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TiN/Ti/HfO2/Pt resistive switching devices have been fabricated, measured, and modeled. After programming the devices in the low resistance state, the current–voltage characteristic below the reset switching voltage was measured at different temperatures (from 90 to 350 K). A weak but complex temperature dependence was obtained for several voltage regimes. These memristors belong to a wider set known as valence change memories, whose conductance is determined by the formation of conductive filaments (CFs) linked to a high density of oxygen vacancies in a dielectric sandwiched between two metal electrodes. This usually leads to ohmic conduction in the low resistance state. However, a non-linear current dependence has been also observed in the measured devices, in addition to symmetric current–voltage curves for positive and negative biases in the 0–0.6 V voltage range. Three different thermal dependences have been considered for explaining the whole set of experimental data. Two of them are linked to ohmic filamentary conduction; the CF shows a conductivity enhancement due to thermally activated mechanisms at low temperatures; on the contrary, a CF conductivity degradation is observed at the higher temperatures. Finally, an additional slightly higher value for the non-linear current component as the temperature rises has also been taken into account. A semiempirical compact model has been implemented including these conduction mechanisms and their corresponding temperature dependences, the device has been simulated in LT-Spice and the experimental currents have been correctly reproduced. [ABSTRACT FROM AUTHOR]

We report for the first time MoS2/CNT hybrid nanostructures for memristor applications on flexible and bio-degradable cellulose paper. In our approach, we varied two different weight percentages (10% and 20%) of CNT's in MoS2 to improve the MoS2 conductivity and investigate the memristor device characteristics. The device with 10% CNT shows a low V SET voltage of 2.5 V, which is comparatively small for planar devices geometries. The device exhibits a long data retention time and cyclic current–voltage stability of ∼104 s and 102 cycles, making it a potential candidate in flexible painted electronics. Along with good electrical performance, it also demonstrates a high mechanical stability for 1000 bending cycles. The conduction mechanism in the MoS2-CNT hybrid structure is corroborated by percolation and defect-induced filament formation. Additionally, the device displays synaptic plasticity performance, simulating potentiation and depression processes. Furthermore, such flexible and biodegradable cellulose-based paper electronics may pave the way to address the environmental pollution caused by electronic waste in the near future. [ABSTRACT FROM AUTHOR]

Printed electronics including large‐area sensing, wearables, and bioelectronic systems are often limited to simple circuits and hence it remains a major challenge to efficiently store data and perform computational tasks. Memristors can be considered as ideal candidates for both purposes. Herein, an inkjet‐printed memristor is demonstrated, which can serve as a digital information storage device, or as an artificial synapse for neuromorphic circuits. This is achieved by suitable manipulation of the ion species in the active layer of the device. For digital‐type memristor operation resistive switching is dominated by cation movement after an initial electroforming step. It allows the device to be utilized as non‐volatile digital memristor, which offers high endurance over 12 672 switching cycles and high uniformity at low operating voltages. To use the device as an electroforming‐free, interface‐based, analog‐type memristor, anion migration is exploited which leads to volatile resistive switching. An important figure of merits such as short‐term plasticity with close to biological synapse timescales is demonstrated, for facilitation (10–177 ms), augmentation (10s), and potentiation (35 s). Furthermore, the device is thoroughly studied regarding its metaplasticity for memory formation. Last but not least, the inkjet‐printed artificial synapse shows non‐linear signal integration and low‐frequency filtering capabilities, which renders it a good candidate for neuromorphic computing architectures, such as reservoir computing. [ABSTRACT FROM AUTHOR]

Numerous synaptic devices have been explored for the next generation of energy‐efficient computing techniques. Among them, optoelectronic synaptic devices based on semiconductor/ferroelectric heterostructures have received a lot of attention lately due to their amazing parallelism, efficiency, and fault tolerance properties. However, polarizing the ferroelectric layer or gating the dielectric layer is necessary to achieve tunable synaptic functions, which generally causes an increase in energy consumption and complex manufacturing processes. Here, a simple and efficient method is demonstrated to develop a tunable optoelectronic synaptic device based on a single ferroelectric semiconductor, BiFeO3‐BaTiO3 (BF‐BT). Multi‐essential synaptic functions including short‐term plasticity, paired‐pulse facilitation, and long‐term plasticity are all satisfactorily replicated by the memristor device. More significantly, light‐controllable synaptic behaviors are realized by altering the ferroelectric polarization state of BF‐BT. Synaptic devices' relaxation characteristics enable simulation of the effects of positive/negative emotions on learning and forgetting processes. This study highlights the potential of the ferroelectric semiconductor memristor in constructing the efficient optoelectronic synapses for future neuromorphic electronics with the ability to learn and sense optical information. [ABSTRACT FROM AUTHOR]

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 TiO2 layer on the COF‐ODA film, a built‐in electric field between the COF‐TiO2 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. [ABSTRACT FROM AUTHOR]

The past decade has seen a growing trend toward utilizing (quasi) van der Waals growth for the heterogeneous integration of various materials for advanced electronics. In this work, pulsed‐laser deposition is used to grow HfO2 thin films on graphene/SiO2/Si. As graphene is easily damaged under standard oxide‐film deposition conditions, the process needs to be adjusted to minimize the oxidation and the collision‐induced damage. A systematic study is conducted in order to identify the crucial deposition parameters for diminishing the defect concentration in the graphene interlayer. For evaluating the quality of graphene, it is mainly relied on data obtained from Raman spectroscopy, using approaches beyond the Tuinstra‐Koenig relation. The results show that the defects are mainly a consequence of the high kinetic energy of the plasma‐plume particles. Using a relatively high Ar process pressure, a sufficiently low defect concentration is ensured, without compromising the quality of the HfO2 thin film. This enabled us to successfully prepare memristive devices with a filamentary type of switching, utilizing the graphene layer as a bottom electrode. The findings of this study can be easily transferred to other systems for the development of oxide electronic devices. [ABSTRACT FROM AUTHOR]

