Search

Electrically induced resistive switching (RS) effects have been proposed as the basis for future non-volatile memories. In this work, 9 nm-thick BiFeO3 (BFO) epitaxial thin films were deposited on (001)-oriented SrTiO3 substrates by pulsed laser deposition and their resistive switching (RS) behaviors were investigated. A large resistive switching with ON/OFF ratio of ∼106 is observed, surpassing the performance of most resistive random access memories ever reported. The conducting filament is proposed to dominate the RS behavior in the positive voltage region, while the modulation of ferroelectric polarization is suggested to play a significant role in the negative voltage region. Our study significantly deepens the understanding of the physical origin of RS and could provide a reference for designing high-performance memories and memristors based on ultrathin ferroelectric films. [ABSTRACT FROM AUTHOR]

Due to their high similarity to biological ion channels, low power consumption, small footprint, and the fact that they do not require reset circuits, threshold switching memristors have been intensively studied for simulating neurons in neuromorphic chips. Switching speed is one of the key challenges which limit the application of threshold switching memristors in chips. In this study, Ta2O5 threshold switching memristors with high switching speeds were prepared by doping with silver. The results show that 14 wt. % Ag doped Ta2O5 threshold switching memristors exhibit excellent bi-directional threshold switching performance, featuring fast switching speeds (<20 ns, <18 ns), low leakage currents (<10 pA), and high switching ratio (>107). According to the field nucleation theory, the rapid switching speed can be attributed to the low nucleation energy (0.26 eV) of silver within the Ta2O5 matrix, which is achieved by incorporating 14 wt. % Ag during the doping process. Based on Pspice, a LIF (leaky integrate-and-fire) neuron based on the silver nanoparticles doped Ta2O5 threshold switching memristors is built, and its firing function has been simulated. The results show that the LIF neuron with a short switching time is able to excite pulse spiking with high frequencies. These results demonstrated that the silver nanoparticles doped Ta2O5-based threshold switching memristors hold significant potential for constructing high-speed artificial neural networks. [ABSTRACT FROM AUTHOR]

This study systematically investigates analog switching and neuromorphic characteristics in a ZnO-based memristor by varying the anodic top electrode (TE) materials [indium tin oxide (ITO), Ti, and Ta]. Compared with the TE materials (Ti and Ta), memristive devices with TEs made of ITO exhibit dual volatile and nonvolatile switching behavior and multistate switching characteristics assessed based on reset-stop voltage and current compliance (ICC) responses. The polycrystalline structure of the ZnO functional layer sandwiched between ITO electrodes was confirmed by high-resolution transmission electron microscopy analysis. The current transport mechanism in the ZnO-based memristor was dominated by Schottky emission, with the Schottky barrier height modulated from 0.26 to 0.4 V by varying the reset-stop voltage under different ICC conditions. The long-term potentiation and long-term depression synaptic characteristics were successfully mimicked by modulating the pulse amplitudes. Furthermore, a 90.84% accuracy was achieved using a convolutional neural network architecture for Modified National Institute of Standards and Technology pattern categorization, as demonstrated by the confusion matrix. The results demonstrated that the ITO/ZnO/ITO/Si memristor device holds promise for high-performance electronic applications and effective ITO electrode modeling. [ABSTRACT FROM AUTHOR]

Cu2AgBiI6 films were prepared by a one-step spin coating method, and flexible memristors with an Ag/PMMA/Cu2AgBiI6/ITO structure were constructed. The devices showed a bipolar resistive switching behavior with low switching voltage, which is beneficial for reducing energy consumption. Furthermore, this study found that the device exhibits an endurance of about 900 cycles, a higher ON/OFF ratio of over 103, a long retention time (∼104 s), and high stabilities against mechanical stress. Remarkably, the present flexible memristor displayed extraordinary flexibility and stability, with no significant change for the resistive switching behavior even at various bending angles or after undergoing 900 bending cycles. This study establishes that the lead-free halide perovskite Cu2AgBiI6 can be used for the resistive random-access memory of flexible electronics. [ABSTRACT FROM AUTHOR]

Efficient data processing is heavily reliant on prioritizing specific stimuli and categorizing incoming information. Within human biological systems, dorsal root ganglions (particularly nociceptors situated in the skin) perform a pivotal role in detecting external stimuli. These neurons send warnings to our brain, priming it to anticipate potential harm and prevent injury. In this study, we explore the potential of using a ferroelectric memristor device structured as a metal–ferroelectric–insulator–semiconductor as an artificial nociceptor. The aim of this device is to electrically receive external damage and interpret signals of danger. The TiN/HfAlOx (HAO)/HfSiOx (HSO)/n+ Si configuration of this device replicates the key functions of a biological nociceptor. The emulation includes crucial aspects, such as threshold reactivity, relaxation, no adaptation, and sensitization phenomena known as "allodynia" and "hyperalgesia." Moreover, we propose establishing a connection between nociceptors and synapses by training the Hebbian learning rule. This involves exposing the device to injurious stimuli and using this experience to enhance its responsiveness, replicating synaptic plasticity. [ABSTRACT FROM AUTHOR]

Artificial synapses based on memristors are used in emulating the synaptic plasticity behavior of a human brain. Here, we have proposed a transparent memristor based on aluminum zinc oxide (AZO) on a flexible substrate—polyethylene naphthalate. We have analyzed the elemental composition of the gadget subjected to the optimized flow rate of Ar/O2 = 2/1 by x-ray photoelectron spectroscopy. The prepared AZO/ZnO/indium-doped tin oxide memristor exhibits a bipolar switching behavior with Vset/Vreset of 1.4/−2.0 V. The results reflect an acceptable endurance of >500 cycles and retention of 104 s. The optimized device shows an improvement in the non-linearity of potentiation—2.31/depression—3.05 and has more than 25 cycles of stability. The transparency is checked using a UV-visible spectrophotometer showing 90% transparency in the visible region making the device suitable for applications in invisible electronics. Our results reflect that the proposed device can be used as a transparent electrode in making artificial synapses for neuromorphic applications. [ABSTRACT FROM AUTHOR]

