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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]

This study presents a preliminary exploration of thermally oxidized TaOx-based memristors and their potential as artificial synapses. Unlike the 10-min annealed devices, which display instability due to current overshoots, the 5-min annealed device exhibits stable resistive switching, retention, and endurance characteristics. Moreover, our memristor showcases synaptic behaviors encompassing potentiation, depression, spike-timing-dependent plasticity, and excitatory postsynaptic currents. This synaptic emulation holds tremendous promise for applications in neuromorphic computing, offering the opportunity to replicate the adaptive learning principles observed in biological synapses. In addition, we evaluate the device's suitability for pattern recognition within a neural network using the modified National Institute of Standards and Technology dataset. Our assessment reveals that the Pt/TaOx/Ta memristor with an oxidized insulator achieves outstanding potential manifested by an accuracy of 93.25% for the identical pulse scheme and an impressive accuracy of 95.42% for the incremental pulse scheme. [ABSTRACT FROM AUTHOR]

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

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

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]

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]

Thermotronics has attracted much attention driven by the promising potentials offered by devices such as thermal diodes, thermal transistors, and thermal memristors. Heat shuttling (or heat ratcheting, or heat pumping) is a phenomenon exhibited by nonlinear materials presenting temperature-dependent thermal conductivity which, when sandwiched between two thermal baths with one bath subjected to a time-varying temperature, show nonvanishing net heat flow, although the baths share the same average temperature. Phase-change materials (PCMs) like VO 2 were recently taken for illustration due to a strong change in conductivity over a small temperature range; energy extraction from the thermal variations of the environment was envisioned thereupon. However, up to now, the impact of PCM hysteresis has been either overlooked or roughly approximated. On the basis of a thermal model simulating partial hysteresis loops and nonhysteretic branches, we demonstrate that the presence of hysteresis profoundly modifies the appearance of the heat-shuttling effect and can constitute a hindrance to its manifestation. Operating configurations to improve its observation have been proposed. [ABSTRACT FROM AUTHOR]

Thermotronics has attracted much attention driven by the promising potentials offered by devices such as thermal diodes, thermal transistors, and thermal memristors. Heat shuttling (or heat ratcheting, or heat pumping) is a phenomenon exhibited by nonlinear materials presenting temperature-dependent thermal conductivity which, when sandwiched between two thermal baths with one bath subjected to a time-varying temperature, show nonvanishing net heat flow, although the baths share the same average temperature. Phase-change materials (PCMs) like VO 2 were recently taken for illustration due to a strong change in conductivity over a small temperature range; energy extraction from the thermal variations of the environment was envisioned thereupon. However, up to now, the impact of PCM hysteresis has been either overlooked or roughly approximated. On the basis of a thermal model simulating partial hysteresis loops and nonhysteretic branches, we demonstrate that the presence of hysteresis profoundly modifies the appearance of the heat-shuttling effect and can constitute a hindrance to its manifestation. Operating configurations to improve its observation have been proposed. [ABSTRACT FROM AUTHOR]

Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼−0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and "learning-forgetting-learning" behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions. [ABSTRACT FROM AUTHOR]

Tunable and uniform evolution of conductance is the key performance metric for neuromorphic computing leveraging memristors. Nonetheless, the stochastic conductance update associated with limited material composition and uncontrollable filament distribution has restricted the tunability that can be customized for targeted synaptic properties. Here, we introduce organic heterojunction memristors utilizing the C60/P3HT bilayer, demonstrating analog switching characteristics with multilevel conductance states. We demonstrate that both conventional bipolar and unipolar voltages can achieve synaptic plasticity modulation for potentiation and depression, offering enhanced tunability. Through in situ Raman spectroscopy and impedance spectroscopy, we directly observe the dynamic alterations within the active layers during switching processes. The reversible migration of ions diminishes the barrier within the polymer layer, leading to highly uniform resistive switching behavior. The C60 layer functions as a confined transport medium, mitigating critical current variability issues. Moreover, we introduce a shunt resistor approach, furnishing analog memristors with selectively adjustable uniformity, enhanced linearity, and expanded dynamic conductance range, providing a general solution adaptable to various memristive hardware architectures. [ABSTRACT FROM AUTHOR]