Memristors with metal/insulator/metal (MIM) double terminal structure have garnered significant attention as potential candidates for next-generation non-volatile memory devices. In this study, the Pt/Ni 0.5 Zn 0.5 Fe 2 O 4 /Pt (Pt/NZFO/Pt) composite films were fabricated and their resistive switching behaviors were modulated by varying the oxygen pressure. It was confirmed that the oxygen pressure influenced the films microstructure, morphology and oxygen vacancy concentration. The resistive switching characteristics of the Pt/NZFO/Pt composite films, including adjustable set/reset voltages, a broad memory window, and robust retention properties, were also regulated by the corresponding oxygen pressure. It is speculated that the resistive switching mechanism is due to the creation and disruption of conducting paths caused by the presence of oxygen vacancies and synergistic interplay of barrier modulation by oxygen vacancies and ferroelectric polarization. Consequently, the Pt/NZFO/Pt composite films, optimized at an oxygen pressure of 0.1 Pa, emerged as suitable candidates for memristor applications. [ABSTRACT FROM AUTHOR]

The presence of turning points (stationary points like maxima and minima) in the Parameter-versus-state map (PSM) curve of a memristor can be beneficial in several memristive uses like multi-level logic design, multi-bit memories, and chaos generation circuits. Surprisingly the memristors with these types of PSM curves exhibit multiple crossing points in the transient input v-i contours. In this paper, the memductance model has been derived for such non-linear memristors exhibiting multi-cross-over characteristics. In the presented framework, the coefficients-related condition for the multi-turning point PSM curve has been derived, a necessary condition for the multi-crossing v-i contour. The condition related to the memristor operating parameters has also been reported taking both input signal and initial states into consideration. To realise the derived memductance model corresponding to the three cross-over memristive behaviour, an OTA-based memristor emulator has been developed. Unlike some existing fractional order non-symmetric multi-crossing memristor emulators, the proposed emulator circuit realises an integer order memristor function operating at moderate frequencies. The realised emulator uses only four OTAs and three grounded passive circuit elements with no external multiplier. For the CMOS implementation of employed OTA, the simulation results are obtained to verify the three pinch-off behaviour. [ABSTRACT FROM AUTHOR]

This issue discusses the asymptotic synchronization and the exponential synchronization for memristor-based quaternion-valued neural networks under the time-varying delays. Some criteria for synchronization of the memristor-based quaternion-valued neural networks are given by exploiting the set-valued theory, the differential inclusion theory, some analytic techniques, as well as constructing novel controllers, It is worth noting that the synchronization problem about the memristor-based quaternion-valued neural networks were studied by the direct analysis method in this paper. Finally, the main theoretical results were verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Summary: With its low power consumption and small size, the memristor has shown great potential for improving data storage density and computing efficiency. Compared to the dual‐port memristor, greater attention should be paid to researching gate‐tunable memristor for image processing to improve the processing speed and reduce hardware resource consumption. Developing gate‐tunable memristor emulators is highly attractive given the immaturity of current fabrication of the gate‐tunable memristor. This work proposes a digital gate‐tunable memristor emulator based on Raspberry Pi, which addresses the non‐reconfigurability and inflexibility issues of the analog emulators. The proposed emulator can match the behavior of different memristor devices by regulating the gate voltage parameter. Additionally, it can operate at a maximum frequency of 500 MHz. To test the functionality of the proposed emulator, a digital implementation of the memristive circuit for motion detection is designed and verified experimentally. Experiments demonstrate that when moving object detection is performed on a 640 × 350 pixel video stream, low power consumption of 53 mW and a delay of 3.52 μs can be achieved. [ABSTRACT FROM AUTHOR]

The creation and optimisation of memristor models with different topologies and physical mechanisms have received increasing attention in recent years. Memristors, known for their unique resistive switching mechanism, have garnered significant interest as promising components for next-generation computing. However, to effectively design and test memristor-based circuits, it is crucial to have a mathematical representation of the experimentally determined current-voltage characteristics of memristors. This paper proposes a model and conducts an analysis that offers insights into memristor technology, beginning with its characteristics and extending to simulations involving various parameters. The proposed model and its dependency on temperature are implemented using MATLAB. The model captures changes in current characteristics concerning the fundamental voltage without using any window functions. Thus, it accurately represents the variation in memristance with temperature, contributing to a more precise and observed modelling approach. [ABSTRACT FROM AUTHOR]

