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Memristor technology has grown at a breakneck pace over the last decade, with the promise to transform data processing and storage. A memristor is a non-linear electrical component with two terminals that connect electric charge and magnetic flux. The ability to store and process data in the same physical location is a fundamental benefit of memristors over traditional electrical components. It has a unique feature in that its resistance may be preset (resistor function) and then saved (memory function). Memristors, unlike other types of memory used in modern electronics, are stable and retain their state even if the device is turned off. In this work, a new highly accurate asymmetrical memristor is proposed for highly efficient analog applications. The proposed work used 5 Complementary metal–oxide–semiconductor (CMOS) devices in a parallel and series-connected manner. A bypass transistor is used to control the current flow between two terminals to perform a stable operation. A differential amplifier circuit is used to validate the proposed memristor performance. The proposed work is implemented using TSMC 45 nm CMOS technology. This application consumes less power and has good performance when compared with conventional techniques. In this work, a 1 V power supply occupies a 67.5 µm2 layout area. The experimental results are improved when compared with the existing circuit. [ABSTRACT FROM AUTHOR]

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on. [ABSTRACT FROM AUTHOR]

A Ti2AlN MAX phase layered thin film electrode and oxygen getter layer for HfO2‐based two‐terminal memristors is presented. The Ti2AlN/HfOx/Ti memristor devices exhibit enhanced resistive switching performance, including an ultra‐low reset current density (< 10−8 MΩ cm2), substantial on‐off ratio (≈ 6000), excellent multi‐level functionality (≈ 9 distinct states), impressive retention (up to 300 °C), and robust endurance (>200 million) as compared to conventional TiN and other alternative materials based memristors. Experimental measurements and modeling suggest that the distinctive combination of low thermal conductivity, high electrical conductivity, and unique ultra‐thin layer‐by‐layer structure of the Ti2AlN MAX phase thin film contribute to this exceptional performance with good reproducibility and stability. The results demonstrate for the first‐time the potential of this innovative sputtered MAX phase material for engineering energy‐efficient, high‐density non‐volatile digital, and analog memory devices aimed toward next‐generation sustainable artificial intelligence. [ABSTRACT FROM AUTHOR]

Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor's inherent stochastic switching dynamics. We demonstrate that the developed LIF neuron is capable of biological neural dynamics. We leverage these characteristics of the proposed LIF neuron by integrating it into a population-coded spiking neural network and a spiking restricted Boltzmann machine (sRBM), thereby showcasing its ability to implement probabilistic learning and inference. The sRBM achieves a software-comparable accuracy of 87.13%. Unlike CMOS-based probabilistic neurons, our design does not require any external noise sources. The designed neurons are highly energy efficient and ultra-compact, requiring only three components: a resistor, a capacitor and a memristor device. [ABSTRACT FROM AUTHOR]

In this article, we propose a circuit to imitate the behavior of a Reward-Modulated spike-timing-dependent plasticity synapse. When two neurons in adjacent layers produce spikes, each spike modifies the thickness in the shared synapse. As a result, the synapse's ability to conduct impulses is controlled, leading to an unsupervised learning rule. By introducing a reward signal, reinforcement learning is enabled by redirecting the growth and shrinkage of synapses based on signal feedback from the environment. The proposed synapse manages the convolution of the emitted spike signals to promote either the strengthening or weakening of the synapse, represented as the resistance value of a memristor device. As memristors have a conductance range that may differ from the available current input range of typical CMOS neuron designs, the synapse circuit can be adjusted to regulate the spike's amplitude current to comply with the neuron. The circuit described in this work allows for the implementation of fully interconnected layers of neuron analog circuits. This is achieved by having each synapse reconform the spike signal, thus removing the burden of providing enough power from the neurons to each memristor. The synapse circuit was tested using a CMOS analog neuron described in the literature. Additionally, the article provides insight into how to properly describe the hysteresis behavior of the memristor in Verilog-A code. The testing and learning capabilities of the synapse circuit are demonstrated in simulation using the Skywater-130 nm process. The article's main goal is to provide the basic building blocks for deep neural networks relying on spiking neurons and memristors as the basic processing elements to handle spike generation, propagation, and synaptic plasticity. [ABSTRACT FROM AUTHOR]

