Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing

Advances in Ga2O3-based memristor devices, modeling, properties, and applications for low power neuromorphic computing

About a decade ago, gallium oxide (Ga2O3) was found to be a very attractive ultrawide-bandgap (4.6-4.9 eV) semiconductor for next-generation low-power devices. Ga2O3 materials have attracted a lot of scientific and technical interest because of their outstanding properties and numerous application opportunities in the field of semiconductor based memristor technology. This review is focused on Ga2O3 thin-film memristors for smart technologies. The capacitance behavior of memristors is very important for adapting nonlinear memristor responses. Also, this comprehensive review explores in depth the ideas, device construction, and manufacturing procedures for Ga2O3-based memristor devices. To improve the device's behavior and performance improvement, a detailed analysis of many modeling and simulation techniques is given. Also, advanced characterization techniques, such as electrical, structural, and thermal evaluations, for studying artificial optoelectronic synaptic characteristics, which are important for use in computational neuroscience, are discussed in detail. The synaptic activities revealed that learning and memory processes were aided by potentiation and depression similar to those found in biological synapses. The most notable accomplishment is the realization of quaternary memory storage in a single device. This idea is supported by empirical evidence and simulations, which demonstrate the possibility of storing and maintaining multiple memory states. This study establishes oxide semiconductor memristors as a doorway to quaternary memory storage and improved synaptic functioning, paving the way for optoelectronic synaptic devices with greater memory capacity.

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.

Biocompatible Neuromorphic Device Array Based on Naturally Sourced Mucin for Implantable Bioelectronics

Although the demand for intelligent implantable bioelectronics is steadily increasing, their progress is hindered by the limited availability of materials with sufficient biocompatibility for implantation. Herein, we propose a neuromorphic device with human brain-inspired biomimetic functionality utilizing naturally sourced mucin as the active layer material. The mucin-based neuromorphic memristor (MNM) array successfully mimics key synaptic behaviors uniformly, including a paired-pulse facilitation index of 122.65%, transition from short-term to long-term memory, long-term potentiation, and long-term depression. In addition to the effect of the defect-rich mucin active layer, these behaviors are enhanced by the presence of a MgOx interfacial layer formed at its interface with the Mg top electrode. The cell cytotoxicity test results demonstrate the superior biocompatibility of the MNM array, which shows a relative cell viability of 108.46% after 72 h of cell culture. Moreover, the artificial neural network simulation demonstrates a recognition rate of 89.93% after 125 training epochs, which suggests that naturally sourced materials, including mucin, can be used in implantable bioelectronics for advanced medical healthcare applications.

Uniformity, Linearity, and Symmetry Enhancement in TiOx/MoS2-xOx Based Analog RRAM via S-Vacancy Confined Nanofilament

Due to the stochastic formation of conductive filaments (CFs), analog resistive random-access memory (RRAM) struggles to simultaneously achieve low variability, high linearity, and symmetry in conductance tuning, thus complicating on-chip training and limiting versatility of RRAM based computing-in-memory (CIM) chips. In this study, we present a simple and effective approach using monolayer (ML) MoS2 as interlayer to control the CFs formation in TiOx switching layer. The limited S-vacancies (Sv) in MoS2-xOx interlayer can further confine the position, size, and quantity of CFs, resulting in a highly uniform and symmetrical switching behavior. The set and reset voltages (Vset and Vreset) in TiOx/MoS2-xOx based RRAM are symmetric, with cycle-to-cycle variations of 1.28% and 1.7%, respectively. Moreover, high conductance tuning linearity and 64-level switching capabilities are achieved, which facilitate high accuracy (93.02%) on-chip training. This method mitigates the device nonidealities of analog RRAM through Sv confined CFs, accelerating the development of RRAM based CIM chips.

Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications

Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

Wafer scale WS2based ultrafast photosensing and memory computing devices for neuromorphic computing

Integration of optical sensors with memristors can establish the bridge between photosensing and memory devices for Internet of Things (IoT) based applications. This paper presents the realization of integrated sensing and computing memory (ISCM) devices using tungsten disulfide (WS2) and their application for neuromorphic computing. The ISCM device fabrication process is scalable as microfabrication steps followed on 2″ wafer, ISCM device testing and image classification for neuromorphic computing. The photosensing/memory tests were conducted using electrical and optical stimulations (broadband spectrum). The fabricated photosensing device offers a higher responsivity (8 A W-1), higher detectivity (2.85 × 1011Jones) and fast response speed (80.2/78.3μs) at 950 nm. The memory device has shown a set/reset time of 51.6/73.5μs respectively. Further, the repeatability, stability and reproducibility tests were conducted by stimulating the device with different modulating frequencies. The frequency modulation tests confirm that the ISCM devices are stable and perfect candidate for real-time IoT applications. Moreover, the device's potentiation and depression results were used for image classification with the accuracy of 98.27%. These demonstrated device's test results provide possibilities to fabricate the smart sensors with integrated functionalities.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Physical reservoirs based on MoS2-HZO integrated ferroelectric field-effect transistors for reservoir computing systems

Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO2-based ferroelectric materials such as Hf0.5Zr0.5O2 (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS2-HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.

Ag-doped non-imperfection-enabled uniform memristive neuromorphic device based on van der Waals indium phosphorus sulfide

Memristors are considered promising energy-efficient artificial intelligence hardware, which can eliminate the von Neumann bottleneck by parallel in-memory computing. The common imperfection-enabled memristors are plagued with critical variability issues impeding their commercialization. Reported approaches to reduce the variability usually sacrifice other performances, e.g., small on/off ratios and high operation currents. Here, we demonstrate an unconventional Ag-doped nonimperfection diffusion channel-enabled memristor in van der Waals indium phosphorus sulfide, which can combine ultralow variabilities with desirable metrics. We achieve operation voltage, resistance, and on/off ratio variations down to 3.8, 2.3, and 6.9% at their extreme values of 0.2 V, 1011 ohms, and 108, respectively. Meanwhile, the operation current can be pushed from 1 nA to 1 pA at the scalability limit of 6 nm after Ag doping. Fourteen Boolean logic functions and convolutional image processing are successfully implemented by the memristors, manifesting the potential for logic-in-memory devices and efficient non-von Neumann accelerators.

Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Insights into synaptic functionality and resistive switching in lead iodide flexible memristor devices

Neuromorphic platforms are gaining popularity due to their superior efficiency, low power consumption, and adaptable parallel signal processing capabilities, overcoming the limitations of traditional von Neumann architecture. We conduct an in-depth investigation into the factors influencing the resistive switching mechanism in memristor devices utilizing lead iodide (PbI2). We establish correlations between device performance and morphological features, unveiling synaptic like behaviour of device making it suitable for range of flexible neuromorphic applications. Notably, a highly reliable unipolar switching mechanism is identified, exhibiting stability even under mechanical strain (with a bending radius of approximately 4 mm) and in high humidity environment (at 75% relative humidity) without the need for encapsulation. The investigation delves into the complex interplay of charge transport, ion migration and the active interface, elucidating the factors contributing to the remarkable resistive switching observed in PbI2-based memristors. The detailed findings highlight synaptic behaviors akin to the modulation of synaptic strengths, with an impressive potentiation and depression of 2 × 104 cycles, emphasizing the role of spike time-dependent plasticity (STDP). The flexible platform demonstrates exceptional performance, achieving a simulated accuracy rate of 95.06% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) dataset with just 30 training epochs. Ultimately, this research underscores the potential of PbI2-based flexible memristor devices as versatile component for neuromorphic computing. Moreover, it demonstrate the robustness of PbI2 memristors in terms of their resistive switching capabilities, showcasing resilience both mechanically and electrically. This underscores their potential in replicating synaptic functions for advanced information processing systems.

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.

Coexisting Phases in Transition Metal Dichalcogenides: Overview, Synthesis, Applications, and Prospects

Over the past decade, significant advancements have been made in phase engineering of two-dimensional transition metal dichalcogenides (TMDCs), thereby allowing controlled synthesis of various phases of TMDCs and facile conversion between them. Recently, there has been emerging interest in TMDC coexisting phases, which contain multiple phases within one nanostructured TMDC. By taking advantage of the merits from the component phases, the coexisting phases offer enhanced performance in many aspects compared with single-phase TMDCs. Herein, this review article thoroughly expounds the latest progress and ongoing efforts on the syntheses, properties, and applications of TMDC coexisting phases. The introduction section overviews the main phases of TMDCs (2H, 3R, 1T, 1T', 1Td), along with the advantages of phase coexistence. The subsequent section focuses on the synthesis methods for coexisting phases of TMDCs, with particular attention to local patterning and random formations. Furthermore, on the basis of the versatile properties of TMDC coexisting phases, their applications in magnetism, valleytronics, field-effect transistors, memristors, and catalysis are discussed. Lastly, a perspective is presented on the future development, challenges, and potential opportunities of TMDC coexisting phases. This review aims to provide insights into the phase engineering of 2D materials for both scientific and engineering communities and contribute to further advancements in this emerging field.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.

