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The switching dynamics of a Au∥V S 2 @MoS 2 atomristor is explored by first-principles computations of the atomic-configuration energy and electron transport. It is found that external bias can reduce the energy barrier between the two (high- and low-) conduction states, to achieve nonvolatile resistive switching. We find that the force acting on the switching atom is a combination of electrostatic force (while its charge is induced both electrostatically and chemically) and also by electron-wind, whose effect may hinder the writing process at larger bias. The analysis uncovers how the writing and reading processes of the atomristor depend on several factors: (i) atomic structure details of the Au tip; (ii) the space-gap distance between the tip and MoS 2 layer; and (iii) tip metal choice. The fundamental understanding of switching events provides useful guidance for memristor design and possible limitations.

Two-dimensional (2D) materials are promising for resistive switching in neuromorphic and in-memory computing, as their atomic thickness substantially improve the energetic budget of the device and circuits. However, many 2D resistive switching materials struggle with complex growth methods or limited scalability. 2D tellurium exhibits striking characteristics such as simplicity in chemistry, structure, and synthesis making it suitable for various applications. This study reports the first memristor design based on nanoscaled tellurium synthesized by vapor transport deposition (VTD) at a temperature as low as 100 °C fully compatible with back-end-of-line processing. The resistive switching behavior of tellurium nanosheets is studied by conductive atomic force microscopy, providing valuable insights into its memristive functionality, supported by microscale device measurements. Selecting gold as the substrate material enhances the memristive behavior of nanoscaled tellurium in terms of reduced values of set voltage and energy consumption. In addition, formation of conductive paths leading to resistive switching behavior on the gold substrate is driven by gold-tellurium interface reconfiguration during the VTD process as revealed by energy electron loss spectroscopy analysis. These findings reveal the potential of nanoscaled tellurium as a versatile and scalable material for neuromorphic computing and underscore the influential role of gold electrodes in enhancing its memristive performance., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr 3 single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr 3 single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

Two-dimensional graphene and graphene-based materials are attracting increasing interest in neuromorphic computing applications by the implementation of memristive architectures that enable the closest solid-state equivalent to biological synapses and neurons. However, the state-of-the-art fabrication methodology involves routine use of high-temperature processes and multistepped chemical synthesis, often on a rigid substrate constraining the experimental exploration in the field to high-tech facilities. Here, we demonstrate the use of a one-step process using a commercial laser to fabricate laser-induced graphene (LIG) memristors directly on a flexible polyimide substrate. For the first time, a volatile resistive switching phenomenon is reported in the LIG without using any additional materials. The absence of any precursor or patterning mask greatly simplifies the process while reducing the cost and providing greater controllability. The fabricated memristors show multilevel resistance-switching characteristics with high endurance and tunable timing characteristics. The recovery time and the trigger pulse-dependent state change are shown to be highly suitable for its use as a synaptic element and in the realization of leaky-integrate and fire neuron in neuromorphic circuits.

Surface covalent modification of black phosphorus (BP) with organic polymers represents a promising strategy to enhance its stability and tailor its electronic properties. Despite this potential, developing memristive materials through suitable polymer structures, grafting pathways, and polymerization techniques remains challenging. In this study, polyaniline (PANI)-covalently grafted black phosphorus nanosheets (BPNS) are successfully prepared with redox functionalities via the in situ polymerization of aniline on the surface of 4-aminobenzene-modified BPNS. The PANI coating protects the BP from direct exposure to oxygen and water, and it endows the material with analog memristive properties, characterized by a continuously adjustable resistance within a limited voltage scan range. When subjected to a broader voltage scan, the Al/PANI-g-BPNS/ITO device demonstrates a typical bistable digital memristive behavior. The integration of both digital and analog memristive functionalities in a single device paves the way for the development of high-density, multifunctional electronic components., (© 2024 Wiley‐VCH GmbH.)

Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing., (© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

Solid-state nanopores are a key platform for single-molecule detection and analysis that allow engineering of their properties by controlling size, shape, and chemical functionalization. However, approaches relying on polymers have limits for what concerns hardness, robustness, durability, and refractive index. Nanopores made of oxides with high dielectric constant would overcome such limits and have the potential to extend the suitability of solid-state nanopores toward optoelectronic technologies. Here, we present a versatile method to fabricate three-dimensional nanopores made of different dielectric oxides with convex, straight, and concave shapes and demonstrate their functionality in a series of technologies and applications such as ionic nanochannels, ionic current rectification, memristors, and DNA sensing. Our experimental data are supported by numerical simulations that showcase the effect of different shapes and oxide materials. This approach toward robust and tunable solid-state nanopores can be extended to other 3D shapes and a variety of dielectrics.

