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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]

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]

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

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

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

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

Neuromorphic computing, inspired by the human brain’s architecture, is expected to break the physical limits of transistors and von Neumann bottleneck. The multiple internal state variables of higher-order memristors (second-order or above) possess dynamic complexity and adaptability, enabling them to mimick the characteristics of biological neurons, which are very important building blocks for neuromorphic computing. This paper presents a simple neuron circuit containing a single second-order current-controlled locally-active memristor (LAM). The pinched hysteresis loop and DC V–I curve of the proposed second-order LAM show good odd symmetry. Applying small signal analysis method, we obtain the small-signal equivalent circuit of the neuron circuit, showing a LC parallel structure and an edge of chaos kernel in its locally-active domain. Also, we draw a parameter classification of the neuron circuit, showing four symmetrical edge of chaos domains, which plays an important role in biphasic action potentials. Finally, we demonstrate that the simple neuron circuit can produce monophasic action potentials, biphasic action potentials and co-existing neuromorphic phenomena via subcritical Hopf bifurcation with different input, verifying the simple circuit is suitable as artificial neurons. [ABSTRACT FROM AUTHOR]

The pursuit of advanced brain‐inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin‐film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3‐based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility. [ABSTRACT FROM AUTHOR]

The advancement of digital technology has spurred the proliferation of data, creating a pressing need for efficient storage solutions. In this regard, memristors have emerged as promising contenders as next‐generation non‐volatile memory, offering superior electrical capabilities. Among memristor materials, lead halide perovskites have garnered significant interest due to their tuneable properties, facile synthesis methods, and remarkable resistive switching (RS) performance. However, concerns over environmental toxicity and stability remain. This issue has been addressed by switching to copper (Cu) based perovskites that exhibit wide bandgap semiconducting properties with zero toxicity and long‐term stability. In this manuscript, a novel approach is investigated to achieve iodine‐enriched Cs–Cu–I perovskite thin films for memristor applications. Through strategic functionalization of synthesis processes, superior optoelectronic properties are achieved in the thin films. This modified Cs–Cu–I based memristor (CCI device) exhibits excellent RS behavior at a low operating voltage of 0.7 V, with a long retention period of 4 × 103 s and very low power consumption of 2 × 10−9 W. The experimental realization of the synaptic behavior from the modified CCI device is demonstrated through its spike‐rate‐dependent plasticity (SRDP) behavior. The device response to short‐ and long‐term pulses experimentally verifies the Atkinson and Shiffrin psychological model for training the human brain. This work not only highlights the potential of the modified CCI devices for RRAM but also paves the way for improved lead‐free perovskite‐based neuromorphic devices for biological applications. [ABSTRACT FROM AUTHOR]

The possibility to use ferroelastic materials as components of neuromorphic devices is discussed. They can be used as local memristors with the advantage that ionic transport is constraint to twin boundaries where ionic diffusion is much faster than in the bulk and does not leak into adjacent domains. It is shown that nano-scale ferroelastic memristors can contain a multitude of domain walls. These domain walls interact by strain fields where the interactions near surfaces are fundamentally different from bulk materials. We show that surface relaxations (~image forces) are curtailed to short range dipolar interactions which decay as 1/d² where d is the distance between domain walls. In bigger samples such interactions are long ranging with 1/d. The cross-over regime is typically in the range of some 200-1500 nm using a simple spring interaction model. [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]

With the advancement of artificial intelligence technology, memristors show great potential in serving as artificial synapses to imitate diverse biological learning behaviors. In this study, we present the implementation of an artificial nociceptor using a threshold switching memristor device based on zinc oxide. The memristor device with threshold switching demonstrates volatile resistance switching properties, offering an on/off ratio of 105. This feature enables the emulation of learning patterns observed in biological synapses. The synaptic plasticity of the Al/ZnO/FTO memristor encompasses spike rate-dependent plasticity, paired-pulse facilitation, spike timing-dependent plasticity, and long-term potentiation/depression. In addition, the threshold switching memristor consisting of Al/ZnO/FTO has a similar function to that of a nociceptor, which is a specialized receptor found in sensory neurons responsible for detecting harmful stimuli and transmitting signals to the central nervous system for protective measures. The device demonstrates several important characteristics resembling nociceptors, such as the occurrence of threshold, relaxation, and sensitization phenomena associated with allodynia and hyperalgesia. These responses are influenced by factors, such as the intensity, duration, and frequency of the external stimuli. This study demonstrates the potential use of this device in alarm systems for humanoid robots, specifically designed to enhance their artificial sensory capabilities. [ABSTRACT FROM AUTHOR]