Previously, we demonstrated hysteretic and persistent changes of resistivity in two-terminal electronic devices based on charge trapping and detrapping at immobile metastable defects [Yin et al., Phys. Rev. Appl. 15, 014014 (2021)]; we termed these defect-level switching (DLS) devices. DLS devices feature all-electronic resistive switching and thus are volatile because of the "voltage-time" dilemma. However, the dynamics of volatile resistive switches may be valuable for emerging applications such as selectors in crosspoint memory and neuromorphic computing concepts. To design circuits using these volatile resistive switches, accurate modeling is essential. In this work, we develop an accurate and analytical model to describe the switching in DLS devices, based on the established theories of point defect metastability in Cu(In,Ga)Se2 (CIGS) and II–VI semiconductors. The analytical nature of our model allows for time-efficient simulations of dynamical behavior of DLS devices. We model the time durations of SET and RESET programming pulses, which can be exponentially shortened with respect to the pulse amplitude. We also demonstrate the concept of inverse design: given desired resistance states, the width and amplitude of the programming signal can be chosen accordingly. [ABSTRACT FROM AUTHOR]

We like and need Information and Communication Technologies (ICTs) for data processing. This is measurable in the exponential growth of data processed by ICT, e.g., ICT for cryptocurrency mining and search engines. So far, the energy demand for computing technology has increased by a factor of 1.38 every 10 years due to the exponentially increasing use of ICT systems as computing devices. Energy consumption of ICT systems is expected to rise from 1500 TWh (8% of global electricity consumption) in 2010 to 5700 TWh (14% of global electricity consumption) in 2030 [A. S. G. Andrae, Eng. Appl. Sci. Lett. 3, 19–31 (2020)]. A large part of this energy is required for the continuous data transfer between separated memory and processor units, which constitute the main components of ICT computing devices in von-Neumann architecture. This, at the same time, massively slows down the computing power of ICT systems in von-Neumann architecture. In addition, due to the increasing complexity of artificial intelligence (AI) compute algorithms, since 2010, the AI training compute time demand for computing technology has increased tenfold every year, for example, from 1 × 10−6 to 1 × 10+4 Petaflops/day in the period from 2010 to 2020 [J. Wang, see https://ark-invest.com/articles/analyst-research/ai-training/ for information about the cost to train an AI interference system (2020)]. It has been theoretically predicted that ICT systems in the neuromorphic computer architecture will circumvent all of this through the use of merged memory and processor units. However, the core hardware element for this has not yet been realized so far. In this work, we discuss the perspectives for non-volatile resistive switches with hysteretic memristance as the core hardware element for merged memory and processor units in neuromorphic computers. [ABSTRACT FROM AUTHOR]

GaOx devices have been extensively explored for applications such as power devices and solar blind detectors, based on their wide bandgap. In this study, we investigated the synaptic properties of the amorphous gallium oxide (a-GaOx)- based memristor with a W/WOx/a-GaOx/ITO structure, in which a-GaOx are deposited by RF magnetic sputtering at ambient temperature. The structure and components of a-GaOx are characterized by XRD, XPS, SEM, and EDS. The electrical test indicates that W/WOx/a-GaOx is ohmic due to the thin WOx layer with a high concentration of oxygen vacancies. Consequently, the synaptic characteristics of the W/WOx/a-GaOx/ITO memristor depend on both the a-GaOx layer itself and the a-GaOx/ITO junction. The fitting results indicate that the a-GaOx/ITO junction is Schottky with unidirectional conductive properties. However, the elevated defect density results in a larger current for the reverse-biased a-GaOx/ITO junction. Moreover, adjusting the thickness of a-GaOx allows the device to achieve almost symmetrical forward and reverse currents. We have successfully observed typical synaptic characteristics in W/WOx/a-GaOx/ITO when stimulated by consecutive spike signals. Clearly, through careful design considerations regarding the structure and parameters, we have realized superior synaptic performance in a-GaOx-based memristors. This achievement shows that amorphous GaOx has great potential applications in neuromorphic computation chips for artificial intelligence or the Internet of Things in the future. [ABSTRACT FROM AUTHOR]

Deposition of Mo S 2 on Au(111) alters the electronic properties of Mo S 2 . In this study, we investigate the free-standing MoS 2 monolayer and the MoS 2 /Au(111) heterostructure, with and without strain, as well as defects of interest in memristive and neuromorphic applications. We focus on the so-called atomristor devices based on monolayer materials that achieve resistive switching characteristics with the adsorption and desorption of metal adatoms. Our study confirms that the formation of midgap states is the primary mechanism behind the resistive switching. Our results show that strain lowers the adsorption/desorption energies of Au+defect structures of interest, leading to more favorable switching energies, but simultaneously reduces the switching ratio between states of differing conductivities. The presence of the Au(111) substrate additionally introduces non-uniform amounts of strain and charge transfer to the MoS 2 monolayer. We propose that the induced strain contributes to the experimentally observed n - to p -type transition and Ohmic to Schottky transition in the MoS 2 monolayer. The charge transfer leads to a permanent polarization at the interface, which can be tuned by strain. Our study has important implications on the role of the electrode as being a source of the observed variability in memristive devices and as an additional degree of freedom for tuning the switching characteristics of the memristor device. [ABSTRACT FROM AUTHOR]