Imine-linked covalent organic frameworks (COFs) are garnering substantial interest in resistive random-access memory, attributed to their superior crystallinity, excellent chemical and thermal stability, and modifiable molecular structures. However, the development of high-performance COF-based memristors impeded by challenges such as low conjugation degree of imine bonds and poor electron delocalization ability. Herein, we report a protonation strategy to modify the imine bonds of donor-acceptor (D-A) type COFs. This modification significantly enhances the electron delocalization capability of imine bonds, lowers the energy barriers for electron injection from electrodes, and stabilizes the conductive charge transfer state, thus markedly improving device performance. The protonated COF-BTT-BPy and COF-BTT-TAPT thin films-based memristors show remarkable device performance with a high ON/OFF current ratio of 10 5 , a low driving voltage, and outstanding endurance exceeding 600 and 1300 cycles, respectively, which is nearly twice the durability of analogous non-protonated COFs-based memristors. Notably, the protonated COF-BTT-TAPT-based memristor exhibit the highest number of cycles reported at present. This work not only unprecedentedly enhances the performance of COF-based memristors, but also provides a universal and promising approach for the molecular design and potential application of D-A type imine-linked COFs., (© 2024 Wiley-VCH GmbH.)

Brainoid computing using 2D atomic crystals and their heterostructures, by emulating the human brain's remarkable efficiency and minimal energy consumption in information processing, poses a formidable solution to the energy-efficiency and processing speed constraints inherent in the von Neumann architecture. However, conventional 2D material based heterostructures employed in brainoid devices are beset with limitations, performance uniformity, fabrication intricacies, and weak interfacial adhesion, which restrain their broader application. The introduction of novel 2D atomic-molecular heterojunctions (2DAMH), achieved through covalent functionalization of 2D materials with functional molecules, ushers in a new era for brain-like devices by providing both stability and tunability of functionalities. This review chiefly delves into the electronic attributes of 2DAMH derived from the synergy of polymer materials with 2D materials, emphasizing the most recent advancements in their utilization within memristive devices, particularly their potential in replicating the functionality of biological synapses. Despite ongoing challenges pertaining to precision in modification, scalability in production, and the refinement of underlying theories, the proliferation of innovative research is actively pursuing solutions. These endeavors illuminate the vast potential for incorporating 2DAMH within brain-inspired intelligent systems, highlighting the prospect of achieving a more efficient and energy-conserving computing paradigm., (© 2024 Wiley‐VCH GmbH.)

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb 2 S 3 ) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb 2 S 3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb 2 S 3 /Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb 2 S 3 -based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb 2 S 3 enables the Ag/Sb 2 S 3 /Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb 2 S 3 in future memory devices and optoelectronics and in shaping electronics with versatility., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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 (WS 2 ) 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 × 10 11 Jones) and fast response speed (80.2/78.3 μ s) at 950 nm. The memory device has shown a set/reset time of 51.6/73.5 μ s 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., (© 2024 IOP Publishing Ltd.)

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field., (© 2024. The Author(s).)

The growth in data generation necessitates efficient data processing technologies to address the von Neumann bottleneck in conventional computer architecture. Memory-driven computing, which integrates nonvolatile memory (NVM) devices in a 3D stack, is gaining attention, with CMOS back-end-of-line (BEOL)-compatible ferroelectric (FE) diodes being ideal due to their two-terminal design and inherently selector-free nature, facilitating high-density crossbar arrays. Here, we demonstrate BEOL-compatible, high-performance FE diodes scaled to 5, 10, and 20 nm FE Al 0.72 Sc 0.28 N/Al 0.64 Sc 0.36 N films. Through interlayer (IL) engineering, we show substantial improvements in the on/off ratios (>166 times) and rectification ratios (>176 times) in these scaled devices. These characteristics also enable 5-bit multistate operation with a stable retention. We also experimentally and theoretically demonstrate the counterintuitive result that the inclusion of an IL can lead to a decrease in the ferroelectric switching voltage of the device. An in-depth analysis into the device transport mechanisms is performed, and our compact model aligns seamlessly with the experimental results. Our results suggest the possibility of using scaled Al x Sc 1- x N FE diodes for high-performance, low-power, embedded NVM.

