Search Results (162)

Lead sulfide quantum dots (PbS QDs), as a functional-layer coating, enable non-volatile integration and neuromorphic computing in memristive structures to address the von Neumann bottleneck. Herein, the dual-interface mechanism of PbS QDs in the memristor film structure is reviewed. First, the local electric field enhancement effect generates tip electrode-like structures in the coating film through QD-mediated spatial charge gradients, thereby enabling precise control over the nucleation and growth of conductive filaments (CFs). As a result, the consistency of switching voltages and the thermal stability at elevated temperatures are significantly improved. Conversely, the anion reservoir effect exploits surface dangling bonds on QDs to efficiently capture anions from the dielectric layer, thereby synergistically regulating vacancy migration kinetics. This process enables zero-initialization behavior and ultra-low-power operation. In addition, the spatial distribution design and density modulation of QDs further reinforce both mechanisms. The structural optimization of QD/dielectric interface engineering can simultaneously improve cycling endurance and resistive switching uniformity. Furthermore, modification of QD surface chemistry through ligand decoration and passivation suppresses the stochasticity of ionic diffusion while improving the linearity of synaptic weight updates. This interfacial engineering strategy utilizing QDs as coating films advances the development of high-performance photonic–electronic systems for memory–computing convergence. Full article

The growing need for secure image transmission across public networks requires robust encryption algorithms. Traditional chaos-based image ciphers typically have a small key space, weak avalanche behavior, or are susceptible to differential cryptanalysis. To overcome such inadequacies, this paper suggests a new adaptive image cryptosystem that combines a fractional-order memristive chaotic engine and a non-linear hybrid encryption kernel. The system uses piano-inspired feedback; the keystream generator dynamically adapts to the previously encrypted pixel, enabling powerful Cipher Block Chaining (CBC)-style chaining and content-dependent diffusion. A four-dimensional memristive system is solved by the use of fractional-order calculus, which gives an ultra-large key space (>10⁸⁰) and very high sensitivity to initial conditions—confirmed by a positive largest Lyapunov exponent (1.7199). The encryption kernel maps the traditional Exclusive OR (XOR) with the reversible two-step operation: the modular addition of the plaintext with the first keystream byte and the XOR with the second keystream one, both of which increase non-linearity and confusion. Large-scale experiments with six standard 256 × 256 colour images indicate almost ideal entropy (7.9994), Number of Pixel Change Rate (NPCR) which is 99.62, Unified Average Changing Intensity (UACI) which is 33.43, correlation coefficients are near to zero, very low Gray-Level Co-occurrence Matrix (GLCM) homogeneity (≈0.017) and high contrast (≈4843) and low energy (≈0.006 The ciphertext passes seven National Institute of Standards and Technology (NIST) SP-800-22 statistical tests, is extremely sensitive to keys (a perturbation of 1 × 10⁻¹⁴ alters >99.6% of ciphertext) and resists chosen-plaintext and known-plaintext attacks. Decryption has linear time complexity O(N), and average encryption and decryption times are 3.40 s and 2.75 s for 256 × 256 images. The proposed cryptosystem provides an attractive security–performance trade-off that can be used in high-security systems like medical image protection, privacy-preserving multimedia transmission, and secure cloud storage. Full article

Organic memristive devices are promising components for neuromorphic systems. Although based on solution-processable materials, their fabrication often involves complex, resource-intensive processes. Here, we report the fabrication of organic memristive devices using aerosol jet printing to deposit both the active channel based on proprietary polyaniline-based bioink and PEDOT:PSS electrodes. Polymers printing has been carried out both on rigid and flexible substrates, the latter with the aim of demonstrating a flexible device not subjected to films delamination upon bending. By optimizing printing parameters, we achieved devices exhibiting high ON/OFF current ratios exceeding 100 and rapid switching dynamics, with performance comparable on glass and Kapton supports. Morphological and electrical characterizations revealed that channel thickness and uniformity critically influence resistive switching behavior. These findings demonstrate that aerosol jet printing enables scalable, low-material-consumption production of flexible organic memristive devices suitable for neuromorphic applications, potentially facilitating their integration into complex, energy-efficient bio-inspired circuits. Full article