With the rapid developments of Artificial Intelligence (AI) and the Internet of Things (IoT), increasingly intricate and expanding application scenarios are placing higher demands on current machine vision capabilities. Therefore, there is a pressing need to simultaneously achieve diverse functionalities, simple designs, and efficient computing in vision devices. Here, the study develops a two‐terminal optoelectronic device utilizing a single 2D intrinsic defect semiconductor In2S3. The device demonstrates wavelength‐selective photodetection and neuromorphic visual capabilities, attributed to defect‐related charge‐trapping/de‐trapping processes. As a photodetector, the device exhibits a high photoresponsivity of 473.6 A W−1, a high external quantum efficiency of 1.6 × 105%, and a fast rise/fall time of 0.3/1.4 ms at the wavelength of 359 nm. As an all‐in‐one neuromorphic visual device, optoelectronic‐driven fundamental synaptic functions, including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), and “learning‐experience”, are successfully mimicked at the wavelength of 671 nm. Moreover, one‐shot recognition of the 12‐letter image “SHAN XI NORMAL” is achieved through an artificial convolutional neural network. This study provides a new strategy for developing compact high‐level intelligence systems for complex application scenarios. [ABSTRACT FROM AUTHOR]

Memristors, recognized as key devices for mitigating the von Neumann bottleneck, show great potential in non-volatile memory technology. Their performance is fundamentally linked to the chemical composition and structural characteristics of their intermediate dielectric layer. Owing to the inherent advantages of metal oxides, such as their simplistic chemical makeup and superior thermal stability, memristors featuring a Metal-Insulator-Metal (MIM) sandwich structure with metal oxides as the intermediate layer have received extensive focus. In this study, a Pt/Ni 0.5 Zn 0.5 Mn x Fe 2-x O 4 (NZMFO)/Pt composite film was prepared, and its resistance switching behavior was modulated by altering the doping concentration of Mnn+ in NZMFO. The experimental results indicate that altering the Mnn+ doping concentration does significantly impact the content of oxygen vacancies. As the concentration of Mnn+ increases from 0.10 to 0.20 at.%, the content of oxygen vacancies correspondingly rises from 46.58 % to 76.42 %. Concurrently, variations in Mnn+ doping concentration significantly alter the resistance switching properties of the Pt/NZMFO/Pt composite films. This includes elevating the set/reset voltage, diminishing the resistance ratio, and enhancing the durability with the augmentation of Mnn+ concentration. By meticulously adjusting the Mnn+ doping concentration, we can achieve a memristor characterized by superior performance across all metrics. Considering all performance aspects, this study determined that the Pt/NZMFO/Pt thin film memristor with x = 0.15 exhibits adequate endurance (100 cycles), high data retention, and stable set/reset voltage. This work underscores the critical role of Mnn+ doping concentration in optimizing the electrochemical properties of NZFO memristors, illustrating a strategic pathway to engineer memristive devices with enhanced performance for future non-volatile memory applications. [ABSTRACT FROM AUTHOR]

The logic circuit is the main component of an integrated circuit chip that dictates the operation and performance of the chip. The logic circuit based on a memristor can improve the integration and operation speed of the existing integrated circuit and reduce the chip size and the number of devices used by a single logic circuit. However, most of the research on logic circuits based on memristors has focused only on simulations, and research on the realization of logic circuits by hardware using actual memristors is limited. In this paper, a memristor based on graphene oxide with stable complementary resistive switching characteristics is fabricated, a logic circuit is built by using this device, and the logic functions of "IMP," "AND," and "NOR" are successfully realized. The complementary resistive switching device can alleviate the severe power loss caused by the memory separation of the von Neumann architecture. Moreover, its unique structure enables it to realize material logic independently without the use of multiple memristors and resistors, providing a new scheme for the physical realization of logic circuits. It also opens up a new path for integrated chips to break through von Neumann architecture. [ABSTRACT FROM AUTHOR]