Organic phototransistors (OPTs), as photosensitive organic field-effect transistors (OFETs), have gained significant attention due to their pivotal roles in imaging, optical communication, and night vision. However, their performance is fundamentally limited by the Boltzmann distribution of charge carriers, which constrains the average subthreshold swing (SS ave ) to a minimum of 60 mV/decade at room temperature. In this study, an innovative one-transistor-one-memristor (1T1R) architecture is proposed to overcome the Boltzmann limit in conventional OFETs. By replacing the source electrode in an OFET with a memristor, the 1T1R device exploits the memristor's sharp resistance state transitions to achieve an ultra-low SS ave of 18 mV/decade. Consequently, the 1T1R devices demonstrate remarkable sensitivity to photo illumination, with a high specific detectivity of 3.9 × 10 9  cm W -1 Hz 1/2 , outperforming conventional OPTs (4.9 × 10 4  cm W -1 Hz 1/2 ) by more than four orders of magnitude. The 1T1R architecture presents a potentially universal solution for overcoming the detrimental effects of "Boltzmann tyranny," setting the stage for the development of ultra-low SS ave devices in various optoelectronic applications., (© 2024 Wiley‐VCH GmbH.)

In order to realize the prevailing artificial intelligence technology, memristor-implemented in-memory or neuromorphic computing is highly expected to break the bottleneck of von Neumann computers. Although high-performance memristors have been vigorously developed in labs or in industry, systematic computational investigations on memristors are seldom. Hence, it is urgent to provide theoretical or computational support for the exploration of memristor operating mechanisms or the screening of memristor materials. Here, a computational method based on the main input parameters learned from the first-principles calculations was developed to measure resistance switching of two-terminal memristors with sandwiched metal/ferroelectric semiconductor/metal architectures, which strikingly agrees with the experimental measurements. Based on our developed method, the diverse multiterminal memristors were designed to fully exploit the application of interlocked ferroelectricity of a ferroelectric semiconductor and realize their heterosynaptic plasticity, and their heterosynaptic behaviors can still be well described. Our developed method can provide a paradigm for the emulation of ferroelectric memristors and inspire subsequent computational exploration. Furthermore, our study also supplies a device optimization strategy based on the interlocked ferroelectricity and easy processing of two-dimensional van der Waals ferroelectric semiconductors, and our proposed heterosynaptic memristors still await further experimental exploration.

Quantum dots (QDs) have garnered a significant amount of attention as promising memristive materials owing to their size-dependent tunable bandgap, structural stability, and high level of applicability for neuromorphic computing. Despite these advantageous properties, the development of QD-based memristors has been hindered by challenges in understanding and adjusting the resistive switching (RS) behavior of QDs. Herein, we propose three types of InP/ZnSe/ZnS QD-based memristors to elucidate the RS mechanism, employing a thin poly(methyl methacrylate) layer. This approach not only allows us to identify which carriers (electron or hole) are trapped within the QD layer but also successfully demonstrates QD-based synaptic devices. Furthermore, to utilize the QD memristor as a synapse, long-term potentiation/depression (LTP/LTD) characteristics are measured, resulting in a low nonlinearity of LTP/LTD at 0.1/1. On the basis of the LTP/LTD characteristics, single-layer perceptron simulations were performed using the Extended Modified National Institute of Standards and Technology, verifying a maximum recognition rate of 91.46%.

Memristors are essential components of neuromorphic systems that mimic the synaptic plasticity observed in biological neurons. In this study, a novel approach employing one-dimensional covalent organic framework (1D COF) films was explored to enhance the performance of memristors. The unique structural and electronic properties of two 1D COF films (COF-4,4'-methylenedianiline (MDA) and COF-4,4'-oxydianiline (ODA)) offer advantages for multilevel resistive switching, which is a key feature in neuromorphic computing applications. By further introducing a TiO 2 layer on the COF-ODA film, a built-in electric field between the COF-TiO 2 interfaces could be generated, demonstrating the feasibility of utilizing COFs as a platform for constructing memristors with tunable resistive states. The 1D nanochannels of these COF structures contributed to the efficient modulation of electrical conductance, enabling precise control over synaptic weights in neuromorphic circuits. This study also investigated the potential of these COF-based memristors to achieve energy-efficient and high-density memory devices., (© 2024 Wiley-VCH GmbH.)

Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing., (© 2024 Wiley‐VCH GmbH.)

Memristor-based design has gained significant attention in recent years due to its potential to revolutionize various fields such as artificial intelligence, neuromorphic computing, non-volatile memory, signal processing, filtering, and radio frequency design. These emerging devices offer unique advantages such as non-volatile memory, low power consumption, and a high integration density. Their scalability and compatibility with existing fabrication processes make them an attractive option for industry adoption, paving the way for faster and more efficient architecture design. Researchers are actively exploring ways to optimize memristor technology for practical applications to harness its full potential. This includes developing novel materials and structures as well as improving the reliability and performance of memristors in various applications. This paper provides a comprehensive overview of the current advancements in memristor technology and their potential impact on the design of future electronic systems, focusing on its applications in the analog domain. By exploring the latest research and development in this field, researchers can gain valuable insights into how analog memristors can be integrated into their designs to achieve enhanced performance and efficiency. The paper delves into the fundamental principles of memristor technology, exploring its unique characteristics and advantages over traditional electronic components. It discusses the potential impact of memristors and challenges in the analog field of electronics, and highlights the progress made in their integration with existing circuitry, enabling novel functionalities and improved performance. Furthermore, it highlights ongoing research efforts to improve the performance and reliability of memristors, as well as the potential limitations and challenges that need to be addressed for widespread adoption, including variability in performance and reliability.

Acquisition of defect-free transition metal dichalcogenides (TMDs) channels with clean heterojunctions is a critical issue in the production of TMD-based functional electronic devices. Conventional approaches have transferred TMD onto a target substrate, and then apply the typical device fabrication processes. Unfortunately, those processes cause physical and chemical defects in the TMD channels. Here, a novel synthetic process of TMD thin films, named confined interfacial chalcogenization (CIC) is proposed. In the proposed synthesis, a uniform TMDlayer is created at the Au/transition metal (TM) interface by diffusion of chalcogen through the upper Au layer and the reaction of chalcogen with the underlying TM. CIC allows for ultraclean heterojunctions with the metals, synthesis of various homo- and hetero-structured TMDs, and in situ TMD channel formation in the last stage of device fabrication. The mechanism of TMD growth is revealed by the TM-accelerated chalcogen diffusion, epitaxial growth of TMD on Au(111). We demonstrated a wafer-scale TMD-based vertical memristors which exhibit excellent statistical concordance in device performance enabled by the ultraclean heterojunctions and superior uniformity in thickness. CIC proposed in this study represents a breakthrough in in TMD-based electronic device fabrication and marking a substantial step toward practical next-generation integrated electronics., (© 2024 Wiley‐VCH GmbH.)

In this study, we demonstrate the implementation of programmable threshold logics using a 32 × 32 memristor crossbar array. Thanks to forming-free characteristics obtained by the annealing process, its accurate programming characteristics are presented by a 256-level grayscale image. By simultaneous subtraction between weighted sum and threshold values with a differential pair in an opposite way, 3-input and 4-input Boolean logics are implemented in the crossbar without additional reference bias. Also, we verify a full-adder circuit and analyze its fidelity, depending on the device programming accuracy. Lastly, we successfully implement a 4-bit ripple carry adder in the crossbar and achieve reliable operations by read-based logic operations. Compared to stateful logic driven by device switching, a 4-bit ripple carry adder on a memristor crossbar array can perform more reliably in fewer steps thanks to its read-based parallel logic operation.

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm. However, the hardware and energy overhead can be significantly large, because all streaming events must be accumulated and converted into histogram data, which requires a large amount of memory such as SRAM. In this paper, to combine the spike-based operation with the normal backpropagation algorithm, memristor-CMOS hybrid circuits are proposed for implementing event-driven neural networks in hardware. The proposed hybrid circuits are composed of input neurons, synaptic crossbars, hidden/output neurons, and a neural network's controller. Firstly, the input neurons perform preprocessing for the DVS camera's events. The events are converted to histogram data using very simple memristor-based latches in the input neurons. After preprocessing the events, the converted histogram data are delivered to an ANN implemented using synaptic memristor crossbars. The memristor crossbars can perform low-power Multiply-Accumulate (MAC) calculations according to the memristor's current-voltage relationship. The hidden and output neurons can convert the crossbar's column currents to the output voltages according to the Rectified Linear Unit (ReLU) activation function. The neural network's controller adjusts the MAC calculation frequency according to the workload of the event computation. Moreover, the controller can disable the MAC calculation clock automatically to minimize unnecessary power consumption. The proposed hybrid circuits have been verified by circuit simulation for several event-based datasets such as POKER-DVS and MNIST-DVS. The circuit simulation results indicate that the neural network's performance proposed in this paper is degraded by as low as 0.5% while saving as much as 79% in power consumption for POKER-DVS. The recognition rate of the proposed scheme is lower by 0.75% compared to the conventional one, for the MNIST-DVS dataset. In spite of this little loss, the power consumption can be reduced by as much as 75% for the proposed scheme., Competing Interests: The authors declare no conflicts of interest.