In this work, spherical-like ZnO nanoparticles (NPs) were synthesized via avocado seed extract and calcined at 400 °C, which exhibited a hexagonal wurtzite structure with an average size of around 42 nm. The chemical states, organic residuals, and defects were confirmed by X-ray photoelectron spectroscopy (XPS). The ZnO NPs-based memristors exhibited remarkably stable analog switching, over 300 continuous cycles, and a wide operating window of 400. Moreover, the asymmetry in the current observed was attributed to the self-rectifying nature of this device, comprising one analog memristor and one Schottky diode (1D–1R). By controlling the peak voltage to 4 V and applying 50 bias pulses, it was possible to effectively imitate the key features of synaptic behavior, including potentiation and depression, through current modulation. [Display omitted] • The Cr/ZnO/FTO memristors based on green synthesized ZnO NPs demonstrate stable analog resistive switching over 300 operating cycles and a 400-ON/OFF ratio window. • The self-rectifying property of the memristor originates from the Schottky diode at the Cr/ZnO junction. • The synaptic behavior showcases non-volatile properties and emulates synapse-like characteristics. [ABSTRACT FROM AUTHOR]

We demonstrate that an analog synaptic weight update can be achieved using identical pulses via gradual resistance switching of memristors based on sputter-deposited InGaZnO (IGZO) films. When a positive voltage is applied to the IGZO memristor, oxygen vacancies (V o) are ionized and electrons that serve as dopants are generated. This lowers the Schottky barrier formed at the interface and decreases the resistance. However, a negative voltage provides electrons from the Pd electrode, annihilating the ionized vacancies. As the barrier is reconstructed, the IGZO memristor exhibits a high-resistance state (HRS). As V o plays a crucial role in switching, the effect of the radio frequency (RF) power on the memristor during the IGZO deposition is examined. A physical analysis reveals that the V o concentration in the IGZO is proportional to the RF power, inducing a leaky HRS. However, at the expense of a reduced memory window in the IGZO deposited at a higher power of 200 W, the resultant low-resistance state is reliable over time. The strengthened retention allows the synaptic weights to be linearly decreased even by consecutive negative pulses. Hence, when the IGZO memristors are used for synaptic elements to construct neural networks, a higher recognition accuracy for the MNIST dataset is achieved. [ABSTRACT FROM AUTHOR]

The rapid advancement of neuromorphic computing demands innovative hardware solutions capable of efficiently mimicking the functionality of biological neural systems. In this context, dynamic memristors have emerged as promising candidates for realizing neuromorphic reservoir computing (RC) architectures. The dynamic memristors characterized by their ability to exhibit nonlinear conductance variations and transient memory behaviors offer unique advantages for constructing RC systems. Unlike recurrent neural networks (RNNs) that face challenges such as vanishing or exploding gradients during training, RC leverages a fixed‐size reservoir layer that acts as a nonlinear dynamic memory. Researchers can capitalize on their adaptable and efficient characteristics by integrating dynamic memristors into RC systems to enable rapid information processing with low learning costs. This perspective provides an overview of the recent developments in dynamic memristors and their applications in neuromorphic RC. It highlights their potential to revolutionize artificial intelligence hardware by offering faster learning speeds and enhanced energy efficiency. Furthermore, it discusses challenges and opportunities associated with integrating dynamic memristors into RC architectures, paving the way for developing next‐generation cognitive computing systems. [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]