Halide perovskites are at the forefront of active research in many applications, such as high performance solar cells, photodetectors, and synapses and neurons for neuromorphic computation. As a result of ion transport and ionic‐electronic interactions, current and recombination are influenced by delay and memory effects that cause hysteresis of current–voltage curves and long switching times. A methodology to formulate device models is shown, in which the conduction and recombination electronic variables are influenced by internal state variables. The models are inspired in biological frameworks of the Hodgkin–Huxley class of models. Here, the theoretical precedents, the main physical components of the models, and their application to describe dynamical measurements in halide perovskite devices are summarized. The application of several measurement methods is analyzed, as the current–voltage curves at different scan rates, the impedance spectroscopy response, and the time transients. The transition from normal (capacitive) to inverted (inductive) hysteresis, and the convergence of current–voltage curves to a stable value, are described. It is proposed that neuron‐style models capture dynamical complexity with a favorable economy of parameters, toward the identification of the dominant global dynamic processes across a wide voltage span that determines the practical response of different types of devices. [ABSTRACT FROM AUTHOR]

The energy interaction-induced firing and synchronization activities in existing neuronal networks have recently garnered attention. However, the initials-boosted extreme multistability and synchronization firings supplied by the field energy simulated with memristors have not been extensively documented. Therefore, it is of paramount importance to integrate Hamilton energy and dynamics in exploring these novel behaviors to unveil the physical essence of neuronal firing, enhance network flexibility and controllability, and reinforce the practical value of networks. To address these issues, using a memristor synapse to connect two Rulkov neurons that are exposed to memristive electromagnetic radiation, we construct a bionic memristor synapse-coupled bi-mRulkov neuron network model, known as the bi-mRulkov network. It possesses spatial equilibrium points, topological invariance, and synchronization convergence associated with the initial conditions of memristors. In particular, by establishing quantitative indicators based on the Hamilton energy and synchronization factor, the interesting boosting firing and its energy balance and transition processes are visualized from the two-dimensional plane and local dynamics. Theoretical analysis and numerical simulation demonstrate that the bi-mRulkov network can generate the boosting extreme multistability firing. Furthermore, the complex line-boosted complete synchronization and plane-boosted parallel offset synchronization firings are determined by the memristor-controlled energy balance and transition, respectively. Finally, both the PSpice-based analog circuit and microcontroller-based digital circuit platforms are developed to validate the abundant firing activities, which offers accurate implementation paradigms for the proposed neuronal network. [ABSTRACT FROM AUTHOR]

In continuous neural modeling, memristor coupling has been investigated widely. Yet, there is little research on discrete neural networks in the field. Discrete models with synaptic crosstalk are even less common. In this paper, two locally active discrete memristors are used to couple two discrete Aihara neurons to form a map called DMCAN. Then, the synapse is modeled using a discrete memristor and the DMCAN map with crosstalk is constructed. The DMCAN map is investigated using phase diagram, chaotic sequence, Lyapunov exponent spectrum (LEs) and bifurcation diagrams (BD). Its rich and complex dynamical behavior, which includes attractor coexistence, state transfer, Feigenbaum trees, and complexity, is systematically analyzed. In addition, the DMCAN map is implemented in hardware on a DSP platform. Numerical simulations are further validated for correctness. Numerical and experimental findings show that the synaptic connections of neurons can be modeled by discrete memristor coupling which leads to the construction of more complicated discrete neural networks. [ABSTRACT FROM AUTHOR]

In recent years, memristors have received widespread attention due to their inherent nonlinear characteristics. To this end, a three-dimensional memristor chaotic system based on secondary nonlinear magnetron memristors has been constructed. Through the analysis of phase portraits, 0–1 tests, Poincare sections, bifurcation diagrams, and Lyapunov exponent spectra, it was found that the system exhibits rich dynamical behaviors, including immovable points, periodic orbits, and chaos. The coexistence attractors of the system were studied, and their basins were also plotted. Comparative complexity analysis indicates that the chaotic system possesses high complexity, thus providing higher security for image encryption. The practicality of the chaotic system was verified through circuit simulation and FPGA. Finally, the chaotic system was applied to DNA image encryption. The focus was on analyzing the histogram, adjacent pixel correlation, information entropy, security, and runtime. The analysis results demonstrate that the algorithm has high security and practicality. [ABSTRACT FROM AUTHOR]