Molybdenum disulfide, a two-dimensional material extensively explored for potential applications in non-von Neumann computing technologies, has garnered significant attention owing to the observed hysteresis phenomena in MoS 2 FETs. The dominant sources of hysteresis reported include charge trapping at the channel-dielectric interface and the adsorption/desorption of molecules. However, in MoS 2 FETs with different channel thicknesses, the specific nature and density of defects contributing to hysteresis remain an intriguing aspect requiring further investigation. This study delves into memristive devices with back-gate modulated channel layers based on CVD-deposited flake-based and thin-film-based MoS 2 FETs, with a few-layer (FL) and thin-film (TF) channel thickness. Analysis of current-voltage (I-V) and conductance-frequency (Gp/ω-f) measurements led to the conclusion that the elevated hysteresis observed in TF MoS 2 devices, as opposed to FL devices, stems from a substantial contribution from intrinsic defects within the channel volume, surpassing that of interface defects. This study underscores the significance of considering both intrinsic defects within the bulk and the interface defects of the channel when analyzing hysteresis in MoS 2 FETs, particularly in TF FETs. The selection between FL and TF MoS 2 devices depends on the requirements for memristive applications, considering factors such as hysteresis tolerance and scaling capabilities.

Memristors are regarded as promising candidates for breaking the problems including high off-chip memory access delays and the hash rate cost of frequent data moving induced by algorithms for data-intensive applications of existing computational systems. Recently, organic-inorganic halide perovskites (OIHPs) have been recognized as exceptionally favorable materials for memristors due to ease of preparation, excellent electrical conductivity, and structural flexibility. However, research on OIHP-based memristors focuses on modulating resistive switching (RS) performance through electric fields, resulting in difficulties in moving away from complex external circuits and wire connections. Here, a multilayer memristor has been constructed with eutectic gallium and indium (EGaIn)/ MAPbI 3 /poly(3,4-ethylenedioxythiophene): poly(4-styrenesulphonate) (PEDOT: PSS)/indium tin oxide (ITO) structure, which exhibits reproducible and reliable bipolar RS with low SET/RESET voltages, stable endurance, ultrahigh average ON/OFF ratio, and excellent retention. Importantly, based on ion migration activated by sound-driven piezoelectric effects, the device exhibits a stable acoustic response with an average ON/OFF ratio greater than 10 3 , thus realizing non-contact, multi-signal, and far-field control in RS modulation. This study provides a single-structure multifunctional memristor as an integrated architecture for sensing, data storage, and computing., (© 2024 The Authors. Advanced Science published by Wiley-VCH GmbH.)

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices., (© 2024. The Author(s).)

Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of the network is unstable due to locally enhanced Joule heating and the Gibbs-Thomson effect, which poses an obstacle to the integration of threshold switching and memory function in the same AgNW memristor. Here, fragmented AgNW networks combined with Ag nanoparticles (AgNPs) and mercapto self-assembled monolayers (SAMs) are devised to construct memristors with stable threshold switching and memory behavior. In the above design, the planar gaps between NW segments are for resistive switching, the AgNPs act as metal islands in the gaps to reduce threshold voltage ( V th ), and the SAMs suppress surface atom diffusion to avoid Oswald ripening of the AgNPs, which improves switching stability. The fragmented NW-NP/SAM memristors not only circumvent the side effects of conventional NW-stacked junctions to provide durable threshold switching at > V hold ). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.V th but also exhibit synaptic characteristics such as long-term potentiation at ultralow voltage (≪ V th ). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10 -3 , using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

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 (NiPS 3 ) 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 (>10 2 ), 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/NiPS 3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS 3 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., (© 2023 Wiley-VCH GmbH.)