Improved resistive switching performance and realized electric control of exchange bias in a NiO/HfO2 bilayer structure

The fluctuation of switching parameters is unavoidable in conductive filaments (CFs)-type resistive switching (RS) devices, which restricts the application in resistive random-access memory. Here, we employed an uninsulated antiferromagnetic (AFM) NiO layer adhered to a well-insulating HfO2 layer to effectively suppress the RS fluctuation by achieving forming-free, narrower set voltage distribution, a more stable on/off ratio, and better endurance in comparison with single-HfO2-layer based RS devices. The conduction scaling behavior indicates that the NiO/HfO2 bilayer has a smaller scale parameter S0 (lateral dimension of the bottleneck for the CFs). Besides this, considering some preexisting conductive paths in the NiO layer, the electric fields and the formation/rupture of CFs can be highly localized, leading to reduced switching fluctuation and improved RS performance in the NiO/HfO2-based RS devices. Moreover, asymmetric I-V curves measured in a high resistance state (HRS) in positively and negatively biased regions and the electric modulation of exchange bias (EB) arising from the Co-NiO interfacial coupling are favorable for revealing the inherent mechanism for RS. The coexistence of RS and EB is also useful to the design of novel multifunctional memory devices.

Flexible, Transparent, and Wafer-Scale Artificial Synapse Array Based on TiOx /Ti3 C2 Tx Film for Neuromorphic Computing

A high-density neuromorphic computing memristor array based on 2D materials paves the way for next-generation information-processing components and in-memory computing systems. However, the traditional 2D-materials-based memristor devices suffer from poor flexibility and opacity, which hinders the application of memristors in flexible electronics. Here, a flexible artificial synapse array based on TiOx /Ti3 C2 Tx film is fabricated by a convenient and energy-efficient solution-processing technique, which realizes high transmittance (≈90%) and oxidation resistance (>30 days). The TiOx /Ti3 C2 Tx memristor shows low device-to-device variability, long memory retention and endurance, a high ON/OFF ratio, and fundamental synaptic behavior. Furthermore, satisfactory flexibility (R = 1.0 mm) and mechanical endurance (104 bending cycles) of the TiOx /Ti3 C2 Tx memristor are achieved, which is superior to other film memristors prepared by chemical vapor deposition. In addition, high-precision (>96.44%) MNIST handwritten digits recognition classification simulation indicates that the TiOx /Ti3 C2 Tx artificial synapse array holds promise for future neuromorphic computing applications, and provides excellent high-density neuron circuits for new flexible intelligent electronic equipment.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ ∼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

Optically Reconfigurable Complementary Logic Gates Enabled by Bipolar Photoresponse in Gallium Selenide Memtransistor

To avoid the complexity of the circuit for in-memory computing, simultaneous execution of multiple logic gates (OR, AND, NOR, and NAND) and memory behavior are demonstrated in a single device of oxygen plasma-treated gallium selenide (GaSe) memtransistor. Resistive switching behavior with R_ON /R_OFF ratio in the range of 10⁴ to 10⁶ is obtained depending on the channel length (150 to 1600 nm). Oxygen plasma treatment on GaSe film created shallow and deep-level defect states, which exhibit carriers trapping/de-trapping, that lead to negative and positive photoconductance at positive and negative gate voltages, respectively. This distinguishing feature of gate-dependent transition of negative to positive photoconductance encourages the execution of four logic gates in the single memory device, which is elusive in conventional memtransistor. Additionally, it is feasible to reversibly switch between two logic gates by just adjusting the gate voltages, e.g., NAND/NOR and AND/NAND. All logic gates presented high stability. Additionally, memtransistor array (1×8) is fabricated and programmed into binary bits representing ASCII (American Standard Code for Information Interchange) code for the uppercase letter "N". This facile device configuration can provide the functionality of both logic and memory devices for emerging neuromorphic computing.