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (−0.39 ± 0.08 V), concentrated Set/Reset voltage distribution (<0.08/0.15 V), and long retention time (>104 s) but also continuously modulable conductance by both electric (different width/interval/amplitude) and light (470–808 nm with different intensity) stimulus. These advantages make the device good electrically and optically simulated synaptic functions, including excitatory and inhibitory, paired-pulsed facilitation, short-/long-term plasticity, spike-timing-dependent plasticity, and "memory-forgetting" behavior. Significantly, decimal arithmetic calculation (addition, subtraction, and commutative law) is realized based on the linear conductance regulation, and high precision pattern recognition (>88%) is well achieved with an artificial neural network constructed by 5 × 5 × 4 memristor array. Predictably, such kesterite-based optoelectronic memristors can greatly open the possibility of realizing multi-functional neuromorphic systems. [ABSTRACT FROM AUTHOR]

Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of precisely tuning the resistive switching behavior of Au/BiFeO 3 /Pt/Ti/SiO 2 /Si memristors through controlled modulation of laser energy density during pulsed laser deposition (PLD). By systematically reducing the laser energy density within the fabrication process, notable alterations in the properties of the BiFeO 3 (BFO) thin film are observed. As the laser energy density decreases, the grain size in the BFO film and the thickness of the film decrease. Furthermore, we obtain the minute structural variations in response to the diverse laser energy densities employed during the deposition process. Energy-dispersive x-ray spectroscopy analysis is employed to investigate the distribution of Ti 4 + ions within the BFO thin film. The reduction in the grain size and film thickness, along with the prominent nucleation of specifically oriented grains, and the diffusion of Ti 4 + ions, lead to the BFO memristor fabricated with a lower laser energy density having more grain boundaries and a shortened conduction path (grain boundary) in the thickness direction. Consequently, the enhanced movement of oxygen vacancies facilitates their preferential accumulation along the grain boundaries within the BFO layer, resulting in an augmented on/off ratio, rectification factor, and set current in the devices. Overall, our findings explain the significant influence of laser energy density in PLD on the microstructure and electrical properties of BFO thin films. Particularly, the lower energy densities are employed to improve electrical characteristics. This research not only enhances our fundamental understanding but also provides valuable insights into optimizing BFO memristors for reliable, robust, and practical applications. [ABSTRACT FROM AUTHOR]

Two-terminal structure memristors are the most promising electronic devices that could play a significant role in artificial intelligence applications of the next generation and the post-Moore era. In this work, we fabricated the memristive device by depositing a heterojunction WOx/TiOy functional layer onto an indium tin oxide substrate using magnetron sputtering. The Ag/WOx/TiOy/ITO device exhibits improved memory behavior of bipolar resistive switching (RS) nonvolatile compared to TiOy-based single-layer memristors, enabling it to meet high-density information storage requirements. Moreover, our device exhibited the coexistence of the negative differential resistance effect and the behavior of the RS memory. Through a comprehensive analysis of conductivity on the curve of current–voltage (I–V), a physical model based on the mechanism of space charge-limited current, ohmic conduction, and Schottky emission was suggested to explain the behavior device RS memory. This study's findings demonstrate that including a heterojunction bilayer WOx/TiOy as a functional layer can significantly improve the performance of memristive devices. This advancement expands the potential application of ferroelectric metallic oxide heterojunctions within the field of memristors. [ABSTRACT FROM AUTHOR]

Summary: Memristors exhibit potential applications in neuromorphic computing, because of their nanoscale and low power. Non‐volatile passive memristors usually behave as electronic synapses, while volatile locally active memristors can be used to construct artificial neurons. In this paper, we apply an Nb2O5 locally active memristor with the parasitic capacitance as a LIF neuron and analyze the possibility of generating spiking oscillations by the neuron through small signal equivalent circuits and the Hopf bifurcation method. By combining Nb2O5 memristive neurons with the voltage‐controlled non‐volatile memristive synapses, we construct an unsupervised learning network and classify 5 × 3 letter images and 5 × 5 number images. In particular, before building the hardware circuit, we predict the training time, recognition time, and recognition accuracy of the pattern recognition network through theoretical analysis, which guides the actual circuit experiment. Specifically, the training time of the network is related to the synaptic memristor resistance change rate, the recognition time of the network is related to the oscillation period of the Nb2O5 memristor, and whether the network can work properly is related to the parameters of Nb2O5 memristor and NMOS. The LTspice simulation results manifest that the proposed circuit can recognize different patterns and can be applied to the neural morphological system of pattern recognition. [ABSTRACT FROM AUTHOR]

Chain-scaling fractional-order memristors (fracmemristors) refers to the concept of implementing them in circuits. Despite its advantages, a few issues that require urgent attention remain, including the effect of input signals on its instantaneous valid frequency range, failure to find the corresponding time-domain electrical characteristic expression of the fracmemristor, and the use of several memristor emulators in circuit schematics. Accordingly, a simple circuit for implementing a chain-scaling fracmemristor (CSF) within a fixed valid frequency range is proposed in this paper. First, a CSF circuit configuration with a fixed valid frequency range is described. Subsequently, a simple circuit schematic of an incremental/decremental CSF is presented, and the corresponding time-domain electrical characteristic expressions of the fracmemristor are obtained. Finally, rich theoretical analysis results are obtained along with the CSF circuit implementation. The accuracy of the theoretical analysis is verified experimentally. In this study, we developed a method to achieve a CSF with a fixed valid frequency range through the replacement of all the capacitors with memcapacitors, which also facilitated the design of circuit schematics. [ABSTRACT FROM AUTHOR]

Brain-inspired neuromorphic sensory devices play a crucial role in addressing the limitations of von Neumann systems in contemporary computing. Currently, synaptic devices rely on memristors and thin-film transistors, requiring the establishment of a read voltage. A built-in electric field exists within the p–n junction, enabling the operation of zero-read-voltage synaptic devices. In this study, we propose an artificial synapse utilizing a ZnO diode. Typical rectification curves characterize the formation of ZnO diodes. ZnO diodes demonstrate distinct synaptic properties, including paired-pulse facilitation, paired-pulse depression, long-term potentiation, and long-term depression modulations, with a read voltage of 0 V. An artificial neural network is constructed to simulate recognition tasks using MNIST and Fashion-MNIST databases, achieving test accuracy values of 92.36% and 76.71%, respectively. This research will pave the way for advancing zero-read-voltage artificial synaptic diodes for neural network computing. [ABSTRACT FROM AUTHOR]