Cerium-based materials (CeO2−x) are of significant interest in the development of vacancy-modulated resistive switching (RS) memory devices. However, the influence of grain boundaries on the performance of memristors is very limited. To fill this gap, this study explores the influence of grain boundaries in cerium-based thin film resistive random-access memory (RRAM) devices. Sm0.2Ce0.8O2−x (SDC20) thin films were deposited on (100)-oriented Nb-doped SrTiO3 (NSTO) and (110)-oriented NSTO substrates using pulsed laser deposition (PLD). Devices constructed with a Pt/SDC20/NSTO structure exhibited reversible and stable bipolar resistive switching (RS) behavior. The differences in conduction mechanisms between single-crystal and polycrystalline devices were confirmed, with single-crystal devices displaying a larger resistance window and higher stability. Combining the results of XPS and I–V curve fitting, it was confirmed that defects near the grain boundaries in the SDC-based memristors capture electrons, thereby affecting the overall performance of the RRAM devices. [ABSTRACT FROM AUTHOR]

We developed three coupled van der Pol oscillators with different memristor coupling strengths. We investigate the synchronization state changing the coupling strength of one memristor. Compared with different resistor coupling strengths, amplitude death as the new synchronization state is obtained. In addition, we evaluated the synchronization dependence on the parameter of one memristor to analyze the relative phase difference, amplitude, and power consumption. [ABSTRACT FROM AUTHOR]

Memristors, being prospective work‐horses of future electronics offer various types of memory (volatile and nonvolatile) along with specific computational functionalities. Further development of memristive technologies depends on the availability of suitable materials. These materials should be easily available, stable, and preferably of low toxicity. Commonly used materials are lead halide perovskites, however, they are highly toxic and unstable under ambient conditions. Therefore a novel material is developed on the basis of bismuth iodide. In reaction with butylammonium iodide, it yields a novel compound, butylammonium iodobismuthate (BABI). Here, a diffusive memristor is introduced based on this compound and evaluates its memristive and neuromorphic properties. In contrast to nonvolatile memristors, the BABI memristors exhibit diffusive dynamics, which enable them to store the information only for short periods of time. This property is utilized to mimic the short‐term synaptic plasticity described by the leaky integrate‐and‐fire model of a biological neuron. Combined with high switching uniformity and self‐rectifying behavior, these devices show high classification accuracy for MNIST handwritten datasets, paving the way for their application in neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

A pioneering integration of oxide semiconductor memristors with optoelectronic features is presented, surpassing binary limitations to realize multi‐valued synaptic operations. Through Pt/Ga2O3/Pt memristors, their structural and electronic attributes via atomic force microscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy are explored. Demonstrating unipolar resistance switching with remarkable endurance and retention, the devices exhibit intricate light‐resistance correlations, yielding substantial photoelectric effects in distinct resistance states. Investigating synaptic behaviors, potentiation, and depression akin to biological synapses are unveiled, facilitating learning and memory processes. The standout achievement lies in attaining quaternary memory storage within a single device. Empirical data and simulations validate this concept, showcasing the potential for encoding and sustaining multiple memory states. This innovation heralds transformative possibilities, emphasizing oxide semiconductor memristors as a gateway to quaternary memory storage and enhanced synaptic functions. In essence, this work pioneers optoelectronic synaptic devices with expanded memory capabilities. [ABSTRACT FROM AUTHOR]

In‐memory computing significantly reduces workload and energy cost of data access in the traditional von Neumann computing architecture. Using various memristors, stateful logic is developed to realize in‐memory computing. However, memristors have encountered critical issues in terms of device variation and reliability, and therefore, fundamental device solutions are required to realize practical stateful logic. In this study, NAND, NOR, and half‐adder stateful logic operations of gated silicon diodes with a p+–n–p–n+ structure are demonstrated to achieve in‐memory computing. Gated diodes have bistable, steep switching, and quasi‐nonvolatile memory characteristics, enabling reliable stateful logic operation. The uniformity of their device characteristics overcomes the inherent stochastic characteristics of memristors that are widely researched for stateful logic. The sequential multiread logic operation simplifies the complex state logic scheme. In particular, a nondestructive half‐adder operation can be executed in five sequential steps using six parallel diodes. Thus, the stateful logic of gated diodes can be a potential building block for in‐memory computing. [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]