The synaptic response of ZnO/HfZrO memristor devices to electrical stimuli was studied. It was found that the frequency of the stimuli affects the rate of current increase, which can be explained by the cluster state of oxygen vacancies at the interface of ZnO/HfZrO. This variation in the synaptic response due to stimulus frequency was consistent with the dynamic adjustment of tolerance thresholds observed in the human brain's response to environmental stimuli. The rate of change in current, representing the sensitivity to stimuli, mirrors the biological nervous system's reaction to environmental changes, such as Ebbinghaus forgetting behavior. Additionally, frequent irregular changes in stimuli lead to a reduced lifespan of the devices, highly resembling the biological lifetime-injure on frequent environmental fluctuations. These findings highlight the memristors' significant similarity to biological nerves in response to stimuli, confirming their tremendous potential in bionic synaptic simulation applications. [ABSTRACT FROM AUTHOR]

A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of "task specific" neural networks that derive from this material tunability. [ABSTRACT FROM AUTHOR]

The proposal of discrete memristors has made memristive chaotic maps based on them an important research topic. In this study, a new two-dimensional chaotic map without fixed points is constructed, and numerical simulation results display its rich dynamical behaviors. The analysis reveals the map's center inversion symmetry and Lyapunov exponent controller. The map exhibits complex dynamical behaviors, including memristor initial-boosting and single-parameter-offset boosting. Embedding the absolute value function within the memristor results in the emergence of localized boosting-free regions. Moreover, a class of multicavity transients is captured that greatly enhances the system's complexity. Ultimately, this map is implemented on the STM32 platform, demonstrating its practical applicability in potential practical application scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a special spoon neural network is proposed, which is composed of four neurons with direct connection and indirect connection. On this basis, the far induction network and the near induction network (NINN) are constructed by using hyperbolic tangent memristors to explore the influence of electromagnetic induction between neurons at different positions on the dynamic behavior of attractors. NINN exhibits more complex attractor structures and wider chaotic parameters, and also displays a heterogeneous coexisting attractor of limit cycles and chaos under network parameter control. By varying the parameters, coexisting chaotic attractors can be synthesized into a double scrolls attractor, and their oscillation amplitude can be controlled without changing the chaotic characteristics. The type of attractors in human brain determines the clarity of memory. These complex dynamic behaviors demonstrate that near induction has a more pronounced effect on the forgetting and disappearance of memory compared to far induction. Finally, a circuit using switches to change the type of electromagnetic induction is constructed and the results are verified. [ABSTRACT FROM AUTHOR]

To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there is a requirement for the discovery and design of artificial neuron hardware that mimics the advanced functionality and operation of the neural networks available in biological organisms. We examine experimental artificial neuron circuits that we designed and built in hardware with memristor devices using 4.2 nm of hafnium oxide and niobium metal inserted in the positive and negative feedback of an oscillator. At room temperature, these artificial neurons have adaptive a spiking behavior and hybrid non-chaotic/chaotic modes. When networked, they output with strong itinerancy, and we demonstrate a four-neuron learning network and modulation of signals. The superconducting state at 8.1 K results in Josephson tunneling with signs that the hafnium oxide ionic states are influenced by quantum control effects in accordance with quantum master equation calculations of the expectation values and correlation functions with a calibrated time-dependent Hamiltonian. These results are of importance to continue advancing neuromorphic hardware technologies that integrate memristors and other memory devices for many biological-inspired applications and beyond that can function with adaptive-itinerant spiking and quantum effects in their principles of operation. [ABSTRACT FROM AUTHOR]

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between "0" and "1." [ABSTRACT FROM AUTHOR]