Light‐based modulation on resistive switching has opened a new route for development of the devices for advanced applications like integrated switching with memory functionality, neuromorphic computing, and humanoid robotics. This article presents the wafer‐scalable process to fabricate titanium dioxide (TiO2)/porous silicon (P‐Si)‐based heterostructures for memristor applications. The resistive switching performance of the memristor is analyzed using electrical/optical stimulation. The fabricated devices offer enhanced performance metrics during optical stimulation when compared to electrical stimulation. The memristor device offers the paired pulse facilitation (PPF) index of ≈519% (during optical stimulation), which is 3.4 times of the electrical stimulation. Further, the stability and reliability measurements are performed at different temperatures especially (room temperature to 348 K) under 450 nm illumination. The testing results prove that the developed TiO2/P‐Si‐based memristors are suitable for the next‐generation computation systems development. [ABSTRACT FROM AUTHOR]

Mobile Ru ions in oxide media have been reported as a novel species that offer extremely low switching currents for memristors. However, their bi‐stable resistive‐switching (RS) and low‐switching currents dynamics have not been quantitatively unveiled. Here, the bi‐stable RS mechanism via in‐depth field‐induced atomic migration and chemical bonding state studies is elucidated, showing that the RS of the Ru‐based hybrid memristor (RHM) is possible via the simultaneously controlled hybrid Ru cation and oxygen anion. Additionally, the Ru ion mobility is quantitatively obtained via atomic moving distance and switching time measurements, demonstrating that the lower Ru ion mobility, compared to other conventional mobile species in oxide media, can be the origin of the low‐switching currents. It is found that the current conduction mechanism of the low‐resistance‐state in RHMs has temperature‐range‐dependencies. The direct tunneling conduction mechanism is dominant in relatively low temperatures; however, the ionic transport and thermally activated hopping conduction mechanism govern the current flow in high temperatures. Owing to the low Ru ion mobility, the RHM exhibits highly linear synaptic plasticity with a low‐conductance regime, showing outstanding energy efficiency compared to other memristors in image recognition tasks. These findings can contribute to improving the feasibility of hyper‐scale synaptic cores consisting of RHMs. [ABSTRACT FROM AUTHOR]

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor's memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The "learning-forgetting-relearning" characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%. [ABSTRACT FROM AUTHOR]

The rapid development of machine vision has put forward higher requirements for image perception and visual learning systems. However, limited by the von Neumann bottleneck, traditional artificial vision systems rely on complex optical and circuit designs, and the separate distribution of sensors, memory, and computing modules results in high energy consumption and high latency. Memristor‐based reservoir computing (RC) system with integrated sensing and memory functions provides a solution to effectively improve the computational efficiency of artificial vision networks. Here, a complete artificial visual recognition system based on in‐sensor RC is developed by combining the excellent optoelectronic synaptic properties of 2D WS2 memristor array. Through effectively regulating the conductance of memristors by light or electrical stimulation, image information is converted into high‐dimensional information in the reservoir layer, and the classification can be completed by simple training, significantly reducing the training complexity and cost consumption. With this system, an effective recognition of hand‐written numbers with an accuracy of 88.3% is demonstrated, and a 100% recognition of traffic signals of different colors is achieved. The proposed in‐sensor RC system advances the development and application of artificial vision recognition, paving the way for efficient machine learning and neuromorphic vision systems. [ABSTRACT FROM AUTHOR]

In the context of human active response to exponentially increasing data volumes, memristors have emerged as a focal point in systematic problem‐solving approaches. Particularly, organic memristors, characterized by excellent scalability, flexibility, and 3D stacking capabilities, have shown its tremendous potential in innovative electronic applications. However, the modulation of molecular assembly and interface interaction at the electrode surface poses a challenging task, despite their crucial role in determining the memristive performance. Herein, a collaborative method involving hydrophobic molecular design and interface engineering is proposed to generate high‐quality nanofilms with optimal photoelectric and electrochemical properties. By subjecting the bottom electrode to O2 plasma treatment and chemical modification using a hydrophobic ionic liquid poly(diallyldimethylammonium) bis(trifluoromethanesulfonyl)imide (PTFSI), the hydrophobic compound DN demonstrates exceptional affinity and stronger intermolecular interactions with the electrode surface. Consequently, the two‐terminal device array comprising Al/DN@PTFSI/ITO exhibits robust non‐volatile write‐once‐read‐many times (WORM) memory behavior, resulting in a tripling production yield from 30% to 92% along with a lower threshold voltage of 1.5 V, higher ON/OFF current ratio of 103 and excellent stability. This study presents a versatile approach to optimize the film assembly and material/electrode interface, thereby accelerating the development of organic memristors toward future applications. [ABSTRACT FROM AUTHOR]

Memristors have emerged as a promising candidate to mimic the human behavior and thus unlocking the potential for bio‐inspired computing advancement. However, these devices operate at a voltages which are still far from the energy‐efficient biological counterpart, which uses an action potential of 50–120 mV to process the information. Here, a diffusive memristor is reported from synthetic WSe2 fabricated in Ag/WSe2/Au vertical device geometry. The devices operate at bio‐voltages of 40–80 mV with Ion/Ioff ratio of 106 and steep switching turn ON and OFF slopes of 0.77 and 0.88 mV per decade, respectively. The power consumption in standby mode and power per set transition are found to be 10 fW and 64 pW, respectively. Further, the diffusive memristors are utilized to emulate the nociceptor, a special receptor for sensory neurons that selectively responds to noxious stimuli. Nociceptor in turn imparts a warning signal to the central nervous system which then triggers the motor response to take precautionary actions to prevent the body from injury. The key features of a nociceptor including “threshold”, “relaxation”, “no‐adaptation” and “sensitization” are demonstrated using artificial nociceptors. These illustrations imply the feasibility of developing low‐power diffusive memristors for bio‐inspired computing, humanoid robots, and electronic skins. [ABSTRACT FROM AUTHOR]