Directional ionic migration in ultra-thin metal-oxide semiconductors under applied electric fields is a key mechanism for developing various electronic nanodevices. However, understanding charge transfer dynamics is challenging due to rapid ionic migration and uncontrolled charge transfer, which can reduce the functionality of microelectronic devices. This research investigates the supercapacitive-coupled memristive characteristics of ultra-thin heterostructured metal-oxide semiconductor films at TiO2-In2O3/Au Schottky junctions. Using atomic layer deposition (ALD), we nano-engineered In2O3/Au-based metal/semiconductor heterointerfaces. TEM studies followed by XPS elemental analysis revealed the chemical and structural characteristics of the heterointerfaces. Subsequent AFM studies of the hybrid heterointerfaces demonstrated supercapacitor-like behavior in nanometer-thick TiO2-In2O3/Au junctions, resembling ultra-thin supercapacitors, pseudocapacitors, and nanobatteries. The highest specific capacitance of 2.6 × 104 F.g−1 was measured in the TiO2-In2O3/Au junctions with an amorphous In2O3 electron gate. Additionally, we examined the impact of crystallization, finding that thermal annealing led to the formation of crystalline In2O3 films with higher oxygen vacancy content at TiO2-In2O3 heterointerfaces. This crystallization process resulted in the evolution of non-zero I-V hysteresis loops into zero I-V hysteresis loops with supercapacitive-coupled memristive characteristics. This research provides a platform for understanding and designing adjustable ultra-thin Schottky junctions with versatile electronic properties. [ABSTRACT FROM AUTHOR]

This paper presents the design of a synergistic arithmetic logic unit (SALU) that combines a complementary metal-oxide–semiconductor (CMOS) and memristors using the modified gate diffusion input (m_GDI) technique. The proposed design aims to reduce power consumption, delay, and the number of transistors, which are critical parameters in digital VLSI design. We have incorporated the m_GDI technique to implement the SALU, resulting in a lower number of transistors and reduced power consumption along with the delay and power delay product (PDP). The whole proposed architecture has been constructed using a basic m_GDI cell that consists of one p-type (pMOS) and two memristors. Combinational arithmetic circuits like full adder and full subtractor have been designed using the XOR-based m_GDI approach, and their performance will double that of current methods. A 1-to-8 de multiplexer circuit has also been utilized to perform 8 different operations for the ALU. Using Cadence Virtuoso tools in 45-nm technology, we have simulated the proposed design and evaluated its performance with existing designs. The proposed design has the potential to be a viable option for low-power and high-performance digital VLSI design applications. It delivers improved power, delay, and PDP characteristics while using fewer components. The power consumption, delay, and PDP of the proposed architecture are 9.71E−6 W, 2.141E−9 s, and 2.0789E−14 J, respectively. Furthermore, the design comprises just 33 pMOS transistors and 66 memristors, which is a lower count and unique combination in contrast to previous techniques. [ABSTRACT FROM AUTHOR]

We found that a second-order ideal memristor [whose state is the charge, i.e., x = q in v = R x , i , t i ] degenerates into a negative nonlinear resistor with an internal power source. After extending analytically and geographically the above local activity (experimentally verified by the two active higher-integral-order memristors extracted from the famous Hodgkin–Huxley circuit) to other higher-order circuit elements, we concluded that all higher-order passive memory circuit elements do not exist in nature and that the periodic table of the two-terminal passive ideal circuit elements can be dramatically reduced to a reduced table comprising only six passive elements: a resistor, inductor, capacitor, memristor, mem-inductor, and mem-capacitor. Such a bounded table answered an open question asked by Chua 40 years ago: Are there an infinite number of passive circuit elements in the world? [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]

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]

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

We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications.