Memory devices with sensitivity, selectivity, and operation voltage towards the gases are rarely reported for artificial olfactory sensors. Additionally, there are no reports available on the atomistic aspects of artificial olfactory sensors. This study reports an atomistic simulation of monoclinic‐ZrO2 (m‐ZrO2). The impact of external electric field on the formation of the oxygen vacancies are evaluated by considering the different directions of electric field. Furthermore, it is conducted nudged elastic band calculations which showed a decrease in the migration barrier energy with an increase in the electric field for all considered directions. Moreover, it is simulated the memristor device (Ta/m‐ZrO2/Pt) and investigated the impact of oxygen vacancies on electrical conductivity by considering oxygen vacancies at different locations in m‐ZrO2. Finally, it is evaluated the possibility of using the m‐ZrO2 based memristor device for an artificial olfactory sensor by studying the gas sensing properties of the (111) surface of m‐ZrO2. The pristine structure exhibits low sensitivity towards toxic molecules (CO2, CO, NH3, and NO2), while the sensing performance is significantly enhanced on the oxygen vacancy rich surface. These atomistic simulation results provide an atomic level understanding of the Ta/m‐ZrO2/Pt device and suggest the potential for it to be use as an artificial olfactory sensor. [ABSTRACT FROM AUTHOR]

The learning accuracy of neuromorphic computing that mimics the biological brain, is affected by the conductance‐modulation characteristics of an artificial synapse. In ferroelectric‐based devices, these characteristics are implemented using a distribution of polarization values. Therefore, the distribution in a ferroelectric thin film with various external voltage signals is investigate. As polarization switching proceeds with voltage pulse, the domains of the switched polarization become larger. In ferroelectric‐gate field effect transistors, the channel layer assumed to lie beneath the ferroelectrics experiences a local conductance change, according to the polarization distribution of the ferroelectric layer. It is found that small clusters with high conductivity become large clusters in the channel layer as the polarization switching proceeds. When the additional pulses are applied, the high conductive regions eventually connect (i.e., percolate) in the channel layer and the conductance of the layer is greatly increased. Adjusting the height of the applied voltage can slow down or speed up this phenomenon. Also, the nanosecond voltage pulses are employed and the width of the conductive pathway is adjusted. It enables to fine‐tune the conductance of the channel layer. It demonstrates that conductance modulation is optimized with an appropriate voltage pulse train pattern. [ABSTRACT FROM AUTHOR]

Metal halide perovskites (MHPs) exhibit electronic and ionic characteristics suitable for memristors. However, polycrystalline thin film perovskite memristors often suffer from reliability issues due to grain boundaries, while bulk single‐crystal perovskite memristors struggle to achieve high LRS/HRS ratios. In this study, a single crystal memristive device utilizing a wide bandgap perovskite is introduced, MAPbBr3, in a high surface/thickness configuration. This thin single crystal overcomes these challenges, exhibiting a remarkable LRS/HRS ratio of up to 50 and endurance of 103 cycles, representing one of the highest reported values to date. This exceptional stability enables to analyze the electroforming process and LRS through impedance spectroscopy, providing insights into the underlying operational mechanism. As far as it is known, this is the first reported thin single‐crystal MHP memristor device and the first time that the electroforming process is recorded through impedance spectroscopy. This device's outstanding stability and performance position it as a promising candidate for high‐density data storage and neuromorphic applications. [ABSTRACT FROM AUTHOR]

Physically unclonable functions (PUFs) are advanced physical security measures that offer fundamental, unclonable appraisals of physical objects, providing an effective defense against hardware vulnerability breaches. They function as unique digital hardware fingerprints. This study discusses previous methods adopted for improving hardware security via PUF technology, with a specific focus on PUF circuits implemented on FPGA boards. Hardware security is assumed to be enhanced by adding a memristor to the ring oscillator PUF circuit and implementing these authentication architectures on FPGA boards. Additionally, this study explores methods for improving the main performance metrics for FPGA-based memristive-ring oscillator PUFs, including uniqueness, uniformity, and reliability. The study was founded on many scientific studies selected according to specific criteria. This study aims to assess and contrast these studies to achieve substantial enhancements in the security of devices on the basis of the obtained results. Upon comparing the findings, it was revealed that the proposed techniques, which provide flexibility and adaptability in dealing with memristive-PUF circuits to improve security services, displayed a distinct enhancement in security performance compared with other research that did not include any references to memristors. As an essential part of the authentication architecture, performance metrics involving memristor technology are verified in this study, with a uniqueness of 48.57%, uniformity of 51.43%, and bit-aliasing of 51.43%. These outcomes demonstrate the validation of memristor-based physical unclonable functions (M-PUF) against encryption and verification within a certified key exchange and tests. [ABSTRACT FROM AUTHOR]

Memristor-based logic circuits are gaining a lot of attention due to the potential for high logic density hardware and novel in-memory computing applications. Readily available methods for fabricating of memristor logic structures that are suitable for integration with conventional computer hardware are a growing need. This work presents a direct laser writing process capable of rapidly fabricating memristor logic circuits by laser irradiation of metal salt precursor solutions. Planar memristor patterns are fabricated, and their I–V response is characterized. Boolean logic gates are fabricated from planar memristor pairs that exhibit low programming voltages and rapid switching. Cu/Cu2O/Cu and Ag/Cu2O/Cu memristors are also fabricated in crossbar arrays, showing the ability to be programmed to multiple resistance states through ultrashort voltage pulses. The devices also show the potential to have high endurance and nonvolatile resistance state retention. [ABSTRACT FROM AUTHOR]