Organic–inorganic hybrid perovskites (OIHPs) have been spotlighted as emerging solution-processed electronics and show promise as economical, flexible, and stretchable emerging memristors. However, OIHPs suffer from considerable defects derived from solution processing; thus, post-processing is often required to alleviate such shortcomings. Treating alkyl ammonium organic salt on OIHP films is a representative approach to efficiently mediate the defect density. Here, we adopted n-octyl ammonium chloride (OACl) in our post-processing agent for organometal perovskite memristors (OPMs). The OACl-incorporated OPM showed an enlarged ON/OFF resistance ratio of 520 compared to OPM without passivation (3.91). Thin film analysis revealed that the synergistic insulating/passivating layer from OACl post-treatment retarded the interaction with the electroactive electrode (Ag). Additionally, the combined effects from the lower-dimensional OIHP induced by OA, including the p-doping of lead by chloride, enhanced the energetic barrier with Ag electrode to reduce the parasitic current at the high resistance state. This study offers deeper insights into the surface passivation of OIHPs and its effects on memristic performance. [ABSTRACT FROM AUTHOR]

Harnessing the full power of memristors as artificial synapses demands a simple and scalable crossbar architecture enabling their seamless integration into diverse applications. This Letter presents the 3 × 3 memristor crossbar array configuration featuring a grid of interconnected devices. The composition includes Al as the reactive top electrode connecting the device columns and Gd1−xCaxMnO3 (GCMO, x = 0.8) serving as the bottom electrode connecting the device rows as well as the memristive material eliminating the need for additional layers and fabrication steps. Controlled-sized vias through insulating the Al2O3 layer connect the electrodes forming the active interface. The idea is validated with a test sample of 3 × 3 crossbars with the Au/GCMO/Al structure, Au enabling Ohmic contact to GCMO, with device resistive switching ratios mostly around 102 and yield of over 90%. The devised crossbar structure could provide a highly scalable, yet simple, geometry suitable for synaptic networks. [ABSTRACT FROM AUTHOR]

This study presents a rapid synthesis method using carbon fibers, converting ammonium tetrathiomolybdate to molybdenum disulfide films with controllable thickness (1–5 nm) and smooth surface (200 pm on average) in tens of seconds. Electronic devices based on these multilayers exhibit promising performance: memristors operate at an average of 1.5 V; field-effect transistors display electron mobility ranging from 0.1 to 0.4 cm2/V·s with a 103 on/off ratio. The electrical performance can be enhanced by optimizing annealing recipes. This scalable, time-efficient method holds the potential for large-scale production of multilayer two-dimensional semiconductors for electronics. [ABSTRACT FROM AUTHOR]

Memristor crossbar arrays are expected to achieve highly energy-efficient neuromorphic computing via implementing parallel vector–matrix multiplication (VMM) in situ. The similarities between memristors and neural synapses offer opportunities for realizing hardware-based brain-inspired computing, such as spike neural networks. However, the nonlinear I–V characteristics of the memristors limit the implementation of parallel VMM on passive memristor crossbar arrays. In our work, we propose to utilize differential conductance as a synaptic weight to implement linear VMM operations on a passive memristor array in parallel. We fabricated a TiO2/HfO2 memristor crossbar array, in which differential-conductance-based synaptic weight exhibits plasticity, nonvolatility, multi-states, and tunable ON/OFF ratio. The noise-dependent accuracy performance of VMM operations based on the proposed approach was evaluated, offering an optimization guideline. Furthermore, we demonstrated a spike neural network circuit capable of processing small spiking signals through the differential-conductance-based synapses. The experimental results showcase effective space-coded and time-coded spike pattern recognition. Importantly, our work opens up new possibilities for the development of passive memristor arrays, leading to increased energy and area efficiency in brain-inspired chips. [ABSTRACT FROM AUTHOR]