In the era of big data, the necessity for in-memory computing has become increasingly pressing, rendering memristors a crucial component in next-generation computing architectures. The self-rectifying memristor (SRM), in particular, has emerged as a promising solution to mitigate the sneak path current issue in crossbar architectures. In this work, a Pt/HfO2/WO3−x/TiN SRM structure is reported with an impressive rectification ratio above 106. To elucidate the underlying mechanisms, we systematically investigate the impact of the WO3−x resistive layer thickness modulation on the device's conductive behavior. Our findings reveal that the abundant traps in the WO3−x resistive layer and the excellent insulating property of HfO2 synergistically suppress negative current while promoting positive current. According to the simulation, the crossbar array based on the proposed SRMs can realize an array scale of over 21 Gbit. Furthermore, artificial synapses fabricated using these SRMs demonstrate a remarkable linearity of 0.9973. In conclusion, our results underscore the great potential of these SRMs for the ultra-large-scale integration of neuromorphic hardware, providing a guide for future ultra-high-energy efficiency hardware with minimal circuit overhead. [ABSTRACT FROM AUTHOR]

In conventional designs, sensory systems are segregated from memory and computing units. The conversion and transmission of data from analog sensing domains to digital storage result in inefficient power utilization and increased latency. Here, a multifunctional memristor capable of detecting gamma radiation while also serving as a data storage device and an artificial synapse is reported. Large‐scale integration of oxide‐based memristors for artificial neural networks faces major challenges due to the sneak‐path current issue in crossbar arrays. Consequently, material combinations and fabrication variables significantly shape nanoscale processes, which are essential in determining resistive switching properties and functionalities. Resistive switching in AlFeO3 is studied using different electrode materials (silver (Ag), gold (Au), chromium (Cr), fluorine‐doped tin oxide, and silicon), embedding metal (Ag, Au) nanocrystals to engineer a class of tunable memories capable of functioning as selector, memory, artificial synapse, and dosimeter. Techniques like electrode engineering, nanocrystal seeding, and temperature‐dependent thin film deposition are employed to tune resistive and threshold switching functionalities. Accessing different functionalities requires changing the electrode materials or changing the synthesis conditions of the AlFeO3 resistive switching layer and are not interconvertible in the same device simultaneously. The devices emulate critical neural functions and demonstrate interconversion dynamics between short‐term and long‐term plasticity. [ABSTRACT FROM AUTHOR]

Binocular stereo vision relies on imaging disparity between two hemispherical retinas, which is essential to acquire image information in three dimensional environment. Therefore, retinomorphic electronics with structural and functional similarities to biological eyes are always highly desired to develop stereo vision perception system. In this work, a hemispherical optoelectronic memristor array based on Ag‐TiO2 nanoclusters/sodium alginate film is developed to realize binocular stereo vision. All‐optical modulation induced by plasmonic thermal effect and optical excitation in Ag‐TiO2 nanoclusters is exploited to realize in‐pixel image sensing and storage. Wide field of view (FOV) and spatial angle detection are experimentally demonstrated owing to the device arrangement and incident‐angle‐dependent characteristics in hemispherical geometry. Furthermore, depth perception and motion detection based on binocular disparity have been realized by constructing two retinomorphic memristive arrays. The results demonstrated in this work provide a promising strategy to develop all‐optically controlled memristor and promote the future development of binocular vision system with in‐sensor architecture. [ABSTRACT FROM AUTHOR]

This study explores the impact of organic cations in bismuth iodide complexes on their memristive behavior in metal-insulator-metal (MIM) type thin-layer devices. The presence of electron-donating and electron withdrawing functional groups (-CN, -CH3, -NH2, and -N(CH3)2) on pyridinium cations induces morphological alterations in crystals, thus influencing the electronic or ionic conductivity of devices comprising sandwiched thin layers (thickness = 200 nm ± 50) between glass/ITO as bottom electrode (~110 nm) and copper (~80 nm) as the top electrode. It was found that the current-voltage (I-V) scans of the devices reveal characteristic pinched hysteresis loops, a distinct signature of memristors. The working voltage windows are significantly influenced by both the types of cation and the dimensionality of ionic fragments (0D or 1D) in the solid-state form. Additionally, the temperature alters the surface area of the I-V loops by affecting resistive switching mechanisms, corresponding log-log plots at three temperatures (-30 °C, room temperature and 150 °C) are fully studied. Given that a memristor can operate as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigates long-term depression, potentiation, and spike-time-dependent plasticity-a specific form of the Hebbian learning rule-to mimic biologically synaptic plasticity. Different polar pulses, such as triangle, sawtooth, and square waveforms were employed to generate Hebbian learning rules. The research demonstrates how the shape of the applied pulse series, achieved by overlapping pre- and postpulses at different time scales, in association with the composition and dimensionality of ionic fragments, lead to changes in the synaptic weight percentages of the devices. [ABSTRACT FROM AUTHOR]

Memristors have emerged as promising devices for enabling efficient multiply-accumulate (MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC). However, variations in memristors and associated circuits can affect the accuracy of analog computing. Typically, this is mitigated by on-chip training, which is challenging for memristors with limited endurance. We present a hardware-software codesign using magnetic tunnel junction (MTJ)-based AiMC off-chip calibration that achieves software accuracy without costly on-chip training. Hardware-wise, MTJ devices exhibit ultralow cycle-to-cycle variations, as experimentally evaluated over 1 million mass-produced devices. Software-wise, leveraging this, we propose an off-chip training method to adjust deep neural network parameters, achieving accurate AiMC inference. We validate this approach with MAC operations, showing improved transfer curve linearity and reduced errors. By emulating large-scale neural network models, our codesigned MTJ-based AiMC closely matches software baseline accuracy and outperforms existing off-chip training methods, highlighting MTJ's potential in AI tasks. [ABSTRACT FROM AUTHOR]