Perovskite‐based memristors with tunable nonvolatile states are developed to mimic the synaptic interconnects of biological nervous systems and map neuromorphic computing networks to integrated circuits. To emulate the plasticity of synaptic structures, memristors with robust multilevel resistive states are fabricated in this work using high‐density polycrystalline MAPbCl3 nanowires (NWs) array that vertically integrated using solution method. In particular, the fabricated memristors exhibit both short‐ and long‐term plasticity and traits akin to biological synapses. A fabricated memristor device is precisely programmed to 18 resistive states and each state exhibits stable data retention of more than 100 000 s. Furthermore, a matrix processing unit using a 4‐by‐4 memristor array is fabricated as the hardware core of an encoder–decoder artificial neural network to demonstrate high accuracy and reliable in‐image font conversion. The resistive states of the 16 memristors are precisely programmed to the corresponding resistance values for specific synaptic weights of the artificial‐neural‐network‐trained offline. In addition, experimental characterization and first‐principles simulations attribute the continuous programmability and high reliability features of the memristors to the confinement mechanisms of the horizontal grain‐boundary structure in polycrystalline perovskite NWs. [ABSTRACT FROM AUTHOR]

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain. [ABSTRACT FROM AUTHOR]

The prevalence of artificial intelligence applications using artificial neural network architectures for functions such as natural language processing, text prediction, object detection, speech, and image recognition has significantly increased in today's world. The computational functions performed by artificial neural networks in classical applications require intensive and large‐scale data movement between memory and processing units. Various software and hardware efforts are being made to perform these operations more efficiently. Despite these efforts, latency in data traffic and the substantial amount of energy consumed in data processing emerge as bottleneck disadvantages of the Von Neumann architecture. To overcome this bottleneck problem, it is necessary to develop hardware units specific to artificial intelligence applications. For this purpose, neuro‐inspired computing chips are believed to provide an effective approach by designing and integrating a set of features inspired by neurobiological systems at the hardware level to address the problems arising in artificial intelligence applications. The most notable among these approaches is memristor‐based neuromorphic computing systems. Memristors are seen as promising devices for hardware‐level improvement in terms of speed and energy because they possess non‐volatile memory and exhibit analog behavior. They enable effective storage and processing of synaptic weights, offering solutions for hardware‐level development. Taking into account these advantages of memristors, this study examines the research conducted on artificial neural networks and hardware that can directly perform deep learning functions and mimic the biological brain, which is different from classical systems in today's context. [ABSTRACT FROM AUTHOR]

In this study, all-inorganic two-dimensional (2D) perovskite Cs2Pb(SCN)2Br2 was employed in a thin-film vertical structure prototype memristor. The device consisted of a Cs2Pb(SCN)2Br2 film prepared through solution approach, sandwiched between an Ag electrode and a TiO2/FTO substrate bottom electrode. Two types of resistive switching (RS) behaviors were observed within a single device at different temperatures. At room temperature, the dominant control mechanism was the interface Schottky barrier, whereas at higher temperatures, the primary driving force shifted to the conductive channel. The device has an on/off ratio exceeding 103 under the interface control mechanism. The migration of mobile bromine vacancies within the Cs2Pb(SCN)2Br2 film, whose concentration was enhanced by the infiltration and reaction of the Ag active electrode within the Cs2Pb(SCN)2Br2 film, is proposed to be the root cause for both types of RS characteristics. These findings offer insights into the potential application of 2D Cs2Pb(SCN)2Br2 perovskite in RS memory devices. [ABSTRACT FROM AUTHOR]

Memristors based on 2D semiconductors hold great promise due to their atomic‐level thickness and tunable optoelectronic properties. However, a significant challenge lies in suppressing the large off‐state current, which leads to additional standby power consumption. Here, a simple and versatile method is presented to address this issue by introducing a thin h‐BN interlayer between 2D semiconductors and the electrodes. The thickness of the h‐BN interlayer serves as a pivotal parameter for modulating the interfacial Schottky barrier, thereby influencing the off‐state current level. This fabricated graphene/α‐In2Se3/h‐BN/Cr‐Au memristor, forming a van der Waals heterostructure, exhibits unipolar resistive switching behavior. Remarkably, the memristor incorporating an 8 nm h‐BN interlayer showcases an ultralow off‐state current of 4.2 × 10−13 A, five orders of magnitude lower than that without the h‐BN interlayer. It also achieves a current switching on/off ratio of up to 109 and realizes 32 distinct resistance states, enabling robust multi‐bit memory capabilities. Excellent stability and durability are maintained due to the self‐encapsulation of the h‐BN interlayer. Furthermore, this method is also applicable to memristors built on HfS2, WS2, and WSe2, highlighting its broad potential for technological applications. [ABSTRACT FROM AUTHOR]

2D materials have attracted attention in the field of neuromorphic computing applications, demonstrating the potential for their use in low‐power synaptic devices at the atomic scale. However, synthetic 2D materials contain randomly distributed intrinsic defects and exhibit a stochasitc forming process, which results in variability of switching voltages, times, and stat resistances, as well as poor synaptic plasticity. Here, this work reports the wafer‐scale synthesis of highly polycrystalline semiconducting 2H‐phase molybdenum ditelluride (2H‐MoTe2) and its use for fabricating crossbar arrays of memristors. The 2H‐MoTe2 films contain small grains (≈30 nm) separated by vertically aligned grain boundaries (GBs). These aligned GBs provide confined diffusion paths for metal ions filtration (from the electrodes), resulting in reliable resistive switching (RS) due to conductive filament confinement. As a result, the polycrystalline 2H‐MoTe2 memristors shows improvement in the RS uniformity and stable multilevel resistance states, small cycle‐to‐cycle variation (<8.3%), high yield (>83.7%), and long retention times (>104 s). Finally, 2H‐MoTe2 memristors show linear analog synaptic plasticity under more than 2500 repeatable pulses and a simulation‐based learning accuracy of 96.05% for image classification, which is the first analog synapse behavior reported for 2D MoTe2 based memristors. [ABSTRACT FROM AUTHOR]