Memtransistors, as multi-terminal devices with gate-tunable memristive behavior, offer new opportunities for nonlinear circuit design beyond conventional two-terminal memristors. The paper proposes a novel four-dimensional chaotic oscillator by integrating a three-terminal memtransistor model with a memristor. The mathematical models of both devices are established, and their equivalent circuits are presented. Based on the memtransistor model, a chaotic circuit is constructed, and its dynamical behavior is investigated by the Lyapunov exponent spectrum, bifurcation diagram, dynamical map, and other tools. It is found that the chaotic circuit has complex nonlinear characteristics and that the phenomenon of attractor coexistence exists. Furthermore, the chaotic system is discretized by the Euler approach, and experiments on an STM32-based circuit confirm the reliability of the theoretical analysis. This work provides a hardware-validated platform for studying memtransistor-based nonlinear circuits and may find applications in chaos-based secure communication and neuromorphic computing. Full article

Nonlinear memristors frequently contribute to enhancing the dynamical richness of chaotic systems, yet their complexity and flexibility have often been overlooked. In this work, a piecewise non-smooth threshold memristor model is proposed, which is coupled as a nonlinear term into the Sprott C system, yielding a novel four-dimensional memristive chaotic dynamical system. From a theoretical perspective, stability analysis reveals that unstable index-2 saddle-focus equilibrium points are governed by the memristive piecewise parameter, and the topological invariance of the system is verified. In numerical simulations, bifurcation diagrams, Lyapunov exponents, and phase portraits are employed to reveal the mechanism of novel tunable chaotic dynamics. The results demonstrate that memristive coupling strength can induce the system to generate double-scroll, double-wing, and double-butterfly chaotic attractors; the piecewise parameter of the memristor can control the system to produce multi-structure attractors with expanded quantity, and the initial condition of the memristor can regulate the system to generate offset-boosted chaotic attractors. Finally, the novel tunable dynamics is applied to water facility image encryption. Experimental results demonstrate that the proposed algorithm possesses a key space of $2^{100}$, a correlation coefficient of only 0.0002, and information entropy close to the ideal value of eight. The NPCR and UACI reach 99.6161% and 33.4669%, respectively, the key sensitivity is up to ${10}^{-16}$, and all p-values from the NIST tests are greater than 0.01, confirming that the algorithm achieves excellent security performance. Full article

The rapid expansion of artificial intelligence has intensified the demand for hardware systems capable of emulating brain-like information processing with high accuracy, energy efficiency, and reliability. Neuromorphic computing based on memristive artificial synapses has emerged as a promising approach to overcome the limitations of conventional von Neumann architectures. Although inorganic and oxide-based synaptic memristors have been widely explored for neuromorphic systems, they often suffer from poor linearity, asymmetric potentiation/depression behavior, limited conductance states, and device variability, which restrict learning accuracy and scalability. In contrast, polymer-based memristors have gained significant attention owing to their intrinsic advantages, including mechanical flexibility, molecular tunability, controllable electronic/ionic transport, low-temperature processability, and compatibility with large-area fabrication. This review critically examines recent advances in polymer—based memristive materials and devices for achieving linear and symmetric artificial synaptic behavior. Polymer synapses are classified into pure polymer, polymer composite, and polymer-hybrid systems through a mechanism to function framework. Rather than providing a general compilation of organic memristor studies, this review analyzes how polymer chemistry, ion-migration control, trap state distribution, redox activity, electrode selection, active layer thickness, and interface engineering govern conductance update linearity, symmetry, and uniformity. Fundamental switching mechanisms, material classifications, device architectures, key synaptic characteristics, and system-level neuromorphic performance, including pattern-recognition applications, are critically discussed. By explicitly linking material and device design to conductance update fidelity, learning accuracy, training convergence, and pattern-recognition reliability, this review provides practical design guidelines and future perspectives for next-generation polymer-based neuromorphic hardware with improved linearity, symmetry, reliability, and scalability. Full article

