Search Results (284)

With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and resistance to attacks. Advances in fractional calculus and memristive technologies offer new avenues for enhancing security through more complex and tunable dynamics. However, the practical deployment of high-dimensional fractional-order memristive chaotic systems in hardware remains underexplored. This study addresses this gap by presenting a secure image transmission system implemented on a field-programmable gate array (FPGA) using a universal high-dimensional memristive chaotic topology with arbitrary-order dynamics. The design leverages four- and five-dimensional hyperchaotic oscillators, analyzed through bifurcation diagrams and Lyapunov exponents. To enable efficient hardware realization, the chaotic dynamics are approximated using the explicit fractional-order Runge–Kutta (EFORK) method with the Caputo fractional derivative, implemented in VHDL. Deployed on the Xilinx Artix-7 AC701 platform, synchronized master–slave chaotic generators drive a multi-stage stream cipher. This encryption process supports both RGB and grayscale images. Evaluation shows strong cryptographic properties: correlation of $-6.1081\times {10}^{-5}$, entropy of 7.9991, NPCR of 99.9776%, UACI of 33.4154%, and a key space of $2^{1344}$, confirming high security and robustness. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

In this work, research on liquid-based resistive switching devices is carried out, using bottom printable electrodes fabricated from Silver (Ag) paste and silver nitrate (AgNO₃) solution. The self-crossing I-V curves are observed and repeatedly shown by applying 100 sweep cycles, demonstrating repeatability and stability. This liquid device can be refreshed by adding extra droplets of AgNO₃ so that self-crossing I-V hysteresis with up to 493 dual sweeps can be obtained. The ability to be refreshed by supplying a new liquid solution demonstrates an advantage of liquid-based memristive devices, in comparison to their solid counterparts, where the switching layer is fixed after fabrication. The switching mechanism is attributed to Ag migration in the liquid, which narrows the gap between electrodes, giving rise to the observed phenomenon. The devices further show some synaptic properties including excitatory post-synaptic current (EPSC) and potentiation-depression, presenting opportunities to utilize the devices in mimicking some functions of biological neurons. The simplicity and cost-effectiveness of these devices may advance research into fluidic memristors, in which devices with versatile forms and shapes could be fabricated. Full article

This study presents transistor-level simulation results for a novel memristor emulator circuit. The design incorporates an inverter and a current-mode-controlled operational transconductance amplifier to stabilize the output voltage. Transient performance is evaluated across a 20 MHz to 100 MHz frequency range. Simulations using 0.18 μm TSMC technology confirm the circuit’s functionality, demonstrating a power consumption of 0.1 mW at a 1.2 V supply. The memristor model’s reliability is verified through corner simulations, along with Monte Carlo and temperature variation tests. Furthermore, the emulator is applied in a Memristive Integrate-and-Fire neuron circuit, a CMOS-based system that replicates biological neuron behavior for spike generation, enabling ultra-low-power computing and advanced processing in retinal prosthesis applications. Full article

Memristors with resistive switching capabilities are vital for information storage and brain-inspired computing, making them a key focus in current research. This study demonstrates non-volatile analog resistive switching behavior in Al/TiO_x/TiN/Si(n⁺⁺)/Al memristive devices. Analog resistive switching offers gradual, controllable conductance changes, which are essential for mimicking brain-like synaptic behavior, unlike digital/abrupt switching. The amorphous titanium oxide (TiO_x) active layer was deposited using the pulsed-DC reactive magnetron sputtering technique. The impact of increasing the oxide thickness on the electrical performance of the memristors was investigated. Electrical characterizations revealed stable, forming-free analog resistive switching, achieving endurance beyond 300 DC cycles. The charge conduction mechanisms underlying the current–voltage (I–V) characteristics are analyzed in detail, revealing the presence of ohmic behavior, Schottky emission, and space-charge-limited conduction (SCLC). Experimental results indicate that increasing the TiO_x film thickness from 31 to 44 nm leads to a notable change in the current conduction mechanism. The results confirm that the memristors have good stability (>1500 s) and are capable of exhibiting excellent long-term potentiation (LTP) and long-term depression (LTD) properties. The analog switching driven by oxygen vacancy-induced barrier modulation in the TiO_x/TiN interface is explained in detail, supported by a proposed model. The remarkable switching characteristics exhibited by the TiO_x-based memristive devices make them highly suitable for artificial synapse applications in neuromorphic computing systems. Full article