Neuromorphic computation is based on memristors, which function equivalently to neurons in brain structures. These memristors can be made more efficient and tailored to neuromorphic devices by using ferroelastic domain boundaries as fast diffusion paths for ionic conduction, such as of oxygen, sodium, or lithium. In this paper, we show that the local memristor generates a second, unexpected feature, namely, weak magnetic fields that emerge from moving ferroelastic needle domains and vortices. The vortices appear near ferroelastic "junctions" that are common when the external stimulus is a combination of electric fields and structural phase transitions. Many ferroelastic materials show such phase transitions near room temperatures so that device applications display a "multiferroic" scenario where the memristor is driven electrically and read magnetically. Our computer simulation study of an elastic spring model suggests magnetic fields in the order of 10−7 T, which opens the way for a fundamentally new way of running neuromorphic devices. The magnetism in such devices emerges entirely from intrinsic displacement currents and not from any intrinsic magnetism of the material. [ABSTRACT FROM AUTHOR]

Memristors have attracted considerable attention as next-generation devices for logic and neuromorphic computing applications, owing to their high on/off current ratio, low power consumption, and high switching speed. Despite the various excellent characteristics of memristors, they suffer from unstable conductive filament-based switching when applied in real-world applications. To address this issue, the effects of Schottky barrier modulation on device performance, in terms of conduction and failure mechanisms of an Ag/WOx/p-Si memristor, were investigated in this study by varying the silicon (Si) doping concentration. Through the temperature analysis of I–V characteristics, different conduction mechanisms are observed according to the doping concentration and resistance state. Moreover, endurance failure with several doping concentrations is analyzed by using filament overgrowth phenomena. The results of this study are expected to help in the development of devices with characteristics suitable for application. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Emerging optoelectronic memristors are promising candidates to develop neuromorphic computing, owing to the combined advantages of photonics and electronics. However, the reversible modulation on device conductance usually requires complicated operations involving hybrid optical/electrical signals. Herein, we design a fully light-modulated memristor based on ZnO/MoOx heterojunction, which exhibits potentiation and depression behaviors under the irradiation of ultraviolet and visible light, respectively. Several basic synaptic functions have been emulated by utilizing optical signals, including short-term/long-term plasticity and spike-number-dependent plasticity. Based on the all-optical modulation characteristics, low-level image pre-processing (including contrast enhancement and noise reduction) is demonstrated. Furthermore, logic operations ("AND," "NOTq," and "NIMP") can be performed by combining various optical signals in the same device. The memristive switching mechanism under optical stimulus can be attributed to barrier change at the heterojunction interface. This work proposes a fully light-modulated memristor based on ZnO/MoOx heterojunction that may promote the development of neuromorphic computing with high efficiency. [ABSTRACT FROM AUTHOR]

In the last decade, HfO2-based ferroelectric capacitors (FeCaps) have undergone significant advancements, particularly within the realm of nonvolatile ferroelectric random access memories (FeRAMs). Nonetheless, the READ operation in FeRAMs is inherently destructive, rendering it unsuitable for neuromorphic computing. In this study, we have engineered tunable nonvolatile capacitances within FeCaps, featuring nondestructive readout functionality. Robust capacitance states can be read at a zero d.c. bias (Vbias) with different a.c. signals, not only preventing the alteration of their stored state but also benefiting to the low power consumption. Moreover, the capacitance memory window (CMW) at Vbias of zero can be effectively modulated through electrode engineering, leading to a larger CMW when there is a greater disparity in work functions between the electrodes. Furthermore, we provide a comprehensive investigation into synaptic behavior of TiN/Hf0.5Zr0.5O2/Pt FeCaps, demonstrating their excellent cycle-to-cycle uniformity, retention, and endurance characteristics, which confirm their high reliability in maintaining nonvolatile capacitance states. These findings underscore the significant potential of FeCaps in advancing low-power neuromorphic computing. [ABSTRACT FROM AUTHOR]