Dynamic memristors are considered as the optimal hardware devices for reservoir computing (RC) enabled by their nonlinear conductance variations. This significantly reduces the extensive training workload typically required by traditional neural networks. Lead halide perovskites, with their tunable band structure and active ion migration properties, have emerged as highly promising materials for developing dynamic memristors. However, large‐scale and consistently stable production remains a challenge for perovskite functional films, while lead elements' toxicity and environmental impact also partly restrict their practical device utilization. In this work, lead‐free copper‐based perovskite (i.e., CsCu2I3) films are prepared by thermal evaporation for constructing dynamic memristors. The effective conductivity modulation of CsCu2I3‐based memristor can be utilized in artificial neural networks, achieving a high handwritten digit recognition accuracy of 91.2%. In addition, the RC system is also constructed based on the dynamic behavior of the devices, by which a letter recognition accuracy of 98.2% with simple training is achieved. This technology provides a feasible pathway to construct copper‐based perovskite dynamic memristors for future neural network information processing. [ABSTRACT FROM AUTHOR]

Top‐contact bottom‐gate pentacene OFETs are fabricated with single layer dielectrics comprised of either polystyrene (PS), poly(4‐methylstyrene) (P4MS), or poly(4‐tert‐butylstyrene) (P4TBS). The polystyrenes are blended with varying concentrations of two different small molecules, dibenzotetrathiafulvalene (DBTTF) and 2,8‐difluoro‐5,11‐bis(triethylsilylethynyl)anthradithiophene (diF‐TES‐ADT), to form small, separated crystallites contained throughout the polymer dielectric layer. The OFET characteristics of these devices are investigated and their threshold voltage shifts are measured after −70 V static charging for 5 min. Two‐terminal measurements are conducted using multiple different gate biases in the range of −50 to +50 V to investigate memristor behavior in the devices. OFETs containing DBTTF exhibited ΔVth increases as large as 330% relative to control OFETs containing no DBTTF, while OFETs containing at least 7.5 wt.% DBTTF exhibited memristor activity, with currents ranging from 20 nA to 44 µA depending on the applied bias. This work demonstrates that including small, separated crystallites in polymer dielectrics enhances their charge storage ability and can be promising for creating nonbinary memory devices for data processing. Additionally, the observed memristor activity indicates the OFETs in this work can be used in development of neuromorphic systems that aim to mimic the synaptic behavior of the human nervous system. [ABSTRACT FROM AUTHOR]

This study introduces a pioneering design for leaky integrate-and-fire (LIF) neurons by integrating memristor devices with CMOS transistors, thereby forming an innovative hybrid CMOS/memristor neuron circuit. Employing Pt/TaOx/Ta as the memristor device, the proposed model was meticulously implemented and rigorously evaluated using the Cadence Virtuoso simulation environment. The simulation outcomes affirm the effective functionality of the design, marking a significant advancement in hybrid circuit engineering. Notably, the proposed neuron circuit exhibits a compact footprint, attributed to the efficient utilization of hybrid CMOS/memristor gates. This characteristic is poised to address the critical challenge of scaling in current neuromorphic systems, offering a viable pathway to substantially augment density and cater to the escalating demands of advanced computational architectures. The findings of this research hold promising implications for enhancing the efficiency and scalability of neuromorphic systems, setting a new benchmark for future developments in this domain. [ABSTRACT FROM AUTHOR]

A neuromorphic computing network based on SiCx memristor paves the way for a next-generation brain-like chip in the AI era. Up to date, the SiCx–based memristor devices are faced with the challenge of obtaining flexibility and uniformity, which can push forward the application of memristors in flexible electronics. For the first time, we report that a flexible artificial synaptic device based on a Ag NPs:a–SiC0.11:H memristor can be constructed by utilizing aluminum foil as the substrate. The device exhibits stable bipolar resistive switching characteristic even after bending 1000 times, displaying excellent flexibility and uniformity. Furthermore, an on/off ratio of approximately 107 can be obtained. It is found that the incorporation of silver nanoparticles significantly enhances the device's set and reset voltage uniformity by 76.2% and 69.7%, respectively, which is attributed to the contribution of the Ag nanoparticles. The local electric field of Ag nanoparticles can direct the formation and rupture of conductive filaments. The fitting results of I–V curves show that the carrier transport mechanism agrees with Poole–Frenkel (P–F) model in the high-resistance state, while the carrier transport follows Ohm's law in the low-resistance state. Based on the multilevel storage characteristics of the Al/Ag NPs:a–SiC0.11:H/Al foil resistive switching device, we successfully observed the biological synaptic characteristics, including the long–term potentiation (LTP), long–term depression (LTD), and spike–timing–dependent plasticity (STDP). The flexible artificial Ag NPs:a–SiC0.11:H/Al foil synapse possesses excellent conductance modulation capabilities and visual learning function, demonstrating the promise of application in flexible electronics technology for high-efficiency neuromorphic computing in the AI period. [ABSTRACT FROM AUTHOR]

In this study, a tri‐layer Pt/Al/TiOx/HfOx/Al2O3/Pt memristor device is fabricated and analyze its electrical characteristics for reservoir computing and neuromorphic systems applications. This device incorporates an oxygen reservoir of a TiOx and a barrier layer of an Al2O3, enabling stable bipolar switching characteristics without the need for an electroforming process over 103 cycles. It also exhibits self‐rectifying properties under a negative bias. Based on these characteristics, it is investigated essential synaptic functions such as long‐term potentiation (LTP), long‐term depression (LTD), paired‐pulse facilitation (PPF), spike‐rate‐dependent plasticity (SRDP), spike‐duration‐dependent plasticity (SDDP), spike‐number‐dependent plasticity (SNDP), and spike‐amplitude‐dependent plasticity (SADP), to assess their suitability for neuromorphic applications that mimic biological synapses. Furthermore, utilizing the short‐term memory characteristics of the device, reservoir computing (RC) measurement from [0000] to [1111] in 4‐bit representation is conducted. This capability enables us to achieve a high accuracy of 95.5% in MNIST pattern recognition tasks. Lastly, the natural decay characteristics caused by oxygen ion migration in the device, examining the transition from short‐term to long‐term memory in image memorization tasks is explored. The potential for deployment in high‐density crossbar arrays by calculating the read margin based on the device I–V curve and programming scheme is also evaluated. [ABSTRACT FROM AUTHOR]