Neuromorphic devices can help perform memory-heavy tasks more efficiently due to the co-localization of memory and computing. In biological systems, fast dynamics are necessary for rapid communication, while slow dynamics aid in the amplification of signals over noise and regulatory processes such as adaptation- such dual dynamics are key for neuromorphic control systems. Halide perovskites exhibit much more complex phenomena than conventional semiconductors due to their coupled ionic, electronic, and optical properties which result in modulatable drift, diffusion of ions, carriers, and radiative recombination dynamics. This is exploited to engineer a dual-emitter tandem device with the requisite dual slow-fast dynamics. Here, a perovskite-organic tandem light-emitting diode (LED) capable of modulating its emission spectrum and intensity owing to the ion-mediated recombination zone modulation between the green-emitting quasi-2D perovskite layer and the red-emitting organic layer is introduced. Frequency-dependent response and high dynamic range memory of emission intensity and spectra in a LED are demonstrated. Utilizing the emissive read-out, image contrast enhancement as a neuromorphic pre-processing step to improve pattern recognition capabilities is illustrated. As proof of concept using the device's slow-fast dynamics, an inhibition of the return mechanism is physically emulated., (© 2023 Wiley-VCH GmbH.)

Memristor with low-power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration-based memristor is expected to provide a compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, high-performance memristor based on the structure of ITO/Ni single-atoms (NiSAs/N-C)/Polyvinyl pyrrolidone (PVP)/Au is reported. By utilizing well-distributed trapping sites , small tunneling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, ultralong retention capability of 10 6  s, a low set voltage (V set ) of ≈0.7 V, a substantial ON/OFF ration of 10 3 , and low spatial variation in cycle-to-cycle (500 cycles) and device-to-device characteristics (128 devices) is demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. Finally, a memristor ratioed logic-based combinational memristor array to realize the one-bit full adder is further implemented., (© 2023 Wiley-VCH GmbH.)

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices., Methods: In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms., Results: Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage., Discussion: This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Siddique, Zhang and An.)

Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoO x (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.

Facing the era of information explosion and the advent of artificial intelligence, there is a growing demand for information technologies with huge storage capacity and efficient computer processing. However, traditional silicon-based storage and computing technology will reach their limits and cannot meet the post-Moore information storage requirements of ultrasmall size, ultrahigh density, flexibility, biocompatibility, and recyclability. As a response to these concerns, polymer-based resistive memory materials have emerged as promising candidates for next-generation information storage and neuromorphic computing applications, with the advantages of easy molecular design, volatile and non-volatile storage, flexibility, and facile fabrication. Herein, we first summarize the memory device structures, memory effects, and memory mechanisms of polymers. Then, the recent advances in polymer resistive switching materials, including single-component polymers, polymer mixtures, 2D covalent polymers, and biomacromolecules for resistive memory devices, are highlighted. Finally, the challenges and future prospects of polymer memory materials and devices are discussed. Advances in polymer-based memristors will open new avenues in the design and integration of high-performance switching devices and facilitate their application in future information technology.

The imitation of human visual memory demands the multifunctional integration of light sensors similar to the eyes, and image memory, similar to the brain. Although humans have already implemented electronic devices with visual memory functions, these devices require a combination of various components and logical circuits. However, the combination of visual perception and high-performance information storage capabilities into a single device to achieve visual memory remains challenging. In this study, inspired by the function of human visual memory, a dual-functional perovskite-based photodetector (PD) and memristor are designed to realize visual perception and memory capacities. As a PD, it realizes an ultrahigh self-powered responsivity of 276 mA W -1 , a high detectivity of 4.7 × 10 11  Jones (530 nm; light intensities, 2.34 mW cm -2 ), and a high rectification ratio of ≈100 (±2 V). As a memristor, an ultrahigh on/off ratio (≈10 5 ), an ultralow power consumption of 3 × 10 -11  W, a low setting voltage (0.15 V), and a long retention time (>7000 s) are realized. Moreover, the dual-functional device has the capacity to perceive and remember light paths and store data with good cyclic stability. This device exhibits perceptual and cyclic erasable memory functions, which provides new opportunities for mimicking human visual memory in future multifunctional applications., (© 2023 Wiley-VCH GmbH.)