Vision sensors are becoming increasingly ubiquitous, and they continuously collect, store, communicate, and process vast amount of sensitive data that are vulnerable to being stolen and misused. Existing cryptosystems based on complex cipher algorithms generally require extensive computational resources, making them difficult to use in vision sensors that have limited processing capabilities. Here, we propose and experimentally demonstrate a novel in situ image cryptography scheme based on a neuromorphic vision sensor comprising all-optically controlled (AOC) memristors. Due to the unique light wavelength and irradiation history-dependent bidirectional persistent photoconductivity of AOC memristors, a visual image can be stored, encrypted, decrypted, denoised, and destroyed within a vision sensor. A decrypted image can be encoded in situ and then accurately recognized through a memristive neural network. Encrypted and destroyed images are capable of withstanding hacking attacks even with trained neural networks. Our cryptography scheme enables complete cryptographic operations entirely on a sensor and, therefore, effectively safeguards visual information. This work provides a simple yet efficient solution to the security challenges faced by vision sensors. [ABSTRACT FROM AUTHOR]

The Von Neumann architecture has been the foundation of modern computing systems. Still, its limitations in processing large amounts of data and parallel processing have become more apparent as computing requirements increase. Neuromorphic computing, inspired by the architecture of the human brain, has emerged as a promising solution for developing next-generation computing and memory devices with unprecedented computational power and significantly lower energy consumption. In particular, the development of optoelectronic artificial synaptic devices has made significant progress toward emulating the functionality of biological synapses in the brain. Among them, the potential to mimic the function of the biological eye also paves the way for advancements in robot vision and artificial intelligence. This review focuses on the emerging field of optoelectronic artificial synapses and memristors based on low-dimensional nanomaterials. The unique photoelectric properties of these materials make them ideal for use in neuromorphic and optoelectronic storage devices, with advantages including high carrier mobility, size-tunable optical properties, and low resistor–capacitor circuit delay. The working mechanisms, device structure designs, and applications of these devices are also summarized to achieve truly sense-storage-computer integrated optoelectronic artificial synapses. [ABSTRACT FROM AUTHOR]

The commercial introduction of a novel electronic device is often preceded by a lengthy material optimization phase devoted to the suppression of device noise as much as possible. The emergence of novel computing architectures, however, triggers a paradigm shift in noise engineering, demonstrating that non-suppressed but properly tailored noise can be harvested as a computational resource in probabilistic computing schemes. Such a strategy was recently realized on the hardware level in memristive Hopfield neural networks, delivering fast and highly energy efficient optimization performance. Inspired by these achievements, we perform a thorough analysis of simulated memristive Hopfield neural networks relying on realistic noise characteristics acquired on various memristive devices. These characteristics highlight the possibility of orders of magnitude variations in the noise level depending on the material choice as well as on the resistance state (and the corresponding active region volume) of the devices. Our simulations separate the effects of various device non-idealities on the operation of the Hopfield neural network by investigating the role of the programming accuracy as well as the noise-type and noise amplitude of the ON and OFF states. Relying on these results, we propose optimized noise tailoring and noise annealing strategies, comparing the impact of internal noise to the effect of external perturbation injection schemes. [ABSTRACT FROM AUTHOR]

Despite the significant progress made in deep learning on digital computers, their energy consumption and computational speed still fall short of meeting the standards for brain-like computing. To address these limitations, reservoir computing (RC) has been gaining increasing attention across communities of electronic devices, computing systems, and machine learning, notably with its in-memory or in-sensor implementation on the hardware–software co-design. Hardware regarded, in-memory or in-sensor computers leverage emerging electronic and optoelectronic devices for data processing right where the data are stored or sensed. This technology dramatically reduces the energy consumption from frequent data transfers between sensing, storage, and computational units. Software regarded, RC enables real-time edge learning thanks to its brain-inspired dynamic system with massive training complexity reduction. From this perspective, we survey recent advancements in in-memory/in-sensor RC, including algorithm designs, material and device development, and downstream applications in classification and regression problems, and discuss challenges and opportunities ahead in this emerging field. [ABSTRACT FROM AUTHOR]