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

In recent years quantum dot (QDs) based resistive switching devices(memristors) have gained a lot of attention. Here we report the resistive switching behavior of nitrogen-doped graphene quantum dots/Polyvinyl alcohol (N-GQDs/PVA) degradable nanocomposite thin film with different weight percentages (wt.%) of N-GQDs. The memristor device was fabricated by a simple spin coating technique. It was found that 1 wt% N-GQDs/PVA device shows a prominent resistive switching phenomenon with good cyclic stability, high on/off ratio of ~102 and retention time of ∼104 s. From a detailed experimental study of band structure, we conclude that memristive behavior originates from the space charge controlled conduction (SCLC) mechanism. Further transient property of built memristive device was studied. Within three minutes of being submerged in distilled water, the fabricated memory device was destroyed. This phenomenon facilitates the usage of fabricated memristor devices to develop memory devices for military and security purposes. [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]

Titanium dioxide is one of the earliest materials used in memristors. It is widely studied and researched due to its advantages of the simple preparation process, high dielectric constant, wide bandgap and chemical stability. In this paper, a two-step anodization process was used to prepare the oxidized nanotubes array, which served as the foundation for fabricating Ag/TiO2/Ti memristors. The research delved into the impact of diverse oxidation voltages on the resistive performance of memristor and discussed the underlying mechanism for resultant performance. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations. [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]

Memristors are a promising option for achieving high-density storage and neuromorphic computing. However, the high-bias electroforming step and the sneak current arises in the crossbar array architecture impeded the further advancement of memristors. To overcome these obstacles, there is a need for in-depth research into the materials and fabrication processes of memristors. In this work, we have chosen the conventional titanium oxide as the functional layer and prepared the memristor with the structure of Pt/TiOx/Al by magnetron sputtering process. The conduction behavior of the Pt/TiOx/Al memristor is attributed to the Schottky emission. The device was an interface-type memristor with electroforming-free switching and self-rectifying effect. The conductance changes with different dynamic synaptic characteristics were demonstrated. The typical long-term potentiation (LTP) and long-term depression (LTD) were implemented by adjusting the pulse width, interval, or amplitude. The non-linearity can be adjusted by pulse parameters, and optimal non-linearity (0.167) were obtained by stepwise pulse stimulus. This study demonstrates that the Pt/TiOx/Al memristor has potential applications in high-density and efficient neuromorphic computing. [ABSTRACT FROM AUTHOR]

Chaotic or hyperchaotic systems have a significant role in engineering applications such as cryptography and secure communication, serving as primary signal generators. To ensure stronger complexity, memristors with sufficient nonlinearity are commonly incorporated into the system, suffering a limitation on the physical implementation. In this paper, we propose a new four-dimensional (4D) hyperchaotic system based on the linear memristor which is the most straightforward to implement physically. Through numerical studies, we initially demonstrate that the proposed system exhibits robust hyperchaotic behaviors under typical parameter conditions. Subsequently, we theoretically prove the existence of solid hyperchaos by combining the topological horseshoe theory with computer-assisted research. Finally, we present the realization of the proposed hyperchaotic system using an FPGA platform. This proposed system possesses two key properties. Firstly, this work suggests that the simplest memristor can also induce strong nonlinear behaviors, offering a new perspective for constructing memristive systems. Secondly, compared to existing systems, our system not only has the largest Kaplan-Yorke dimension, but also has clear advantages in areas related to engineering applications, such as the parameter range and signal bandwidth, indicating promising potential in engineering applications. [ABSTRACT FROM AUTHOR]

Memristors offer a promising solution to address the performance and energy challenges faced by conventional von Neumann computer systems. Yet, stochastic ion migration in conductive filament often leads to an undesired performance tradeoff between memory window, retention, and endurance. Herein, a robust memristor based on oxygen‐rich SnO2 nanoflowers switching medium, enabled by seed‐mediated wet chemistry, to overcome the ion migration issue for enhanced analog in‐memory computing is reported. Notably, the interplay between the oxygen vacancy (Vo) and Ag ions (Ag+) in the Ag/SnO2/p++‐Si memristor can efficiently modulate the formation and abruption of conductive filaments, thereby resulting in a high on/off ratio (>106), long memory retention (10‐year extrapolation), and low switching variability (SV = 6.85%). Multiple synaptic functions, such as paired‐pulse facilitation, long‐term potentiation/depression, and spike‐time dependent plasticity, are demonstrated. Finally, facilitated by the symmetric analog weight updating and multiple conductance states, a high image recognition accuracy of ≥ 91.39% is achieved, substantiating its feasibility for analog in‐memory computing. This study highlights the significance of synergistically modulating conductive filaments in optimizing performance trade‐offs, balancing memory window, retention, and endurance, which demonstrates techniques for regulating ion migration, rendering them a promising approach for enabling cutting‐edge neuromorphic applications. [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]