The increased demand of high‐performance computing systems has exposed the inherent limitations of the current state‐of‐the‐art von Neumann architecture. Therefore, developing alternate computing primitives that can offer faster computing speed with low energy expenditure is critical. In this context, while several non‐volatile memory (NVM) devices such as synaptic transistors, spintronic devices, phase change memory (PCM), and memristors have been demonstrated in the past, their two‐terminal nature necessitates additional peripheral elements that increase area and energy overhead. Recently, a new multiterminal device prototype known as a memtransistor has shown tremendous potential to overcome these limitations through exceptional control of the gate electrostatics as enabled by 2D channel materials. In this perspective, a brief overview of recent developments in 2D‐memtransistor devices is provided, including their fundamental operational mechanisms and the role of defects in enabling multiple NVM states and optical photoresponse. An overview of their implementation in the context of neuromorphic, probabilistic, information security, and edge‐sensing primitives is also provided. Finally, a futuristic perspective is provided looking toward their successful large‐scale technological integration. [ABSTRACT FROM AUTHOR]

This study presents a method to enhance data processing by integrating a unidirectional analogue artificial neuromorphic memristor device with a piezoelectric nanogenerator, taking inspiration from biological information processing. A self‐powered unidirectional neuromorphic resistive memory device is proposed, comprising an ITO/ZnO/Yb2O3/Au structure combined with a high‐sensitivity piezoelectric nanogenerator (PENG) ITO/ZnO/Al. The memristor device is operated at a voltage sweep of ±4 V with a low operating current in a range of 1.4 µA. The filament formation is studied using a conductive mode atomic force microscope. The integration enables the creation of a self‐powered artificial sensing system that converts mechanical stimuli from the PENG into electrical signals, which are subsequently processed by analogue unidirectional neuromorphic device to mimic the functionality of a neuron without requiring additional circuitry. This emulation encompasses crucial functions such as potentiation, depression, and synaptic plasticity. Furthermore, this study highlights the potential for hardware implementations of neural networks with a weight change of memristor device with nonlinearity (NL) of potentiation and depression of 1.94 and 0.89, respectively, with an accuracy of 93%. The outcomes of this research contribute to the progress of next‐generation low‐power, self‐powered unidirectional neuromorphic perception networks with correlated learning and trainable memory capabilities. [ABSTRACT FROM AUTHOR]

Janus 2D materials have drawn substantial attention recently owing to its extraordinary interface properties and promising applications in optoelectronic devices. However, the scalable fabrication of high‐quality Janus 2D materials is still one of the main obstacles to hinder its implementation in the industry. Herein, a new method (called "chemical breakdown") is developed to obtain large‐area uniform Janus graphene oxide (J‐GO) films with high‐quality. Moreover, the first application of J‐GO in the field of memristive devices is presented for neuromorphic computing. In particular, crossbar arrays of Ag/J‐GO/Au memristive devices that exhibit threshold resistive switching (RS) with enhanced performance are fabricated, e.g., low leakage current (≈10−12 A), low operation voltage (≈0.3 V), high endurance (>12,000 cycles), and electro‐synaptic plasticity. This work provides a novel strategy to obtain large‐area, continuous and uniform Janus 2D films, and proposes a new application for Janus 2D materials in a hot topic (i.e., neuromorphic computing) within the field of solid‐state microelectronics. [ABSTRACT FROM AUTHOR]

The 2D insulating material hexagonal boron nitride (h‐BN) has attracted much attention as the active medium in memristive devices due to its favorable physical properties, among others, a wide bandgap that enables a large switching window. Metal filament formation is frequently suggested for h‐BN devices as the resistive switching (RS) mechanism, usually supported by highly specialized methods like conductive atomic force microscopy (C‐AFM) or transmission electron microscopy (TEM). Here, the switching of multilayer hexagonal boron nitride (h‐BN) threshold memristors with two nickel (Ni) electrodes is investigated through their current conduction mechanisms. Both the high and the low resistance states are analyzed through temperature‐dependent current–voltage measurements. The formation and retraction of nickel filaments along boron defects in the h‐BN film as the resistive switching mechanism is proposed. The electrical data are corroborated with TEM analyses to establish temperature‐dependent current–voltage measurements as a valuable tool for the analysis of resistive switching phenomena in memristors made of 2D materials. The memristors exhibit a wide and tunable current operation range and low stand‐by currents, in line with the state of the art in h‐BN‐based threshold switches, a low cycle‐to‐cycle variability of 5%, and a large On/Off ratio of 107. [ABSTRACT FROM AUTHOR]

Recently, phase change chalcogenides, such as monochalcogenides, are reported as switching materials for conduction‐bridge‐based memristors. However, the switching mechanism focused on the formation and rupture of an Ag filament during the SET and RESET, neglecting the contributions of the phase change phenomenon and the distribution and re‐distribution of germanium vacancies defects. The different thicknesses of germanium telluride (GeTe)‐based Ag/GeTe/Pt devices are investigated and the effectiveness of phase loops and defect loops future application in neuromorphic computing are explored. GeTe‐based devices with thicknesses of 70, 100, and 200 nm, are fabricated and their electrical characteristics are investigated. Highly reproducible phase change and defect‐based characteristics for a 100 nm‐thick GeTe device are obtained. However, 70 and 200 nm‐thick devices are unfavorable for the reliable memory characteristics. Upon further analysis of the Ag/GeTe/Pt device with 100 nm of GeTe, it is discovered that a state‐of‐the‐art dependency of phase loops and defect loops exists on the starting and stopping voltage sweeps applied on the top Ag electrode. These findings allow for a deeper understanding of the switching mechanism of monochalcogenide‐based conduction‐bridge memristors. [ABSTRACT FROM AUTHOR]