Performance Regulation of a ZnO/WOx-Based Memristor and Its Application in an Emotion Circuit

The development of a memristor is very important for artificial intelligence and new electronic circuits. In this work, Ag(Al)/ZnO/WO_x/FTO memristors are fabricated by magnetron sputtering, and the device performance is further improved through annealing and oxygen supply during sputtering. The experimental data show that the FTO/WO_x/ZnO-O₂/Ag memristor has the largest high resistance state (HRS)/low resistance state (LRS) resistance ratio and the best durability. Through data fitting and analysis, the switching mechanism of memristors with different top electrodes is investigated. Furthermore, the physical model of the best performance memristor was established by Simulink, and an emotion-monitoring circuit was constructed on this basis. The circuit can be used to monitor and record the mood changes, and the feedback of the emotion monitoring can be fed back to the user to help them adjust the mood.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

Resistive Switching Crossbar Arrays Based on Layered Materials

Resistive switching (RS) devices are metal/insulator/metal cells that can change their electrical resistance when electrical stimuli are applied between the electrodes, and they can be used to store and compute data. Planar crossbar arrays of RS devices can offer a high integration density (>10⁸  devices mm^{- 2} ) and this can be further enhanced by stacking them three-dimensionally. The advantage of using layered materials (LMs) in RS devices compared to traditional phase-change materials and metal oxides is that their electrical properties can be adjusted with a higher precision. Here, the key figures-of-merit and procedures to implement LM-based RS devices are defined. LM-based RS devices fabricated using methods compatible with industry are identified and discussed. The focus is on small devices (size < 9 µm² ) arranged in crossbar structures, since larger devices may be affected by artifacts, such as grain boundaries and flake junctions. How to enhance device performance, so to accelerate the development of this technology, is also discussed.

A Highly Crystalline Single Layer 2D Polymer for Low Variability and Excellent Scalability Molecular Memristors

Large-scale growth of highly crystalline single layer 2D polymers (SL-2DPs) and their subsequent integration into memristors is key to advancing the development of high-density data storage devices. However, leakage problems resulting from the porous structure of 2DPs continue to make such advances extremely challenging. Herein, we overcome this issue by incorporating long alkoxy chains into key molecular building blocks to obtain a highly crystalline 2DP, as visualized by scanning tunneling microscopy, and prevent metal permeation in the subsequent device fabrication process. SL-2DP memristors constructed via direct evaporation of the top electrodes exhibit low variability (σ_Vset  = 0.14) due to the single-monomer-thick feature together with the high regular structure and coordination ability which minimizes the stochastic spatial distribution of conductive filaments (CFs) in both vertical and lateral dimensions. The variability is further decreased to 0.04 by confining the formation and fracture of CFs to the interface through the utilization of bilayer junctions. Using peak force tunneling atomic force microscopy, the nanometer scalability (< 50 nm² ) and low power consumption of these molecular memristor devices are demonstrated.

Printed Electronics Based on 2D Material Inks: Preparation, Properties, and Applications toward Memristors

Printed electronics, which fabricate electrical components and circuits on various substrates by leveraging functional inks and advanced printing technologies, have recently attracted tremendous attention due to their capability of large-scale, high-speed, and cost-effective manufacturing and also their great potential in flexible and wearable devices. To further achieve multifunctional, practical, and commercial applications, various printing technologies toward smarter pattern-design, higher resolution, greater production flexibility, and novel ink formulations toward multi-functionalities and high quality have been insensitively investigated. 2D materials, possessing atomically thin thickness, unique properties and excellent solution-processable ability, hold great potential for high-quality inks. Besides, the great variety of 2D materials ranging from metals, semiconductors to insulators offers great freedom to formulate versatile inks to construct various printed electronics. Here, a detailed review of the progress on 2D material inks formulation and its printed applications has been provided, specifically with an emphasis on emerging printed memristors. Finally, the challenges facing the field and prospects of 2D material inks and printed electronics are discussed.

Bipolar Resistive Switching in TiO2 Artificial Synapse Mimicking Pavlov's Associative Learning

Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO₂/Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO₂ memristor shows excellent performance of digital resistive switching with a memory window of order ∼10³. Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO₂-based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC.

Atomically Thin Synapse Networks on Van Der Waals Photo-Memtransistors

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti_4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Conventional filamentary memristors are limited in dynamics by the high electric-field dependence of the conductive filament. Here, Jeong et al. presents a method which creates a cluster-type memristor, enabling large conductance range and long data retention.

In-Memory Computing using Memristor Arrays with Ultrathin 2D PdSeOx /PdSe2 Heterostructure

In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeO_x /PdSe₂ heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.