This paper is concerned with the problems of mean-square global dissipativity and global asymptotic stability for a class of stochastic fractional-order memristive BAM neural networks with leakage terms and mixed time-varying delays, including discrete delays and distributed delays. By using differential inclusion theory, stochastic analysis, matrix measure approach, and Lyapunov stability theory combined with linear matrix inequalities (LMIs), several new sufficient conditions are derived to ensure the mean-square global dissipativity and global asymptotic stability of the considered system. Compared with the existing results, the obtained stability and dissipativity criteria are less conservative due to the adoption of matrix measure and fractional-order differential inequalities. The proposed model simultaneously incorporates stochastic perturbations, memristive discontinuity, leakage effects, and mixed delays, which makes it more consistent with actual engineering scenarios such as pattern recognition and intelligent control. Finally, a numerical example is provided to demonstrate the effectiveness and correctness of the theoretical results. Full article

Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article

Pneumatic artificial muscles exhibit pronounced hysteresis in the force-contraction domain, which complicates accurate force modeling under pressure-dependent operation. This work presents a discrete-time quasi-static hysteresis model for a compact pneumatic artificial muscle using a memristive system-based branch-memory formulation. The model combines separate loading and unloading force surfaces through a bounded internal state and is evaluated on experimental data acquired at a force-change rate of $4\mathrm{N}/\mathrm{s}$. Measurements were performed at 13 pressure levels from 0 to 0.6 MPa in 0.05 MPa increments, with 32 unloading points and 32 loading points per pressure level and five repetitions for each operating condition. Representative branch curves were obtained by median reduction in the repeated measurements, and the loading and unloading surfaces were identified with the five-parameter Sárosi–Fabulya exponential-bilinear function. The state update parameter was evaluated over a fixed grid, and the best loop reconstruction on the present dataset was obtained for the hard-switching case $\alpha =1$. Benchmark comparisons with Prandtl–Ishlinskii, discrete Preisach, Maxwell-slip, and sampled Bouc–Wen-type models show that Preisach and Bouc–Wen provide higher loop-reconstruction accuracy. The proposed memristive formulation should not be interpreted as a best-fit benchmark model, but as a low-order global branch-memory representation that preserves pressure dependence and branch asymmetry within a single analytical framework over the investigated quasi-static operating range. Full article

Spin–Orbit Torque Magnetic Random-Access Memory (SOT-MRAM)-based in-memory computing (IMC) offers a transformative solution for energy-efficient edge intelligence, yet the deployment of robust online learning remains challenging due to memristive non-idealities and the “memory wall” inherent in Von Neumann architectures. In this work, we propose a Tiki-Taka-inspired SOT-MRAM IMC architecture with a cross-layer co-design that decouples gradient accumulation and weight storage. A time-multiplexing (Time-MUX) strategy is introduced to alleviate interconnect overhead, achieving over 28 times area reduction while maintaining comparable energy consumption to spatial mapping schemes. By integrating a canted-type x SOT-MTJ compact model into the NeuroSim framework, we perform system-level evaluation under realistic constraints. The results reveal that the proposed approach enables an effective trade-off between area, latency, and accuracy. Furthermore, the 1-bit/4-bit hybrid precision scheme achieves accuracy comparable to 8-bit configurations with reduced hardware cost while supporting a five-year cumulative write load of 1.87 × 10⁷ cycles, significantly surpassing the endurance limits of conventional memristor devices. This work provides key insights into cross-layer optimization and establishes a practical design framework for resource-constrained edge learning systems. Full article