2D memristors have demonstrated attractive resistive switching characteristics recently but also suffer from the reliability issue, which limits practical applications. Previous efforts on 2D memristors have primarily focused on exploring new material systems, while damage from the metallization step remains a practical concern for the reliability of 2D memristors. Here, the impact of metallization conditions and the thickness of MoS2 films on the reliability and other device metrics of MoS2‐based memristors is carefully studied. The statistical electrical measurements show that the reliability can be improved to 92% for yield and improved by ≈16× for average DC cycling endurance in the devices by reducing the top electrode (TE) deposition rate and increasing the thickness of MoS2 films. Intriguing convergence of switching voltages and resistance ratio is revealed by the statistical analysis of experimental switching cycles. An "effective switching layer" model compatible with both monolayer and few‐layer MoS2, is proposed to understand the reliability improvement related to the optimization of fabrication configuration and the convergence of switching metrics. The Monte Carlo simulations help illustrate the underlying physics of endurance failure associated with cluster formation and provide additional insight into endurance improvement with device fabrication optimization. [ABSTRACT FROM AUTHOR]

Memristor‐based electronic memory have recently started commercialization, although its market size is small (~0.5%). Multiple studies claim their potential for hardware implementation of artificial neural networks, advanced data encryption, and high‐frequency switches for 5G/6G communication. Application aside, the performance and reliability of memristors need to be improved to increase their market size and fit technology standards. Multiple groups propose novel nano‐materials beyond phase‐change, metal‐oxides, and magnetic materials as resistive switching medium (e.g., two‐dimensional, nanowires, perovskites). However, most studies use characterization setups that are blind to critical phenomena in understanding charge transport across the devices. Here an advanced setup with high temporal resolution is used to analyze current noise, dielectric breakdown growth, and ambipolar resistive switching in memristors based on multilayer hexagonal boron nitride (h‐BN), one of the most promising novel nano‐materials for memristive applications. The random telegraph noise in pristine memristors and its evolution as the devices degrade, covering ~7 orders of magnitude in current with consistent observation, is studied. Additionally, an ambipolar switching regime with very low resistance down to 50Ω and its connection with a telegraph behavior with high/low current ratios >100, linked to a thermally‐driven disruption of a metallic nanofilament, is shown. [ABSTRACT FROM AUTHOR]

The multi-terminal modular multi-level converter-based high voltage direct current (MMC-HVDC) grid with short circuit protection equipment (SCPE) is so complex that it is difficult to estimate its fault current and analyze the performance of SCPE by conventional time-domain numerical calculation method, it meets three big obstacles. This paper has made significant progress in overcoming these obstacles. 1). By applying the modern electrical circuit theory, a systematic formulation of the differential equation set for fault current calculation is developed to avoid a lot of complex and cumbersome matrix manual calculations. 2). A novel Y-Delta transformation in the s-domain is proposed to develop an eliminating virtual node approach for a complex MMC-HVDC grid, including the ring, radial, and hybrid topologies. 3). It is difficult to solve the equivalent circuit of MMC-HVDC grid with SCPE since SCPE is a timevariable-nonlinear circuit. A canonical voltage source model of SCPE is established to transform the time-variable-nonlinear circuit into a piecewise linear circuit. Based on the three significant progresses, a DC fault current fast-computing method of MMC-HVDC grid with SCPE is put forward to deal with all kinds of MMC-HVDC grids with several kinds of SCPEs. Then, the performance of several kinds of SCPE is analyzed and compared by this method. Consequently, the proposed DC fault current fast-computing method is a new powerful tool to estimate the fault current of MMC-HVDC grid and analyze the performance of SCPE. [ABSTRACT FROM AUTHOR]

Memristors have recently become powerful competitors toward artificial synapses and neuromorphic computation, arising from their structural and electrical similarity to biological synapses and neurons. From the diversity of materials, numerous organic and inorganic materials have proven to exhibit great potential in the application of memristors. Herein, this work focuses on a class of memristors based on organic frameworks (OFs) materials, and pay attention to the most advanced experimental demonstrations. First, the typical device structures and memristive switching mechanisms are introduced. Second, the latest progress of OFs‐based memristors is comprehensively summarized, including metal‐organic frameworks (MOFs), covalent organic frameworks (COFs), and hydrogen‐bonded organic frameworks (HOFs), as well as their applications in data storage, artificial synapses, and neuromorphic devices. Finally, the future challenges and prospects of OFs‐based memristors are deeply discussed. [ABSTRACT FROM AUTHOR]

Covalent organic polymers (COPs) have emerged as a promising class of materials for memory devices due to their unique electronic properties and potential for tunability. This review highlights recent advances in the field of COPs‐based thin films for memory applications, with a focus on the synthesis and characterization of COP thin films, their electronic properties, and their performance as memory devices. The potential of COPs‐based thin films as flexible memory devices is also discussed. Overall, the recent progress in COPs‐based thin films for memory applications suggests that these materials may have a significant impact on the development of next‐generation memory technologies. [ABSTRACT FROM AUTHOR]