This paper proposes a new four-dimensional chaotic system that consists of two active magnetically controlled memristors. The dynamic characteristics of the system, including equilibrium points, Lyapunov exponent spectrum, bifurcation diagram, double-parameter Lyapunov exponent, and attractor basin, are analyzed. The results indicate that the Lyapunov exponents of the system undergo abrupt changes. The bifurcation diagrams reveal the occurrence of sudden cusp bifurcations, and the diverse manifestations of two-parameter Lyapunov exponents under different parameter combinations further underscore the system's complexity and variability. This chaotic system also possesses an infinite number of equilibrium points and coexisting attractors, demonstrating multiple stable states. By adjusting parameters, multi-vortex chaotic attractors with high controllability can be generated, enabling the transition from two-vortex to four-vortex and then to multi-vortex chaotic systems. As the number of vortices increases, the system's dynamic characteristics become richer and more complex. Introducing pulse functions has realized mirrored multi-vortex scroll chaotic attractors, further enhancing the chaotic characteristics of the system. To verify the feasibility and practical value of the mathematical model, a circuit model of the system was constructed. Simulations were conducted using Multisim software, and the system was implemented as a digital circuit using FPGA to validate its feasibility. [ABSTRACT FROM AUTHOR]

The paper presents a new design for a compact memristor emulator that uses a single active component and a grounded capacitor. This design incorporates a current backward transconductance amplifier as the active element, enabling the emulation of both grounded and floating memristors in incremental and decremental modes. The paper provides an in-depth analysis of the circuit, covering ideal, non-ideal, and parasitic factors. The theoretical performance of the memristor emulator is confirmed through post-layout simulations with 180 nm generic process design kit (gpdk) technology, demonstrating its capability to operate at low voltages (±1 V) with minimal power consumption. Additionally, the emulator shows strong performance under variations in process, voltage, and temperature (PVT) and functions effectively at a frequency of 2 MHz. Experimental validation using commercially available integrated circuits further supports the proposed design. [ABSTRACT FROM AUTHOR]

Neuromorphic computing aims to leverage physical phenomena of adaptive materials for emulating information processing capabilities and effectiveness of biological neuronal circuits. In this framework, memristors (resistors with memory) based on 2D materials are demonstrated for the hardware implementation of highly integrated artificial neural networks. All the works reported thus far exploited electrical properties of 2D materials to emulate neuromorphic functionalities. Here, a 2D memitter (emitter with memory) is reported on that exploits the stimuli‐responsive photoluminescence of a monolayer WS2 for neuromorphic‐type of data processing. A combined experimental and modeling approach reveals that photoluminescent dynamics triggered by optical stimulation emulates Short‐Term synaptic Plasticity and Visual Short‐Term Memory typical of biological systems. While spatio‐temporal processing capabilities of input signals can be used for information processing in the context of reservoir computing, the capability of the 2D memitter of sensing, processing, and memorizing‐forgetting optical inputs in the same physical substrate can be utilized for in‐sensor computing. [ABSTRACT FROM AUTHOR]

Memristors have diverse potential for improving data storage through linear memory control and synaptic operation in AI and neuromorphic computing. Prior research on optimizing memristors in next‐generation devices has generally indicated that emerging arrays and vertical structures can improve memory density, although special fabrication steps are required to realize improved operation. Until now, many obstructions, such as the sneak path current and forming processes from the initial device in array structure operation at the device level, have limited the development of array‐based memristor devices for further progressing circuits and integrated design. In this paper, memristor array studies are examined that have suggested solutions for sneak path current and forming operation problems at the device level. Ultimately, representative solutions are proposed to progress memristors into array structures by introducing the latest research on one diode‐one RRAM (1D1R), one selector‐one RRAM (1S1R), overshoot suppressed RRAM (OSRRAM), self‐rectifying cell (SRC), charge trap memory (CTM) and their applications. Additionally, essential details demonstrating the practical implementation of these devices in crossbar array memory are investigated. Finally, the advantages and perspectives of these array‐based memristor solutions are summarized. [ABSTRACT FROM AUTHOR]