Gamma-aminobutyric acid (GABA) is a crucial inhibitory neurotransmitter of the central nervous system. It modifies the signal threshold of the nociceptor, allowing it to react to external stimuli in various circumstances. Thus, GABAergic behaviors are critical characteristics of adaptive behavior in life. Here, a threshold-modulative artificial GABAergic nociceptor is reported for the first time at a Pt/Ti/Nb 2 O 5- x /Al 2 O 3- y /Pt/Ti (top to bottom) of the double charge trapping structure. The Al 2 O 3- y layer contains deep defect states that function similarly to the GABA neurotransmitter in modulating the signal threshold. Meanwhile, the Nb 2 O 5- x layer traps volatile charges and produces nociceptive behaviors. The combined dynamics of the two layers readily offer threshold-modulative GABAergic nociceptive behaviors. Based on these GABAergic behaviors, a method of implementing hot- and cold-sensitive thermoreceptors is demonstrated and shows its potential applications in advanced sensory devices., (© 2023 Wiley-VCH GmbH.)

The constant drive to achieve higher performance in deep neural networks (DNNs) has led to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in place and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due to non-linear weight updates, device variations, and low-precision. In this work, a mixed-precision training scheme is experimentally implemented to mitigate these effects using a bulk-switching memristor-based CIM module. Low-precision CIM modules are used to accelerate the expensive VMM operations, with high-precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system-onchip of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and verifies that CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software-trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Memristors, known for their adjustable and non-volatile resistance, offer a promising avenue for emulating synapses. However, achieving pulse frequency-dependent synaptic plasticity in memristors or memristive systems necessitates further exploration. In this study, we present a novel approach to modulate the conductance of a memristor in a capacitor-memristor circuit by finely tuning the frequency of input pulses. Our experimental results demonstrate that these phenomena align with the long-term depression (LTD) and long-term potentiation (LTP) observed in synapses, which are induced by the frequency of action potentials. Additionally, we successfully implement a Hebbian-like learning mechanism in a simple circuit that connects a pair of memristors to a capacitor, resulting in observed associative memory formation and forgetting processes. Our findings highlight the potential of capacitor-memristor circuits in faithfully replicating the frequency-dependent behavior of synapses, thereby offering a valuable contribution to the development of brain-inspired neural networks.

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 ∼10 4 , 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.

The ever-increasing demand for data storage and neuromorphic computing calls for innovative, high-density solutions, such as resistive random-access memory (RRAM). However, the integration of resistive switching and rectification at the nanoscale remains a formidable challenge. In this study, we introduce a bistable [2]catenane-based supramolecular junction that simultaneously functions as a resistive switch and a diode. All supramolecular junctions are highly stable and reproducible over thousands of resistive switching cycles, because the nano-confinement of two mechanically interlocked rings can stabilize the radical states of pyridinium moieties under ambient conditions. The successful realization of supramolecular junctions in functionality with a thickness of approximately 2 nm presents a promising avenue for the development of molecule-scale based RRAM for a better solution to high density and energy efficiency., (© 2023 Wiley-VCH GmbH.)

Memristors possess non-volatile memory, adjusting their electrical resistance to the current that flows through them and allowing switching between high and low conducting states. This technology could find applications in fields such as IT, consumer electronics, computing, sensors, and medicine. In this paper, we report successful electrodeposition of thin-film materials consisting of copper tungstate and copper molybdate (CuWO 4 and Cu 3 Mo 2 O 9 ), which showed notable memristive properties. Material characterisation was performed with techniques such as XRD, XPS, and SEM. The electrodeposited materials exhibited the ability to switch between low and high resistive states during varied cyclic scans and short-term impulses. The retention time of these switched states was also explored. Using these materials, the effects seen in biological systems, specifically spike timing-dependent plasticity, were simulated, being based on analogue operation of the memristors to achieve multiple conductivity states. Bio-inspired simulations performed directly on the material could possibly offer energy and time savings for classical computations. Memristors could be crucial for the advancement of high-efficiency, low-energy neuromorphic electronic devices and technologies in the future.

Oxide-based memristors composed of Ag/porous SiO x /Si stacks are fabricated using different etching time durations between 0 and 90 s, and the memristive properties are analyzed in the relative humidity (RH) range of 30-60%. The combination of humidity and porous structure provides binding sites to control silver filament formation with a confined nanoscale channel. The memristive properties of devices show high on/off ratios up to 10 8 and a dispersion coefficient of 0.1% of the high resistance state ( C HRS memristors is investigated, and the mechanism leading to the synergistic effects between the porous structure and environmental humidity is elucidated. The artificial neural network constructed theoretically shows that the recognition rate increases from 60.9 to 85.29% in the RH range of 30-60%. The results and theoretical understanding provide insights into the design and optimization of oxide-based memristors in neuromorphic computing applications. x memristors is investigated, and the mechanism leading to the synergistic effects between the porous structure and environmental humidity is elucidated. The artificial neural network constructed theoretically shows that the recognition rate increases from 60.9 to 85.29% in the RH range of 30-60%. The results and theoretical understanding provide insights into the design and optimization of oxide-based memristors in neuromorphic computing applications.