Maze-solving is a classical mathematical task, and is recently analogously achieved using various eccentric media and devices, such as living tissues, chemotaxis, and memristors. Plasma generated in a labyrinth of narrow channels can also play a role as a route finder to the exit. In this study, we experimentally observe the function of maze-route findings in a plasma system based on a mixed discharge scheme of direct-current (DC) volume mode and alternative-current (AC) surface dielectric-barrier discharge, and computationally generalize this function in a reinforcement-learning model. In our plasma system, we install two electrodes at the entry and the exit in a square lattice configuration of narrow channels whose cross section is 1×1 mm2 with the total length around ten centimeters. Visible emissions in low-pressure Ar gas are observed after plasma ignition, and the plasma starting from a given entry location reaches the exit as the discharge voltage increases, whose route converging level is quantified by Shannon entropy. A similar short-path route is reproduced in a reinforcement-learning model in which electric potentials through the discharge voltage is replaced by rewards with positive and negative sign or polarity. The model is not rigorous numerical representation of plasma simulation, but it shares common points with the experiments along with a rough sketch of underlying processes (charges in experiments and rewards in modelling). This finding indicates that a plasma-channel network works in an analog computing function similar to a reinforcement-learning algorithm slightly modified in this study. [ABSTRACT FROM AUTHOR]

Typically, changes in the conductive properties of resistive random‐access memory elements happen due to the movement of ions in an ultra‐thin dielectric layer under the influence of an electric field. In the case of oxides, they often talk about the movement of oxygen vacancies and the formation/destruction of conducting filaments. Such processes are often described by dynamic systems in which the state parameter corresponds to the position of the boundary between regions with low and high concentrations of oxygen vacancies. In this case, the dependence of the current (or resistance) on the state parameter and the voltage applied to the element can be quite complex. In this regard, the work proposes an approach that uses neural networks to approximate the dependence of the current on the state parameter and voltage. Thus, a hybrid model is obtained in which the state parameter is determined using a dynamic system that takes into account the basic physical characteristics of the elements, and the model is finely tuned to the experimental data at the neural network level. A hybrid model for a memristor based on hafnium oxide (HfO2), as well as on nanocomposite (Co–Fe–B)m(LiNbO3)100−m, has been successfully constructed. [ABSTRACT FROM AUTHOR]

Neuromorphic speech recognition systems that use spiking neural networks (SNNs) and memristors are progressing in hardware development. The conventional manual preprocessing of audio signals is shifting toward event-based recognition with convolutional SNNs. Despite achieving high accuracy in classification, the efficient extraction of spatiotemporal features from audio events continues to be a substantial challenge. In this study, we introduce dynamic time-surface neurons (DTSNs) using volatile memristors featuring an adjustable temporal kernel decay, enabled by series-connected transistors with an Au/LiCoO2/Au configuration. DTSNs act as feature descriptors, enhancing the spatiotemporal feature extraction from event audio data. A two-layer SNN classifier, fully connected and incorporating a 1T1R nonvolatile memristor array, is trained to recognize the spatiotemporal features of the audio data. Our findings show classification accuracies of up to 95.91%, substantial improvements in computational efficiency, and increased noise resilience, confirming the promise of our memristor-based speech recognition system for practical applications. [ABSTRACT FROM AUTHOR]

Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high‐dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)‐enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip‐level edge computing, although promising, still faces challenges such as energy‐efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward‐looking perspective on the future of machine‐learning‐enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field. [ABSTRACT FROM AUTHOR]

A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform. [ABSTRACT FROM AUTHOR]

The memristor is the building block of neuromorphic computing. We report a new type of nanofluidic memristor based on the principle of elastic strain on polymer nanopores. With nanoparticles absorbed at the wall of a single conical polymer nanopore, we find a pinched hysteresis of the current within a scanning frequency range of 0.01–0.1 Hz, switching to a diode below 0.01 Hz and a resistor above 0.1 Hz. We attribute the current hysteresis to the elastic strain at the tip side of the nanopore, caused by electrical force on the particles adsorbed at the inner wall surface. Our simulation and analytical equations match well with experimental results, with a phase diagram for predicting the system transitions. We demonstrate the plasticity of our nanofluidic memristor to be similar to a biological synapse. Our findings pave a new way for ionic neuromorphic computing using nanofluidic memristors. [ABSTRACT FROM AUTHOR]

This paper proposes a new four-dimensional hyper-chaotic system capable of generating multi-wing chaotic attractors by introducing active magnetron memristors, multi-segmented square functions and trigonometric functions. The dynamical properties of this new hyper-chaotic system, such as equilibrium point, dissipation, Lyapunov exponential spectrum, bifurcation diagram and Poincaré cross-section and attraction basin, are analyzed theoretically and simulated numerically, and the complexity of this system with different parameters is analyzed. It is observed that this hyper-chaotic system has periodic, chaotic and hyper-chaotic variations with an infinite number of equilibria and coexisting attractors under different parameter conditions. The circuit simulation was performed using Multisim and the results obtained were consistent with the numerical analysis of the dynamics, and the chaotic circuit system is designed by FPGA to verify the realizability of the system. Finally, an image encryption algorithm is designed in conjunction with the DNA algorithm to enable a new system chaotic sequence for image encryption. The results show that the hyper-chaotic system has rich dynamical behavior and has high-security performance when applied to image encryption with strong chaotic key and plaintext sensitivity and large key space in image encryption. [ABSTRACT FROM AUTHOR]