In viscous dynamics, velocity is proportional to the force. An ideal memristor is a device whose resistance changes at a rate proportional to the driving input. We present a proof-of-principle demonstration of the connection between viscous dynamics and memristive functionality by utilizing a thin-film ferromagnet/antiferromagnet bilayer, where viscous magnetization dynamics results from the frustration at the magnetic interface, and driving is provided by an external magnetic field. Thanks to the atomic scale of frustration effects, the presented approach is amenable to downscaling. It can also be adapted for electronic driving by spin torque, making it attractive for applications in neuromorphic circuits. [ABSTRACT FROM AUTHOR]

A large number of simulation models have been proposed over the years to mimic the electrical behavior of memristive devices. The models are based either on sophisticated mathematical formulations that do not account for physical and chemical processes responsible for the actual switching dynamics or on multi-physical spatially resolved approaches that include the inherent stochastic behavior of real-world memristive devices but are computationally very expensive. In contrast to the available models, we present a computationally inexpensive and robust spatially 1D model for simulating interface-type memristive devices. The model efficiently incorporates the stochastic behavior observed in experiments and can be easily transferred to circuit simulation frameworks. The ion transport, responsible for the resistive switching behavior, is modeled using the kinetic cloud-in-a-cell scheme. The calculated current–voltage characteristics obtained using the proposed model show excellent agreement with the experimental findings. [ABSTRACT FROM AUTHOR]

We present a unique compact model for oxide memristors based upon the concentration of oxygen vacancies as state variables. In this model, the increase (decrease) in oxygen vacancy concentration is similar in effect to the reduction (expansion) of the tunnel gap used as a state variable in existing compact models, providing a mechanism for the electronic current to increase (decrease) based upon the polarity of the applied voltage. Rate equations defining the dynamics of state variables are obtained from simplifications of a recent paper in which electronic processes (i.e., electron capture/emission) were combined with atomic processes (i.e., Frenkel-pair generation/recombination, diffusion) stemming from the thermochemical model of dielectric breakdown. Central to the proposed model is the effect of the electron occupancy of oxygen vacancy traps on resistive switching dynamics. The electronic current is calculated considering Ohmic, band-to-band, and bound-to-band contributions. The model includes uniform self-heating with Joule heating and conductive loss terms. The model is calibrated using experimental current–voltage characteristics for Hf O 2 memristors with different electrode materials. Though a general model is presented, a delta-shaped density of states profile for oxygen vacancies is found capable of accurately representing experimental data while providing a minimal description of bound-to-band transitions. The model is implemented in Verilog-A and tested using read/write operations in a 4 × 4 1T1R nonvolatile memory array to evaluate its ability to perform circuit simulations of practical interest. A particular benefit is that the model does not make strong assumptions regarding filament geometry of which scant experimental-evidence exists to support. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO2) is a high-performance material for emerging device applications, such as in resistive switching memories, in high-k capacitors, or, due to its flexoelectricity, in micro/nano-electro-mechanical systems. Enhanced electrical properties of TiO2 are ensured, especially by a careful selection of the bottom electrode material. Iridium dioxide (IrO2) is an excellent choice, as it favors the high-k rutile phase growth of TiO2. In this study, we introduce the fabrication of IrO2/TiO2/IrO2 capacitors and thoroughly characterize their electrical behavior. These capacitors show a dielectric constant for low temperature sputtered TiO2 of ∼70. From leakage current measurements, a coupled capacitive–memristive behavior is determined, which is assumed due to the presence of a reduced TiO2−x layer at the IrO2/TiO2 interface observed from transmission electron microscopy analyses. The memristive effect most probably originates from trapping and detrapping of electric charges in oxygen vacancy defects, which themselves can be generated and annihilated through an applied electric field, subsequently changing the resistance of the capacitor. The electric degradation type is identified as a filament-forming mechanism. Additionally, the temperature dependence of the leakage current is measured, demonstrating that the temperature behavior is strongly influenced by the ambient atmosphere. The latter dependency leads to the hypothesis that the oxygen evolution reaction of water incorporated in the IrO2/TiO2 interface passivates vacancies, thus significantly impacting the vacancy density in TiO2 and, as a further consequence, the electrical performance. [ABSTRACT FROM AUTHOR]