Search Results (137)

Work investigates the doping of molybdenum oxide (MoO_x) with tungsten (W). The successful incorporation of W into the MoO_x lattice was confirmed through X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDS). Structural and optical analysis revealed the presence of oxygen vacancies within the W-MoO_x film, which are known to facilitate resistive switching (RS) in memristive devices. Based on this, a flexible memristor with the structure PET/ITO/W-MoO_x/polymethyl methacrylate (PMMA)/Al was fabricated. PMMA was strategically introduced between the W-MoO_x layer and the aluminum electrode to modulate interfacial properties that influence RS behavior. The W-MoO_x/PMMA-based memristor exhibited good resistive switching characteristics, with a memory window of approximately 12 and a retention time exceeding 2 × 10⁴ s, demonstrating a non-volatile memory behavior. In the high-resistance state (HRS), the conduction mechanism under higher applied voltages follows a space-charge-limited current (SCLC) model, indicating that the RS process is primarily governed by charge trapping and de-trapping at the interface. Overall, the consistent and robust switching performance of the W-MoO_x/PMMA heterostructure underlines its potential as a reliable functional layer for next-generation resistive random-access memory (ReRAM) devices. Full article

Hybrid bonding technology has emerged as a critical 3D integration solution for advanced semiconductor packaging, enabling simultaneous bonding of metal interconnects and dielectric materials. However, conventional hybrid bonding processes face significant contamination challenges during O₂ plasma treatment required for OH group formation on SiCN or the other dielectric material surfaces. The aggressive plasma conditions cause Cu sputtering and metal migration, leading to chamber and substrate contamination that accumulates over time and degrades process reliability. In this work, we present a novel approach to address these contamination issues by implementing a molybdenum disulfide (MoS₂) barrier layer formed through plasma-enhanced chemical vapor deposition (PECVD) sulfurization of Mo films. The ultrathin MoS₂ layer acts as an effective barrier preventing Cu sputtering during O₂ plasma processing, thereby eliminating chamber contamination, and it also enables post-bonding electrical connectivity through controlled Cu filament formation via memristive switching mechanisms. When voltage is applied to the Cu-MoS₂-Cu structure after hybrid bonding, Cu ions migrate through the MoS₂ layer to form conductive filaments, establishing reliable electrical connections without compromising the bonding interface integrity. This innovative approach successfully resolves the fundamental contamination problem in hybrid bonding while maintaining excellent electrical performance, offering a pathway toward contamination-free and high-yield hybrid bonding processes for next-generation 3D-integrated devices. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

This paper presents two classic analog oscillators: a relaxation oscillator and a Wien bridge one, where a memristor replaces a resistor. The circuits are simulated in TopSPICE 7.12 using a memristor emulation circuit and commercially available components to evaluate the memristor’s impact. In the case of the relaxation oscillator, which includes the memristor, a notable increase in oscillation frequency was observed compared to the classical circuit, with a nearly 10-fold increase from 790 Hz to 7.78 kHz while maintaining a constant amplitude. This confirms the influence of the memristor’s dynamic resistance on the circuit time constant. On the other hand, the Wien-bridge oscillator exhibits variations in specific parameters, such as peak voltage, amplitude, and frequency. In this case, the oscillation frequency decreased from 405 Hz to 146 Hz with the addition of the memristor, a characteristic introduced by the proposed memristive element’s nonlinear interactions. Experimental results confirm the feasibility of incorporating memristors into classical oscillator circuits, enabling frequency changes while maintaining stable oscillations, allowing reconfigurable and adaptable analog designs that leverage the properties of memristive devices. Full article

Neuromorphic computing, inspired by the human brain’s architecture, offers a transformative approach to overcoming the limitations of traditional von Neumann systems by enabling highly parallel, energy-efficient information processing. Among emerging materials, MXenes—a class of two-dimensional transition metal carbides and nitrides—have garnered significant attention due to their exceptional electrical conductivity, tunable surface chemistry, and mechanical flexibility. This review comprehensively examines recent advancements in MXene-based optoelectronic synapses and neurons, focusing on their structural properties, device architectures, and operational mechanisms. We emphasize synergistic electrical–optical modulation in memristive and transistor-based synaptic devices, enabling improved energy efficiency, multilevel plasticity, and fast response times. In parallel, MXene-enabled optoelectronic neurons demonstrate integrate-and-fire dynamics and spatiotemporal information integration crucial for biologically inspired neural computations. Furthermore, this review explores innovative neuromorphic hardware platforms that leverage multifunctional MXene devices to achieve programmable synaptic–neuronal switching, enhancing computational flexibility and scalability. Despite these promising developments, challenges remain in device stability, reproducibility, and large-scale integration. Addressing these gaps through advanced synthesis, defect engineering, and architectural innovation will be pivotal for realizing practical, low-power optoelectronic neuromorphic systems. This review thus provides a critical roadmap for advancing MXene-based materials and devices toward next-generation intelligent computing and adaptive sensory applications. Full article

Nanofluidic memristors are an emerging class of devices that harness ion transport in confined nanoscale environments to achieve tunable resistance states, mimicking biological synaptic functions. The regulation of ion migration, accumulation, and depletion in nanofluidic channels is fundamentally governed by chemical principles, including surface charge modulation, electrostatic interactions, and ion adsorption and desorption processes. This review provides a comprehensive overview of the chemical foundations of nanofluidic memristors, including electric double layer theory, ion transport dynamics, and interfacial chemistry. Additionally, this review further explores how interfacial chemical modifications, such as functionalization with charged species, pH-responsive coatings, and ionic selectivity molecules, influence nanofluidic memristive behaviors. Representative case studies are discussed to illustrate the practical implementation of these principles in applications ranging from neuromorphic computing to biosensing and energy storage. By bridging fundamental chemical theories with real-world applications, this review aims to provide insights into the rational design of next-generation nanofluidic memristive devices. Full article

Understanding the resistive switching (RS) mechanisms in memristive devices is crucial for developing non-volatile memory technologies. Here, we investigate the memristor effect in hydrothermally grown Au-nanoseeded CuO films. Based on I-V measurements, conductive-AFM, S/TEM, and EDS analyses, we examine the changes within the switching layer associated with RS. Our results reveal a filamentary mechanism of RS. Notably, EDS mapping shows directional Au redistribution between the bottom nanoseeds and the top electrode, while Cu and O remain uniformly distributed. These findings support an electrochemical metallization (ECM)-like filamentary mechanism driven by Au species migration. The use of Au-nanoseeds, required by the solution-based growth method, critically affects filament formation and RS behavior. Our results emphasize the importance of microstructure and electrode–oxide interfaces in determining the switching mechanism in oxide-based memristors. 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

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

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

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

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