Memristors, whose magnetic flux is inherently dependent on external excitation, have been widely employed to model electromagnetic induction effects in neural systems. However, when such induction mechanisms are incorporated into fractional-order neurons, the resulting nonlinear dynamics remain largely unexplored. This paper proposes a novel fractional-order memristive neural network (FO-MNN) by embedding two memristors into a single Hopfield-type neuron, both serving to characterize electromagnetic induction behavior. The complex nonlinear dynamics induced by the two memristive modules are systematically investigated. Numerical simulations reveal that, by tuning the parameters of the first memristive module, Lorenz-like double-wing butterfly attractors can be generated. When both memristive modules act simultaneously, the network exhibits highly complex multi-double-wing butterfly chaotic attractors, whose wing numbers can be flexibly adjusted via the control parameter of the second memristive module. Moreover, variations in the initial state of the second memristor lead to initial-condition-dependent coexistence of multiple double-wing butterfly attractors. These rich dynamical behaviors highlight the strong potential of the proposed FO-MNN for chaos-based engineering and security applications. Finally, a novel privacy-protection scheme for the Industrial Internet of Things (IIoT) is constructed based on the FO-MNN, and its effectiveness is validated through encryption experiments. Full article

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

To address the high redundancy and weak security inherent in satellite image transmission, this paper proposes an image encryption algorithm founded on a novel five-dimensional fractional-order cosine memristive Hopfield neural network (5D-FOCMHNN). The constructed hyperchaotic system exhibits long-term memory and multistability, capable of generating reconfigurable multi-scroll attractors. A multivariate bit-level scrambling strategy effectively disrupts pixel correlations using neuron state sequences. Furthermore, the system’s chaotic output dynamically governs DNA encoding rules, while a bidirectional diffusion mechanism ensures strong randomization and resistance to differential attacks. Comprehensive experiments demonstrate that the 5D-FOCMHNN-based scheme provides a key space of $2^{256}$, has an information entropy approaching the ideal value of 8, and exhibits robust resilience against cropping, noise, and statistical cryptanalysis, thereby providing a highly secure solution for satellite image transmission. Full article

This paper addresses the stabilization problem for a class of fractional-order memristive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays under an intermittent boundary control framework. Two scenarios are considered: systems without parametric uncertainties, for which asymptotic stability is established, and systems with uncertainties, for which robust asymptotic stability is ensured. By constructing appropriate Lyapunov functionals and employing Wirtinger-type inequalities, the fractional Razumikhin approach, and key properties of the Mittag–Leffler function, sufficient stability conditions are derived in terms of linear matrix inequalities with reduced conservatism. Furthermore, the effects of time-varying delays and control activation intervals on the stabilization performance are systematically investigated. The effectiveness and advantages of the proposed control methodology are validated through numerical simulations. Full article

Single memristive nanowire networks have emerged as a promising pathway for energy-efficient neuromorphic computing, owing to their intrinsic nonlinearity, high dimensionality, fading memory and volatile switching dynamics relevant to physical reservoir computing. While prior works focused on oxide- or silver-based network systems, these approaches face trade-offs between operating voltage, cost, stability, and scalability. This work presents a proof-of-concept demonstration of stochastic polyvinylpyrrolidone (PVP)-coated nickel nanowire networks as low-cost and scalable memristive platforms, exhibiting low-voltage resistive switching (1–2 V). The electrical characterization reveals predominantly volatile resistive switching combined with nonvolatile behavior, consistent with a filamentary conduction mechanism at nanowire junctions. The switching dynamics are governed by the polymer coating thickness, with an intermediate PVP concentration (Ni@PVP = 1:25) showing optimal performance, with a resistance ratio of ~200, stable retention over 1 h, and a reproducible endurance of over 45 cycles. These results establish Ni@PVP nanowire networks as promising memristive platforms for neuromorphic hardware applications and physical reservoir computing, with relevant properties such as fading memory and nonlinear dynamics. Full article