The self‐rectifying memristor with a bilayer of trap‐rich HfO2 and insulating Ta2O5 oxide layers is considered one of the most promising candidates for the memristive crossbar array due to its superior switching performance, scalability with 3D stacking, and low operating power. However, the output current variation due to the electron detrapping from trap states can cause the failure of critical operations in neuromorphic applications. This work suggests two solutions to mitigate the switching variations and insufficient data retention time by embedding gold nanodots and modifying the bias voltage application methods for read, write, and erase operations in the crossbar array. The switching mechanism is studied by varying the embedded position of the gold nanodots across the thickness direction of the bilayered oxides, which helped to optimize device performance further. Combining the two solutions into the proposed self‐rectifying memristor enables a single device to have the 7‐possible, stable states by preventing interstate overlap and securing the retention. Consequently, the hardware neural network consisting of self‐rectifying memristors with gold nanodots with the modified bias voltage application methods demonstrates a high inference accuracy of 93.1% in MNIST handwritten digit classification, comparable to the software‐based accuracy of 93.4%, benefiting from the enhanced multi‐state uniformity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Implantable devices hold the potential to address conditions currently lacking effective treatments, such as drug-resistant neural impairments and prosthetic control. Medical devices need to be biologically compatible while providing enhanced performance metrics of low-power consumption, high accuracy, small size, and minimal latency to enable ongoing intervention in brain function. Here, we demonstrate a memristor-based processing system for single-trial detection of behaviorally meaningful brain signals within a timeframe that supports real-time closed-loop intervention. We record neural activity from the reward center of the brain, the ventral tegmental area, in rats trained to associate a musical tone with a reward, and we use the memristors built-in thresholding properties to detect nontrivial biomarkers in local field potentials. This approach yields consistent and accurate detection of biomarkers >98% while maintaining power consumption as low as 4.14 nanowatt per channel. The efficacy of our system's capabilities to process real-time in vivo neural data paves the way for low-power chronic neural activity monitoring and biomedical implants. [ABSTRACT FROM AUTHOR]

Implementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms. [ABSTRACT FROM AUTHOR]

To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbOx/Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors. [ABSTRACT FROM AUTHOR]

Carbon quantum dots‐based memristors (CQDMs) have emerged as a rising star in data storage and computing. The key constraint to their commercialization is memristance variability, which mainly arises from the disordered conductive paths. Doping methodology can optimize electron and ion transport to help construct a stable conductive mode. Herein, based on boron (B)‐doped engineering strategy, three kinds of comparable quantum dots are synthesized, including carbon quantum dots (CQDs), a series of boron‐doped CQDs (BCQDs) with different B contents, and boron quantum dots. The corresponding device performances highlight the superiority of BCQDs‐based memristors, exhibiting a ternary flash‐type memory behavior with longer retention time and more controllable memristance stability. The comprehensive analysis results, including device performance, functional layer morphology, and material simulated calculation, illustrate that the doped B elements can directionally guide the migration of aluminum ions by enhancing the capture of free electrons, resulting in ordered conductive filaments and stable ternary memory behavior. Finally, the conceptual applications of logic display and logic gate are discussed, indicating a bright prospect for BCQDs‐based memristors. This work proves that modest B doping can optimize memristance property, establishing a theoretical foundation and template scheme for developing effective and stable CQDMs. [ABSTRACT FROM AUTHOR]

Recently, an emerging class of hydrogen-bonded organic frameworks (HOFs) has become an appealing member of organic material family, attributed to their layered self-assembly structures, high-crystalline, and environmentally friendly characteristics, which have rapidly propelled their development in the field of electronic devices. In this context, we focus on the latest category of topological HOFs, with particular attention given to cutting-edge experimental demonstrations, exceptional electrical performances, and promising applications. First, a concise concept and fundamental mechanism of HOFs are provided, elucidating the potential correlation between structural designs and material properties. Subsequently, a comprehensive summary is presented on the preparation and synthesis methods, such as hydrothermal techniques, epitaxial growth, electro-deposition, among others. Notably, the latest advancements in HOFs-based electronics are thoroughly introduced and discussed, along with their applications in sensors, memristors, artificial synapses, neuromorphic computing, and human perception systems. Finally, the future challenges and prospects of topological HOFs are elaborated upon with the aim of providing valuable guidance for high-performance HOF-based electronics. [ABSTRACT FROM AUTHOR]

Mathematical models such as Fitzhugh–Nagoma and Hodgkin–Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0–1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks. [ABSTRACT FROM AUTHOR]

In this work, three simple configurations of meminductor mutator are presented. The first two configurations of meminductor mutator have been implemented utilizing one CMOS-based operational amplifier, one memristor, one capacitor, and five resistors, while the third configuration of meminductor mutator is implemented utilizing one CMOS-based operational amplifier, two memristors, one capacitor, and four resistors. The implementation and simulation of the proposed configurations are done by using LTspice tool. The viability of the proposed circuits is demonstrated by utilizing TSMC 180 nm CMOS technology parameters. The proposed circuits of the meminductor have a simple structure in contrast to many of the circuits presented in the literature and operate satisfactorily over a wide range of frequencies. The functionality of the proposed circuits is verified through utilization of both the spice model of memristor and a memristor emulator circuit. A chaotic oscillator and second-order high pass filter are designed using the proposed meminductor to verify its workability. [ABSTRACT FROM AUTHOR]

Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256‐level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Previous work that studied hexagonal boron nitride (h‐BN) memristor DC resistive‐switching characteristics is extended to include an experimental understanding of their dynamic behavior upon programming or synaptic weight update. The focus is on the temporal resistive switching response to driving stimulus (programming voltage pulses) effecting conductance updates during training in neural network crossbar implementations. Test arrays are fabricated at the wafer level, enabled by the transfer of CVD‐grown few‐layer (8 layer) or multi‐layer (18 layer) h‐BN films. A comprehensive study of their temporal response under various conditions–voltage pulse amplitude, edge rate (pulse rise/fall times), and temperature–provides new insights into the resistive switching process toward optimized devices and improvements in their implementation of artificial neural networks. The h‐BN memristors can achieve multi‐state operation through ultrafast pulsed switching (< 25 ns) with high energy efficiency (≈10 pJ pulse−1). [ABSTRACT FROM AUTHOR]

Halide perovskites (HPs) are promising materials for memristor devices because of their unique characteristics. In this study, nonvolatile resistive switching memory devices based on thick MAPbI3 perovskite (800 nm) films with structure FTO/MAPbI3/polymethyl methacrylate (PMMA)/Ag are presented. Reproducible and reliable bipolar switching characteristics are demonstrated with an ultra‐low operating voltage (−0.1 V), high ON/OFF ratio (106), endurance (>2 × 103 times) and a record retention time (>105 s). The I–V curve of the first cycle exhibits self‐formed conductive filaments. These are attributed to the presence of metallic Pb resulting from an excess of PbI2 in the perovskite film. The subsequent activation process involves the formation of conductive filaments, consisting of either iodide vacancies or migrated charged metals. Numerical simulations are then carried out to understand the nature of these conductive filaments and the role of the internal electric field in the migration of iodide ions, iodide vacancies, and Ag cations. Finally, an exhaustive model is proposed that explains the set and reset processes governing the first voltage cycle and the steady state, at different voltage ranges. In summary, this work offers a novel and thorough perspective of the complete resistive switching (RS) behavior in a MAPbI3/buffer/Ag memristor, supported by numerical simulations. [ABSTRACT FROM AUTHOR]

In 1971, Chua defined an ideal memristor that links magnetic flux φ and electric charge q. In a magnetic lump with a current-carrying conductor, we found that the direct interaction between physical magnetic flux φ and physical electric charge q is memristive by nature in terms of a time-invariant φ-q curve being nonlinear, continuously differentiable and strictly monotonically increasing. Although we succeeded in demonstrating that the "ideal/real/perfect/... memristor" needs magnetism, the structure still suffers from two serious limitations: 1. a parasitic "inductor" effect and 2. bistability and dynamic sweep of a continuous resistance range. Then, we discussed how to overcome these two limitations to make a fully functioning ideal memristor with multiple or an infinite number of stable states and no parasitic inductance. We then gave a number of innovations to the current memristor structure, such as an "open" structure, nanoscale size, magnetic materials with cubic anisotropy (or even isotropy) and sequential switching of the magnetic domains. Contrary to the conjecture that "an ideal memristor may not exist or may be a purely mathematical concept", we remain optimistic that an ideal memristor will be discovered in nature or will be made in the laboratory. Our finding of the memristive flux–charge interaction may advance the development and application of the memristor technology. [ABSTRACT FROM AUTHOR]

Oxygen vacancy engineering in metal oxides is a propitious route to modulate their resistive switching properties for memory and neuromorphic applications. This review provides an account of the research works on tailoring RS behavior in oxide thin-film-based memristor devices by oxygen vacancy engineering. We discuss the recent research progress on controlling oxygen vacancy concentration in metal oxide thin films and its impact on their resistive switching properties for application in electronic memory and neuromorphic computing devices. [ABSTRACT FROM AUTHOR]

Halide perovskites with naturally coupled electron‐ion dynamics hold great potential for nonvolatile memory applications. Self‐rectifying memristors are promising as they can avoid sneak currents and simplify device configuration. Here we report a self‐rectifying memristor firstly achieved in a single perovskite (NHCINH3)3PbI5 (abbreviated as (IFA)3PbI5), which is sandwiched by Ag and ITO electrodes as the simplest cell in a crossbar array device configuration. The iodide ions of (IFA)3PbI5 can be easily activated, of which the migration in the bulk contributes to the resistance hysteresis and the reaction with Ag at the interface contributes to the spontaneous formation of AgI. The perfect combination of n‐type AgI and p‐type (IFA)3PbI5 gives rise to the rectification function like a p–n diode. Such a self‐rectifying memristor exhibits the record‐low set power consumption and voltage. This work emphasizes that the multifunction of ions in perovskites can simplify the fabrication procedure, decrease the programming power, and increase the integration density of future memory devices. [ABSTRACT FROM AUTHOR]

The solid-electrolyte-based memristors have attracted tremendous attention for the next-generation nonvolatile memory for both logic and neuromorphic applications. However, they encounter variability performance challenges which originated from the random ionic transport and conductive filaments formation. Evidently, the electrochemical metallized mechanism associated with ion transport has been elucidated. Nonetheless, the failure mechanism caused by ion transport during cycles is rarely reported. Hereafter, the five stages of failure in the Ag/Ag10Ge15Te75/W memristor are elucidated through ex-situ current-voltage measurements combined with in-situ transmission electron microscopy characteristics. Our investigation reveals that the migration and enrichment of Ag ions result in the precipitation of Ag2Te. The formation of Ag2Te hinders the device's ability to maintain its bipolar characteristics and also decreases the resistance value of the high resistance state, thereby reducing the device's switching ratio. The promising results provide important guidance for the future design of structures and the manipulation of ion transport for high-performance memristors. [ABSTRACT FROM AUTHOR]