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

The regulation of infrared (IR) thermal radiation has received widespread attention; the proposals have been made for various devices that use electrochromic materials. However, the visible appearance of the mentioned devices also changes when the IR states are altered. Herein, the memristive mechanism is utilized to regulate IR emissivity and design a novel transparent memristor‐type modulator featuring an Indium tin oxide (ITO)/HfO2/ITO structure. The modulator exhibits a dynamic emissivity variation of 0.38 in the IR region of 2.5–25 μm, without compromising transparency in the visible region under the application of an electric voltage. The migration of oxygen vacancies results in the modification of interface barriers and the formation of oxygen vacancy‐enriched regions, thereby enabling the modulator to exhibit resistive switching behavior and regulate IR emissivity. An array of modulators is also assembled, demonstrating the ability to independently display visible and IR information. In this work, new possibilities are presented for various applications such as multispectral displays, energy‐efficient thermoregulation, encrypted information displays, and camouflage. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel two-dimensional discrete memristor map that is based on a discrete memristor model and a sine–arcsine one-dimensional map. First, an analysis is conducted on the memristor model to understand its characteristics. Then, the model is coupled with the sine–arcsine one-dimensional map to achieve the two-dimensional discrete memristor map. Our investigation reveals the presence of coexisting attractors and hyperchaotic attractors as the bifurcation parameters vary. Numerical simulations show that the discrete memristors effectively enhance the complexity of chaos in the sine–arcsine map. Furthermore, a digital circuit is designed to experimentally verify the new chaotic system. The research results can enrich the theoretical analysis and circuit implementation of chaos. [ABSTRACT FROM AUTHOR]

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos. [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]

This work investigates the resistive switching in InGaZnO (IGZO) memristors with different electrodes, including Cu, Au, Ag, and Ti. Cu electrode device possesses the lowest switching voltage, while Au electrode device exhibits a good endurance of 100 repeated cycling tests. Ag electrode device shows a mediocre performance. As for the Ti electrode device, it has a relatively larger on/off ratio but a highest switching voltage. Corresponding mechanisms for the current conduction are explored, and models based on the experimental results are proposed to explain the RS mechanisms. Additionally, multi-value storage potential of Cu electrode device is also investigated by varying compliance current. [ABSTRACT FROM AUTHOR]

In this study, bioengineered and biosynthesized elastin-like polypeptide (ELP) was adopted for a non-volatile memory resistive switching device or a memristor. The ELP mimicked from elastin of mammal was synthetically produced in polypeptide by Escherichia coli gene recombination. They were composed of a repeating pentapeptide sequence having [Val-Pro-Gly-Val-Gly]32 sequence for bioelectronic devices with ELP showing multiple electrical resistance states between a high resistance state and a low resistance state through an applied electrical field. In addition, the ELP-coated 5-nm gold nanoparticles (Au NPs) layer was also to be biomaterials used for nanobiohybrid memristive devices. Simple metal–insulator–metal device structure and lateral electrode device with ELP layer on 5-nm Au NPs could show neuromorphic adaptive current–voltage (I–V) behavior and electrical stimulus-induced potentiation and depression in a hydrogel state. In addition, with an introduction of a neurotransmitter cortisol's specific antibody, a preliminary sensing protocol was also examined with the nanobiohybrid device. Therefore, the introduction of ELP into neuromorphic device is regarded as the cornerstone for the development of biocompatible bioelectronic devices that can be integrated into human bionics for future artificial neuromorphic format. [ABSTRACT FROM AUTHOR]

The optically gated transistor (OGT) has been previously demonstrated as a viable selector device for memristor devices, and may enable optical addressing within cross-point arrays. The OGT current–voltage response is similar to a MOSFET device, with light activating the gate instead of voltage. The OGT also provides a naturally built-in compliance current for a series resistive memory element, determined by the incident light intensity on the gate, thus keeping the integrated periphery circuitry size and complexity to a minimum for a memory array. The OGT gate comprises an amorphous Ge2Se3 material that can readily be doped with other elements to alter the transistor's electrical properties. In this work, we explore the operation of the OGT when the Ge2Se3 gate material is doped with the Group IVA elements C, Si, Sn, and Pb. The dopant atoms provide changes to the optical and electrical properties that allow key electrical properties such as the dark current, photocurrent, switching speed, and threshold voltage to be tuned. [ABSTRACT FROM AUTHOR]

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

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

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner. Homomorphic encryption contributes to eliminating security issues during the data communication for edge computing. Here, the authors propose a 1-trasistor−1-memristor array as a homomorphic encryption hardware prototype. By leveraging the stochastic set-switching behavior and analog non-volatile properties of the memristor, the complete homomorphic communication process is demonstrated. [ABSTRACT FROM AUTHOR]

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]