Liénard Oscillator circuit has numerous variations. Nowadays, due to the developments of semiconductor technology, such an oscillator can be made using various semiconductor circuit elements. In this study, it has been shown that a Liénard Oscillator can also be made using a Schottky diode bridge and a JFET based nonlinear resistor. First, the new Liénard Oscillator topology is given, then, the dynamic model of the circuit is derived, and the simulations of the circuit are made. The currents, voltages and limit cycle of the Liénard Oscillator circuit are obtained with simulations using LTspice circuit analysis program. The simulations have confirmed that the circuit operates as a Liénard Oscillator. [ABSTRACT FROM AUTHOR]

Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses. [ABSTRACT FROM AUTHOR]

Nanofluidic diodes and ionic solutions find application in electrochemical circuits for information processing, controlled release, and signal conversion in hybrid devices. Here, we describe a physical model that accounts for the memory effects observed in conical nanopores in terms of the driving signal and ionic solution characteristics. The concepts invoked describe the device operation on the basis of the electrical interaction between the pore surface charges and the nanoconfined ionic solution. The physical insights provided can explain the experimental dependence of the nanofluidic tunability on the amplitude and frequency of the driving signal, the ionic concentration, and the solution pH. The model should also be useful for the design of electrochemical circuits based on ionic conduction in asymmetric memristors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solution processed cost-effective hydrothermal approach for integration of Zinc oxide and cadmium oxide (C 15 ZO) nano-hybrid composite for memristor devices as an artificial synapse for brain-inspired computer systems. • High R ON /R OFF ratio of (∼105), and excellent reliability with endurance greater than ∼ 104 cycles. • Remarkable switching characteristics, exciting bio-synaptic functions including long term potentiation (LTP), long term depression (LTD) and paired-pulse facilitation (PPF) have been investigated. • Memristor devices demonstrated high identification accuracy up to 92.6%. Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C 15 ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/−0.2) voltage, a high R ON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures. [ABSTRACT FROM AUTHOR]

The thermometer digital-to-analog converter (DAC) is a distinctive architecture that plays a vital role in converting digital data into corresponding analog signals, the thermometer DAC employs a resistor network where each bit of the digital input corresponds to a unique resistor. It has notable drawbacks that need careful consideration. As the resolution of the DAC increases, the number of required current sources grows exponentially, leading to complex and demanding circuitry. This can escalate power consumption and occupy significant chip area, which is a critical concern in integrated circuit design. Furthermore, the current mismatch between the multiple current sources. Therefore, integrating memristors into DACs paves the way for more compact and efficient designs, reducing system complexity and enhancing reliability. The Voltage ThrEshold Adaptive Memristor (VTEAM) model of memristor is validated by using Virtuoso. In addition, a digital-to-analog converter based on memristor technology is implemented, taking advantage of the memristor's compact size, minimal power usage, and a voltage threshold that is relatively low. The DAC design being proposed is based on a core DAC cell that consists of two memristors connected in opposing orientations. [ABSTRACT FROM AUTHOR]

Data-intensive application tasks have always fueled research and development towards more powerful computing systems. In this context, the recently proposed framework of hyper-dimensional computing (HDC) is rapidly emerging to open new opportunities for the development of systems that perform cognitive tasks in hardware. The highly memory-centric nature of HDC was the key motivation for the in-memory computing hardware implementation approaches explored recently where memristive devices were used to locally implement logic operations. In this work, we explore using memristive devices to implement one of the fundamental modules of an HDC system, the "associative memory" (AM). We designed and simulated an HDC system in MATLAB software using a behavioral model for memristive devices and explored the performance of the HDC system in image classification tasks, using different AM implementations to enrich the representation of the image classes in the AM when they included up to 25% of noise. The simulation results also explored the impact of nonidealities of memristive devices and demonstrate the critical system design aspects to consider in such an implementation approach. [ABSTRACT FROM AUTHOR]

Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed technique, SHA3 is implemented in a set of perpendicular crossbar arrays structured to facilitate logic implementation and circular bit rotation (Rho operation), which is perhaps the most complex operation in SHA3 when carried out in memristor arrays. The Rho operation itself is implemented with CMOS multiplexers (MUXs). The proposed accelerator is standby power-free and circumvents the memory access bottleneck in conventional computers. In addition, our design obscures the intermediate values from the I/O interface and outperforms the state-of-the-art memristor-based designs in terms of size and energy. Demonstrating the memristor implementation of SHA3 provides an impetus for utilizing memristors in information security applications. [ABSTRACT FROM AUTHOR]

In this work, a grounded charge-controlled memcapacitor emulator circuit based on a Voltage Difference Current Conveyor (VDCC) is presented. The proposed circuit is constructed with only one VDCC element and four MOSFETs. It has a transistor-based structure without the use of any passive components. The absence of any passive components makes the circuit eliminating the need for complicated analog components. The suggested circuit does away with the necessity for a mutator, which eliminates the need for an additional separate memristor emulator. The analog multiplier circuit is also not used. Additionally, the designed memcapacitor circuit allows for independent electronic control of both the fixed and variable components, adding to its flexibility and adaptability. To demonstrate the accuracy of the suggested circuit, a SPICE simulation was run using a VDCC constructed with 0.18 μm TSMC CMOS transistors. [ABSTRACT FROM AUTHOR]