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

Memristive elements, known as memristors, memcapacitors and meminductors, have become an important topic of research in the electronics world in recent years. As there is still no efficient way to manufacture two-terminal memristive elements, many researchers have focused their efforts on designing emulator circuits that mimic these devices. In this study, a memcapacitor emulator circuit using Current Derivative Buffered Amplifier (CDBA) is proposed, which has significant advantages such as wide dynamic range, differential structure at the input port, high sloping rate and wide bandwidth. The main advantages of the emulator are that it is floating without grounding constraint, it is electronically adjustable, it has charge-controlled incremental and decremental modes and it has a simpler circuit structure since it does not contain a memristor. To ensure the integrity of the circuit theory, the results of the mathematical model and the simulation of the memcapacitor are given together. In addition, the characteristics of the experimentally investigated memcapacitor emulator are in good agreement with the simulation results. To provide an illustration of the performance of the proposed emulator, firstly the second-order active low-pass filter circuit and subsequently the amoeba learning circuit are selected as the working environment. The results show that the filtering performance of the proposed emulator at a value after the cut-off frequency in the filter circuit is $25\%$ more efficient than a standard capacitor and in terms of power consumption, it consumes $27.93\%$ less power than a standard capacitor. Moreover, the emulator successfully accomplishes the learning and data storage tasks in the amoeba learning circuit. Full article

This paper presents a dual-band bandpass filter with passband switchability controlled by using memristors. The memristor is a good choice as a control element due to its characteristics, such as low-power consumption, no bias needed, good electrical characteristics, and no moving parts. The filter’s reconfigurability is achieved by using memristors to selectively connect filter elements to ground. For the filter realization, multilayer technology with quasi-lumped elements has been chosen because of filter size miniaturization. Circuit-level simulations were initially used for quick analysis, followed by 3D EM simulations to validate the expected functionality of the proposed design concept. The results confirm the feasibility of a very small dual-band bandpass filter with independently controllable passbands. The frequency response of each of the two passbands (3.5 GHz and 5.8 GHz) can be tuned with negligible impact on the other passband by controlling the states of the memristors. The filter footprint area is equal to 0.10 λ_g × 0.12 λ_g, where λ_g is the guided wavelength at 3.5 GHz. Full article

Devices with tunable characteristics and parameters are used in many technical fields. Such devices can be based on memristors, which serve as programmable potentiometers. The quality of the tuning is higher by means of memristors than with mechanical and digital potentiometers. We investigate a bandpass filter in the form of an active Wien bridge with a memristor. The filter is analyzed with the help of the nodal voltage method. The dependence of the resonance frequency on the parameters of the Wien circuit, the dependence of the quality factor, and the filter gain at resonant frequency on the parameters of the voltage divider are obtained. The dependences of the resonant frequency, quality factor, and gain at the resonant frequency on the parameters of the Wien filter were formed. The tuning of the main frequency features (the filter gain, quality factor, and resonance frequency) is shown to be independent. Under different values of memristance, the frequency features result from a simulation in LTspice. These features are less than 1 percent different from the corresponding features obtained analytically. Thus, the high precision of modeling and tuning of the frequency characteristics of the memristive Wien filter is demonstrated. Full article

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems. Full article

By embedding an aluminum-laminated layer within La₂O₃ thin films and subjecting them to high-temperature rapid thermal annealing, a La₂O₃/LaAl_xO_y/La₂O₃ sandwich dielectric was formed. This structure enhances the interface properties with both the silicon substrate and the metal gate electrode, improving current conduction. Comprehensive analysis using X-ray Photoelectron Spectroscopy (XPS) revealed that this novel process not only facilitates the formation of a high-quality lanthanum aluminate layer, as indicated with Al 2p peak at 74.5 eV, but also effectively suppresses silicate layer growth, as supported by the weak Si-O signal from both the Si 2s (153.9 eV) and O 1s (533 eV) peaks at the dielectric/Si interface in the Al-laminated samples. Fourier Transform Infrared (FTIR) spectroscopy revealed a significant reduction in the OH absorption peak at 3608 cm⁻¹ OH-related band centered at 3433 cm⁻¹. These improvements are attributed to the aluminum-laminated layer, which blocks oxygen and hydroxyl diffusion, the LaAl_xO_y layer scavenging interface silicon oxide, and the consumption of oxygen during LaAl_xO_y formation under thermal annealing. Electrical measurements confirmed that the dielectric films exhibited significantly lower interface and oxide trap densities compared to native La₂O₃ samples. This approach provides a promising method for fabricating high-quality lanthanum-based gate dielectric films with controlled dielectric/substrate interactions, making it suitable for nano-CMOS and memristive device applications. Full article

