could be aligned via controllable exchange coupling.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter

Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.

Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

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.

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.

Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic Hardware

Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3 ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102 ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.

Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing

Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.