Memristive devices such as resistive switching memories and artificial synapses have emerged as promising technologies to overcome the technological challenges associated with the von Neumann bottleneck. Recently, lead halide perovskites have drawn substantial research attention as the candidate material for memristors and artificial synapses due to their unique optoelectronic properties, solution processability, and mechanical flexibility. However, the toxicity of lead‐containing species has raised major concerns for health and the environment, which makes it crucial to transition from lead‐based to lead‐free materials for practical applications. Herein, the recent progress of lead‐free metal halides including perovskites and perovskite analogs for resistive memory and artificial synapse is reviewed. Initially, the fundamentals of lead‐free materials and switching mechanisms are introduced. Next, the material design, fabrication technique, and device performance are summarized and critically evaluated for each metal halide species. Finally, the challenges of the lead‐free metal halides toward memristors and artificial synapses are outlined and discussed, and some potential research directions for future study are proposed. [ABSTRACT FROM AUTHOR]

This study introduces a simple memristor cellular neural network structure, a minimalist configuration with only two cells, designed to concurrently address two logic problems. The unique attribute of this system lies in its adaptability, where the nature of the implemented logic gate, be it AND, OR, and XOR, is determined exclusively by the initial states of the memristors. The memristors' state, alterable through current flow, allows for dynamic manipulation, enabling the setting of initial conditions and consequently, a change in the circuit's functionality. To optimize the parameters of this dynamic system, contemporary machine learning techniques are employed, specifically gradient descent optimization. Through a case study, the potential of leveraging intricate circuit dynamics is exemplified to expand the spectrum of problems solvable with a defined number of neurons. This work not only underscores the significance of adaptability in logical circuits but also demonstrates the efficacy of memristive elements in enhancing problem‐solving capabilities. [ABSTRACT FROM AUTHOR]

Memristors, since their inception, have demonstrated remarkable characteristics, notably the exceptional reconfigurability of their memory. This study delves into electroforming-free YMnO 3 (YMO)-based resistive switches, emphasizing the reconfigurable memory effect in multiferroic YMO thin films with metallically conducting electrodes and their pivotal role in achieving adaptable frequency responses in impedance circuits consisting of reconfigurable YMO-based resistive switches and no reconfigurable passive elements, e.g., inductors and capacitors. The multiferroic YMO possesses a network of charged domain walls which can be reconfigured by a time-dependent voltage applied between the metallically conducting electrodes. Through experimental demonstrations, this study scrutinizes the impedance response not only for individual switch devices but also for impedance circuitry based on YMO resistive switches in both low- and high-resistance states, interfacing with capacitors and inductors in parallel and series configurations. Scrutinized Nyquist plots visually capture the intricate dynamics of impedance circuitry, revealing the potential of electroforming-free YMO resistive switches in finely tuning frequency responses within impedance circuits. This adaptability, rooted in the unique properties of YMO, signifies a paradigm shift heralding the advent of advanced and flexible electronic technologies. [ABSTRACT FROM AUTHOR]

This study investigates the effect of a high-temperature annealing process on the characteristics and performance of a memristor based on a Ag/Ga2O3/Pt structure. Through X-ray diffraction analysis, successful phase conversion from amorphous Ga2O3 to β-Ga2O3 is confirmed, attributed to an increase in grain size and recrystallization induced by annealing. X-ray photoelectron spectroscopy analysis revealed a higher oxygen vacancy in annealed Ga2O3 thin films, which is crucial for conductive filament formation and charge transport in memristors. Films with abundant oxygen vacancies exhibit decreased set voltages and increased capacitance in a low-resistive state, enabling easy capacitance control depending on channel presence. In addition, an excellent memory device with a high on/off ratio can be implemented due to the reduction of leakage current due to recrystallization. Therefore, it is possible to manufacture a thin film suitable for a memristor by increasing the oxygen vacancy in the Ga2O3 film while improving the overall crystallinity through the annealing process. This study highlights the significance of annealing in modulating capacitance and high-resistive/low-resistive state properties of Ga2O3 memristors, contributing to optimizing device design and performance. This study underscores the significance of high-temperature annealing in improving the channel-switching characteristics of Ga2O3-based memristors, which is crucial for the development of low-power, high-efficiency memory device. [ABSTRACT FROM AUTHOR]