Electronic transitions in molecular-circuit elements hinge on complex interactions between molecules and ions, offering a multidimensional parameter space to embed, access, and optimize material functionalities for target-specific applications. This opportunity is not cultivated in molecular memristors because their low-temperature charge transport, which is a route to decipher molecular many-body interactions, is unexplored. To address this, robust, temperature-resilient molecular memristors based on a Ru complex of an azo aromatic ligand are designed, and current-voltage sweep measurements from room temperature down to 2 K with different cooling protocols are performed. By freezing out or activating different components of supramolecular dynamics, the local Coulombic interactions between the molecules and counterions that affect the electronic transport can be controlled. Operating conditions are designed where functionalities spanning bipolar, unipolar, nonvolatile, and volatile memristors with sharp as well as gradual analog transitions are captured within a single device. A mathematical design space evolves, thereof comprising 36 tuneable parameters in which all possible steady-state functional variations in a memristor characteristic can be attainable. This enables a deterministic design route to engineer neuromorphic devices with unprecedented control over the transformation characteristics governing their functional flexibility and tunability., (© 2022 Wiley-VCH GmbH.)

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing., (© 2022 Wiley-VCH GmbH.)

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO 2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures., (© 2022 Wiley-VCH GmbH.)

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Information-processing devices are the core components of modern electronics. Integrating them into textiles is the indispensable demand for electronic textiles to form close-loop functional systems. Memristors with crossbar configuration are regarded as promising building blocks to design woven information-processing devices that seamlessly unify with textiles. However, the memristors always suffer from severe temporal and spatial variations due to the random growth of conductive filaments during filamentary switching processes. Here, inspired by the ion nanochannels across synaptic membranes, a highly reliable textile-type memristor made of Pt/CuZnS memristive fiber with aligned nanochannels, showing small set voltage variation (<5.6%) under ultralow set voltage (≈0.089 V), high on/off ratio (≈10 6 ), and low power consumption (0.1 nW), is reported. Experimental evidence indicate that nanochannels with abundant active S defects can anchor silver ions and confine their migrations to form orderly and efficient conductive filaments. Such memristive performances enable the resultant textile-type memristor array to have high device-to-device uniformity and process complex physiological data like brainwave signals with high recognition accuracy (95%). The textile-type memristor arrays are mechanically durable to withstand hundreds of bending and sliding deformations, and seamlessly unified with sensing, power-supplying, and displaying textiles/fibers to form all-textile integrated electronic systems for new generation human-machine interactions., (© 2023 Wiley-VCH GmbH.)

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 (MoS 2 ) and aluminum oxide (Al 2 O 3 ) 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 10 4 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al 2 O 3 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., (© 2023 Wiley-VCH GmbH.)

Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Being renowned for operating with visible-light pulses and electrical signals, optoelectronic memristive synaptic devices have excellent potential for neuromorphic computing systems and artificial visual information processing. Here, a flexible back-end-of-line-compatible optoelectronic memristor based on a solution-processable black phosphorus/HfO x bilayer with excellent synaptic features, toward biomimetic retinas is presented. The device shows highly stable synaptic features such as long-term potentiation (LTP) and long-term depression (LTD) for repetitive 1000 epochs, having 400 conductance pulses, each. The device presents advanced synaptic features in terms of long-term memory (LTM)/short term memory (STM), as well as learning-forgetting-relearning when visible light is induced on it. These advanced synaptic features can improve the information processing abilities for neuromorphic applications. Interestingly, the STM can be converted into LTM by adjusting the intensity of light and illumination time. Using the light-induced characteristics of the device, a 6 × 6 synaptic array is developed to exhibit possible use in artificial visual perception. Moreover, the devices are flexed using a silicon back-etching process. The resulting flexible devices demonstrate stable synaptic features when bent down to 1 cm radius. These multifunctional features in a single memristive cell make it highly suitable for optoelectronic memory storage, neuromorphic computing, and artificial visual perception applications., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)