In a biological nervous system, neurons are connected to each other via synapses to transmit information. Synaptic crosstalk is the phenomenon of mutual interference or interaction of neighboring synapses between neurons. This phenomenon is prevalent in biological neural networks and has an important impact on the function and information processing of the neural system. In order to simulate and study this phenomenon, this paper proposes a memristor model based on hyperbolic tangent function for simulating the activation function of neurons, and constructs a three-neuron HNN model by coupling two memristors, which brings it close to the real behavior of biological neural networks, and provides a new tool for studying complex neural dynamics. The intricate nonlinear dynamics of the MHNN are examined using techniques like Lyapunov exponent analysis and bifurcation diagrams. The viability of the MHNN is confirmed through both analog circuit simulation and FPGA implementation. Moreover, an image encryption approach based on the chaotic system and a dynamic key generation mechanism are presented, highlighting the potential of the MHNN for real-world applications. The histogram shows that the encryption algorithm is effective in destroying the features of the original image. According to the sensitivity analysis, the bit change rate of the key is close to 50% when small perturbations are applied to each of the three parameters of the system, indicating that the system is highly resistant to differential attacks. The findings indicate that the MHNN displays a wide range of dynamical behaviors and high sensitivity to initial conditions, making it well-suited for applications in neuromorphic computing and information security. Full article

Perovskite quantum dots (PVK QDs) are gaining significant attention as potential materials for next-generation memory devices leveraged by their ion dynamics, quantum confinement, optoelectronic synergy, bandgap tunability, and solution-processable fabrication. In this review paper, we explore the fundamental characteristics of organic/inorganic halide PVK QDs and their role in resistive switching memory architectures. We provide an overview of halide PVK QDs synthesis techniques, switching mechanisms, and recent advancements in memristive applications. Special emphasis is placed on the ionic migration and charge trapping phenomena governing resistive switching, along with the prospects of photonic memory devices that leverage the intrinsic photosensitivity of PVK QDs. Despite their advantages, challenges such as stability, scalability, and environmental concerns remain critical hurdles. We conclude this review with insights into potential strategies for enhancing the reliability and commercial viability of PVK QD-based memory technologies. Full article

In this paper, a global amplitude-controlled discrete hyperchaotic memristive map is designed utilizing the hyperbolic tangent function. This map exhibits fixed points arranged in a line along the y-axis, and the stability distributions of these fixed points are delineated based on variations in both the initial conditions of the map and the parameter plane. The dynamic characteristics of the map were examined through the analysis of its 2D dynamics and the largest Lyapunov exponent (LE) distribution. The existence of multistability was robustly confirmed through a comprehensive analysis of the basin of attraction, the spectra of LE that depend on initial values, bifurcation diagrams, and trajectory plots. Additionally, the amplitude of the map can be adjusted both globally and locally through manipulation of the non-bifurcation parameter. Subsequently, a digital circuit powered by a microcontroller was designed to embody the map. In comparison to recent maps, the newly devised map exhibits superior efficacy in the realm of image encryption applications. Full article

At the end of the 20th century, the rapid development of brain-like dynamics was attributed to the excellent modeling of numerous neurons and neural systems, which effectively simulated biological behaviors observed in the human brain. With the continuous advancement of research, memristive neural networks (MNNs) have been extensively studied. In recent years, the exploration of fractional-order MNNs (FMNNs) has attracted research interest, leading to the discovery of the system’s dynamical phenomena, including transient chaos, hyperchaos, multi-stability, and the coexistence of attractors. To facilitate comparative research and learning, a review of the newly proposed fractional-order chaotic system models in recent years is urgently needed. In this review, we first introduce the basic theoretical knowledge of chaotic dynamics, artificial neural networks, fractional order, and memristors. Then, we mathematically describe the fractional-order systems and detail the highly regarded FMNNs in recent years, making comparative discussions and studies. Finally, we discuss the application of these models across diverse domains and propose thought-provoking questions and future research directions. Full article