Search Results (80)

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

Spiking Neural Networks (SNNs), with their event-driven and energy-efficient characteristics, have shown great promise in processing data from neuromorphic sensors. However, the sparse and non-stationary nature of event-based data poses significant challenges to optimization, particularly when using conventional algorithms such as AdamW, which assume smooth gradient dynamics. To address this limitation, we propose MemristiveAdamW, a novel algorithm that integrates memristor-inspired dynamic adjustment mechanisms into the AdamW framework. This optimization algorithm introduces three biologically motivated modules: (1) a direction-aware modulation mechanism that adapts the update direction based on gradient change trends; (2) a memristive perturbation model that encodes history-sensitive adjustment inspired by the physical characteristics of memristors; and (3) a memory decay strategy that ensures stable convergence by attenuating perturbations over time. Extensive experiments are conducted on two representative event-based datasets, Prophesee NCARS and GEN1, across three SNN architectures: Spiking VGG-11, Spiking MobileNet-64, and Spiking DenseNet-121. Results demonstrate that MemristiveAdamW consistently improves convergence speed, classification accuracy, and training stability compared to standard AdamW, with the most significant gains observed in shallow or lightweight SNNs. These findings suggest that memristor-inspired optimization offers a biologically plausible and computationally effective paradigm for training SNNs on event-driven data. Full article

This study introduces a memristive Hopfield neural network (M-HNN) model to investigate electromagnetic radiation impacts on neural dynamics in complex electromagnetic environments. The proposed framework integrates a magnetic flux-controlled memristor into a three-neuron Hopfield architecture, revealing significant alterations in network dynamics through comprehensive nonlinear analysis. Numerical investigations demonstrate that memristor-induced electromagnetic effects induce distinctive phenomena, including coexisting attractors, transient chaotic states, symmetric bifurcation diagrams and attractor structures, and constant chaos. The proposed system can generate more than 12 different attractors and extends the chaotic region. Compared with the chaotic range of the baseline Hopfield neural network (HNN), the expansion amplitude reaches 933%. Dynamic characteristics are systematically examined using phase trajectory analysis, bifurcation mapping, and Lyapunov exponent quantification. Experimental validation via a DSP-based hardware implementation confirms the model’s operational feasibility and consistency with numerical predictions, establishing a reliable platform for electromagnetic–neural interaction studies. Full article

In this study, we present a novel, six-dimensional, multistable, memristive, hyperchaotic system model demonstrating two positive Lyapunov exponents. With the maximum Lyapunov exponents surpassing 21, the developed system shows pronounced hyperchaotic behavior. The dynamical behavior was analyzed through phase portraits, bifurcation diagrams, and Lyapunov exponent spectra. Parameter b was a key factor in regulating the dynamical behavior of the system, mainly affecting the strength and direction of the influence of $z_1$ on $z_2$. It was found that when the system parameter b was within a wide range of $[13,300]$, the system remained hyperchaotic throughout. Analytical establishment of multistability mechanisms was achieved through invariance analysis of the state variables under specific coordinate transformations. Furthermore, offset boosting control was realized by strategically modulating the fifth state variable, $z_5$. The FPGA-based experimental results demonstrated that attractors observed via an oscilloscope were in close agreement with numerical simulations. To validate the system’s reliability for cybersecurity applications, we designed a novel image encryption method utilizing this hyperchaotic model. The information entropy of the proposed encryption algorithm was closer to the theoretical maximum value of 8. This indicated that the system can effectively disrupt statistical patterns. Experimental outcomes confirmed that the proposed image encryption method based on the hyperchaotic system exhibits both efficiency and reliability. 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

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

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

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

Discrete systems can offer advantages over continuous ones in certain contexts, particularly in terms of simplicity and reduced computational costs, though this may vary depending on the specific application and requirements. Recently, there has been growing interest in using fractional differences to enhance discrete models’ flexibility and incorporate memory effects. This paper examines the dynamics of the discrete memristive Hindmarsh-Rose model by integrating fractional-order differences. Our results highlight the complex dynamics of the fractional-order model, revealing that chaotic firing depends on both the fractional-order and magnetic strength. Notably, certain magnetic strengths induce a transition from periodic firing in the integer-order model to chaotic behavior in the fractional-order model. Additionally, we explore the dynamics of two coupled discrete systems, finding that electrical coupling leads to the synchronization of chaotic dynamics, while chemical coupling ultimately results in a quiescent state. Full article

Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain. Moreover, inspired by the high parallelism, fault tolerance, adaptability, and low power consumption of brain perceptual systems, replicating these efficient and intelligent systems at a hardware level will endow artificial intelligence (AI) and neuromorphic engineering with unparalleled appeal. Therefore, construction of neuromorphic devices that can simulate neural and synaptic behaviors are crucial for achieving intelligent perception and neuromorphic computing. As novel memristive devices, electrolyte-gated transistors (EGTs) stand out among numerous neuromorphic devices due to their unique interfacial ion coupling effects. Thus, the present review discusses the applications of the EGTs in neuromorphic electronics. First, operational modes of EGTs are discussed briefly. Second, the advancements of EGTs in mimicking biological synapses/neurons and neuromorphic computing functions are introduced. Next, applications of artificial perceptual systems utilizing EGTs are discussed. Finally, a brief outlook on future developments and challenges is presented. Full article

This paper presents a novel approach to in situ memristive learning by training spiking neural networks (SNNs) entirely within the circuit using memristor emulators in SPICE. The circuit models neurons using Lapicque neurons and employs pulse-based spike encoding to simulate spike-timing-dependent plasticity (STDP), a key learning mechanism in SNNs. The Lapicque neuron model operates according to the Leaky Integrate-and-Fire (LIF) model, which is used in this study to model spiking behavior in memristor-based SNNs. More exactly, the first memristor emulator in PySpice, a Python library for circuit simulation, was developed and integrated into a memristive circuit capable of in situ learning, named the “In Situ Memristive Learning Method for Pattern Classification”. This novel technique enables time-based computation, where neurons accumulate incoming spikes and fire once a threshold is reached, mimicking biological neuron behavior. The proposed method was rigorously tested on three diverse datasets: XPUE, a custom non-dominating 3 × 3 image dataset; a 3 × 5 digit dataset ranging from 0 to 5; and a resized 10 × 10 version of the Modified National Institute of Standards and Technology (MNIST) dataset. The neuromorphic circuit achieved successful pattern learning across all three datasets, outperforming comparable results from other in situ training simulations on SPICE. The learning process harnesses the cumulative effect of memristors, enabling the network to learn a representative pattern for each label efficiently. This advancement opens new avenues for neuromorphic computing and paves the way for developing autonomous, adaptable pattern classification neuromorphic circuits. Full article

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO₃/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (V_O-VCM). The voltage-controlled V_O-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of V_O-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the V_O-VCM-based Al/LiNbO₃/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device. Full article

Water-stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with the $\beta$ sheet features of silk II. Electrical measurements of SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca²⁺ ion transport phenomena as well as their trapping effect on water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain, which is quite attractive in structural health monitoring applications. The SF/cement-based composite introduces a capacitive gauge factor which surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated that the SF/cement mortars possessed memcapacitive behavior for positive potentials near +5 V, which was attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings, along with the availability and sustainability of SF, pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures. Full article

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization. Full article

Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results. Full article