Polarity-Differentiated Dielectric Materials in Monolayer Graphene Charge-Regulated Field-Effect Transistors for an Artificial Reflex Arc and Pain-Modulation System of the Spinal Cord

The nervous system is a vital part of organisms to survive and it endows them with remarkable abilities, such as perception, recognition, regulation, learning, and decision-making, by intertwining myriad neurons. To realize such outstanding efficacies and functions, many artificial devices and systems have been investigated to emulate the operating principles of the nervous system. Here, an artificial reflex arc (ARA) and artificial pain modulation system (APMS) are proposed to imitate the unconscious behaviors of the spinal cord. Gd_x O_y - and Al_x O_y -based charge-regulated field-effect transistors (CRFETs) with a monolayer graphene channel are fabricated and adopted as inhibitory and excitatory synapses, respectively, under the same pulse signals to mimic the biological reflex arc through a connection with a poly(vinylidene fluoride-co-trifluoroethylene)-based actuator. Additionally, a memristor is integrated with a CRFET as the interneuron to regulate the Dirac point by controlling the voltage drop on the graphene channel, analogous to the descending pain-inhibition system in the spinal cord, to prevent excessive pain perception. The proposed ARA and APMS provide a significant step forward to realizing the functions of the nervous system, giving promising potential for developing future intelligent alarm systems, neuroprosthetics, and neurorobotics.

Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology

The family of two-dimensional (2D) materials composed of atomically thin layers connected via van der Waals interactions has attracted much curiosity due to a variety of intriguing physical, optical, and electrical characteristics. The significance of analyzing statistics on electrical devices and circuits based on 2D materials is seldom underestimated. Certain requirements must be met to deliver scientific knowledge that is beneficial in the field of 2D electronics: synthesis and fabrication must occur at the wafer level, variations in morphology and lattice alterations must be visible and statistically verified, and device dimensions must be appropriate. The authors discussed the most recent significant concerns of 2D materials in the provided prose and attempted to highlight the prerequisites for synthesis, yield, and mechanism behind device-to-device variability, reliability, and durability benchmarking under memristors characteristics; they also indexed some useful approaches that have already been reported to be advantageous in large-scale production. Commercial applications, on the other hand, will necessitate further effort.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

A Modified SiO2-Based Memristor with Reliable Switching and Multifunctional Synaptic Behaviors

Dielectric SiO₂ has possible uses as an active layer for emerging memory due to its high on/off ratio and low operation voltage. However, SiO₂-based memory that relies on the conducting filament still has limited endurance and stability. Here, we have constructed a passivated layer of SiO₂ using Ag-doped SrTiO₃, which serves as a Ag ion reservoir for the control of filament formation. It is demonstrated that the modified memristor presents an excellent endurance switching and could stably be operated in an ambient environment for 20 days without visible degradation. Based on the reliable switching, the synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, transition from short-term memory to long-term memory, and potentiation/depression have also been implemented. Furthermore, a 7 × 7 pixel array made from memristors has successfully mimicked simple learning and forgetting behavior. The experimental results offer an alternative approach for SiO₂-based memristors and a possibility to be applied in neuromorphic computing.

Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.

Artificial Neuron and Synapse Devices Based on 2D Materials

Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next-generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material-based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling-bond-free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials-based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all-or-nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials-based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials-based neuromorphic devices are discussed.

Growth and Interlayer Engineering of 2D Layered Semiconductors for Future Electronics

Layered materials that do not form a covalent bond in a vertical direction can be prepared in a few atoms to one atom thickness without dangling bonds. This distinctive characteristic of limiting thickness around the sub-nanometer level allowed scientists to explore various physical phenomena in the quantum realm. In addition to the contribution to fundamental science, various applications were proposed. Representatively, they were suggested as a promising material for future electronics. This is because (i) the dangling-bond-free nature inhibits surface scattering, thus carrier mobility can be maintained at sub-nanometer range; (ii) the ultrathin nature allows the short-channel effect to be overcome. In order to establish fundamental discoveries and utilize them in practical applications, appropriate preparation methods are required. On the other hand, adjusting properties to fit the desired application properly is another critical issue. Hence, in this review, we first describe the preparation method of layered materials. Proper growth techniques for target applications and the growth of emerging materials at the beginning stage will be extensively discussed. In addition, we suggest interlayer engineering via intercalation as a method for the development of artificial crystal. Since infinite combinations of the host-intercalant combination are possible, it is expected to expand the material system from the current compound system. Finally, inevitable factors that layered materials must face to be used as electronic applications will be introduced with possible solutions. Emerging electronic devices realized by layered materials are also discussed.

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

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics

The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.

Conductive-bridging random-access memories for emerging neuromorphic computing

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.