In-memory computing electronic components offer a promising non-von Neumann strategy to develop energy-efficient and high-speed hardware systems for artificial intelligence (AI). However, the implementation of conventional electronic hardware demands a huge computational and power budget, thereby limiting their wider application. In this work, we propose a novel superconducting in-memory computing architecture by coupling the memristor device. Leveraging the phase transition of the superconductor induced by external applied Joule power, we can modulate the state of the bottom superconductor based on memristor resistive states and applied voltages, enabling the execution of in-memory computing operations. We then successfully implement vector-matrix multiplication of input and output signals within the designed array, facilitating its integration into AI systems. Constructing a binarized neural network with superconductor-memristor arrays achieves a high level of accuracy, approximately 97%, in handwritten number classification. Through an evaluation of power consumption in our proposed architecture, we find a remarkable ∼48 400× advantage in power efficiency compared to typical memristor systems. This marks the inaugural demonstration of a superconducting in-memory computing architecture through memristor coupling, offering a promising hardware platform for various AI systems with superior energy efficiency and computing capacity. [ABSTRACT FROM AUTHOR]

In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system's attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation. [ABSTRACT FROM AUTHOR]

Memristor is an electronic device, which corresponds to a switch translating memory data (synapse) into resistance values. Scientists made a switch with a combination of titanium dioxide (Tio2) and oxygen deficient TiO2 to create a metal-insulator transition mechanism to make a device with nonlinear conductive states Ron and Roff. This work presents the memory storing capability of memristor by utilizing an equivalent experimental setup of steel balls array. An experiment exhibits an identical memristive mechanism of memristor device where the nonlinear conductive states along an array of steel balls describes an exact mechanism of memristor's functionality. Via utilizing an identical setup, we depict electric and magnetic field compatibility at memristor's pinched (on & off) regions. It shows that how memristor stores synaptic information by means of resistance values at its pinched (on & off) conducting regions. The aim of this effort is to provide technical support to conceive a memristor as a resistive memory storage device, which changes its resistance values with respect to applied voltage (multiple synaptic weights). [ABSTRACT FROM AUTHOR]

There is a need to design a hardware synapse array appropriate for enhancing the efficiency of neuromorphic computing systems while minimizing energy consumption. This study introduces a memristor device with an AlOx overshoot suppression layer (A‐OSL) to achieve a self‐compliance effect. By optimizing each cell within the 16 × 16 crossbar array, synaptic devices are successfully fabricated with reliable characteristics and 3‐bit multilevel capabilities. In addition, the oxygen composition of TiOx and the annealing conditions are optimized to reduce the forming voltage and minimize the variation in the switching voltage. As a result, stable forming‐free characteristics are obtained through A‐OSL insertion, a reduction in forming voltage, and TiOx oxygen composition optimization. Also, target weights are accurately transferred to the A‐OSL memristor crossbar array and conducted the inference process by applying spike signals to the array following the designated time step. The spiking neural network (SNN) is demonstrated by measuring vector‐matrix multiplication (VMM) of the 16 × 16 crossbar array. The VMM results exhibit a classification accuracy of 90.80% for the MNIST dataset, which is close to the accuracy achieved by software‐based approaches, amounting to 91.85%. [ABSTRACT FROM AUTHOR]

Memristors as non-volatile memory devices have been recognized for executing in-memory computation in neuromorphic hardware. In this paper, a multilayer neural network has been developed with memristor-bridges as electrical synapses and trained with modified-chip-in-the-loop technique for an image classification task. Modeling the ideal conduction behavior of memristors by their device-physics inspired analytical model has yielded satisfactory performance. However, repeated voltage cycling degrades the resistance window of memristors by aggregating conductive residuals in filamentary memristors. Therefore, emulation of such nonideality has demonstrated compromised results. To improve the performance, refresh pulses have been introduced to the devices in between write pulses to eradicate the fundamental reason of the degradation — i.e., the residuals. It has been observed that improvement of performance is contingent upon the refreshment frequency, and frequent refreshment has the ability to restore performance to a level closely approaching its ideal emulation. [ABSTRACT FROM AUTHOR]