Search Results (73)

Gallium oxide (Ga₂O₃)-based memristors are gaining traction as promising candidates for next-generation electronic devices toward in-memory computing, leveraging the unique properties of Ga₂O₃, such as its wide bandgap, high thermodynamic stability, and chemical stability. This review explores the evolution of memristor theory for Ga₂O₃-based materials, emphasising capacitive memristors and their ability to integrate resistive and capacitive switching mechanisms for multifunctional performance. We discussed the state-of-the-art fabrication methods, material engineering strategies, and the current challenges of Ga₂O₃-based memristors. The review also highlights the applications of these memristors in memory technologies, neuromorphic computing, and sensors, showcasing their potential to revolutionise emerging electronics. Special focus has been placed on the use of Ga₂O₃ in capacitive memristors, where their properties enable improved switching speed, endurance, and stability. In this paper we provide a comprehensive overview of the advancements in Ga₂O₃-based memristors and outline pathways for future research in this rapidly evolving field. Full article

We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the role of 0D, 1D, and 2D materials in optimizing device performance, focusing on their unique electronic, optical, and mechanical properties. We then analyze synaptic properties such as excitatory post-synaptic current (EPSC), visual adaptation, transition from short-term to long-term plasticity (STP to LTP), nociceptor-inspired responses, and associative learning mechanisms. Finally, we highlight real-world applications, including artificial vision systems, reservoir computing for temporal data processing, adaptive neuromorphic computing for exoplanet detection, and colored image recognition. By consolidating recent developments, this paper provides insights into the potential of optoelectronic synaptic devices for next-generation computing architectures, bridging the gap between optics and neuromorphic engineering. Full article

Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has consistently become a topical issue. This review provides a comprehensive overview of methodologies for hyperspectral unmixing, from traditional to advanced deep learning approaches. A systematic analysis of various challenges is presented, clarifying underlying principles and evaluating the strengths and limitations of prevalent algorithms. Hyperspectral unmixing is critical for interpreting spectral imagery but faces significant challenges: limited ground-truth data, spectral variability, nonlinear mixing effects, computational demands, and barriers to practical commercialization. Future progress requires bridging the gap to applications through user-centric solutions and integrating multi-modal and multi-temporal data. Research priorities include uncertainty quantification, transfer learning for generalization, neuromorphic edge computing, and developing tuning-free foundation models for cross-scenario robustness. This paper is designed to foster the commercial application of hyperspectral unmixing algorithms and to offer robust support for engineering applications within the hyperspectral remote sensing domain. Full article

Halide perovskite-based memristors are promising neuromorphic devices due to their unique ion migration and interface tunability, yet their conduction mechanisms remain unclear, causing stability and performance issues. Here, we engineer interstitial Ag⁺ ions within a quasi-two-dimensional (quasi-2D) halide perovskite ((C₆H₅C₂H₄NH₃)₂Cs_n−1Pb_nI_3n+1) to enhance device stability and controllability. The introduced Ag⁺ ions occupy organic interlayers, forming thermodynamically stable structures and introducing deep-level energy states without structural distortion, which do not act as non-radiative recombination centers, but instead serve as efficient charge trapping centers that stabilize intermediate resistance states and facilitate controlled filament evolution during resistive switching. This modification also leads to enhanced electron transparency near the Fermi level, contributing to improved charge transport dynamics and device performance. Under external electric fields, these Ag⁺ ions act as mobile ionic species, facilitating controlled filament formation and stable resistive switching. The resulting devices demonstrate exceptional performance, featuring an ultrahigh on/off ratio (∼10⁸) and low operating voltages (∼0.31 V), surpassing existing benchmarks. Our findings highlight the dual role of Ag⁺ ions in structural stabilization and conduction modulation, providing a robust approach for high-performance perovskite memristor engineering. Full article

This review outlines the technological principles of neural–computer interface (NCI) construction, classifying them according to: (1) the degree of intervention (invasive, semi-invasive, and non-invasive); (2) the direction of signal communication, including BCI (brain–computer interface) for converting neural activity into commands for external devices, CBI (computer–brain interface) for translating artificial signals into stimuli for the CNS, and BBI (brain–brain interface) for direct brain-to-brain interaction systems that account for agency; and (3) the mode of user interaction with technology (active, reactive, passive). For each NCI type, we detail the fundamental data processing principles, covering signal registration, digitization, preprocessing, classification, encoding, command execution, and stimulation, alongside engineering implementations ranging from EEG/MEG to intracortical implants and from transcranial magnetic stimulation (TMS) to intracortical microstimulation (ICMS). We also review mathematical modeling methods for NCIs, focusing on optimizing the extraction of informative features from neural signals—decoding for BCI and encoding for CBI—followed by a discussion of quasi-real-time operation and the use of DSP and neuromorphic chips. Quantitative metrics and rehabilitation measures for evaluating NCI system effectiveness are considered. Finally, we highlight promising future research directions, such as the development of electrochemical interfaces, biomimetic hierarchical systems, and energy-efficient technologies capable of expanding brain functionality. Full article

We report the fabrication and characterization of Al-doped ZnO (AZO) optoelectronic synaptic devices based on sol–gel-derived thin films with varying Al concentrations (0~4.0 wt%). Structural and optical analyses reveal that moderate Al doping modulates the crystal orientation, optical bandgap, and defect levels of ZnO films. Notably, 2.0 wt% Al doping yields the widest bandgap (3.31 eV), stable PL emission, and uniform deep-level absorption without inducing significant lattice disorder. Synaptic performance, including learning–forgetting dynamics and persistent photoconductivity (PPC), is strongly dependent on Al concentration. The 2.0 wt% AZO device exhibits the lowest forgetting rate and longest memory retention due to optimized trap formation, particularly Al–oxygen vacancy complexes that enhance carrier lifetime. Visual memory simulations using a 3 × 3 pixel array under patterned UV illumination further confirm superior long-term memory (LTM) behavior at 2.0 wt%, with stronger excitatory postsynaptic current (EPSC) retention during repeated stimulation. These results demonstrate that precise doping control via the sol–gel method enables defect engineering in oxide-based neuromorphic devices. Our findings provide an effective strategy for designing low-cost, scalable optoelectronic synapses with tunable memory characteristics suitable for future in-sensor computing and neuromorphic vision systems. Full article

Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-dependent phenomena such as bistability, multistability, periodicity, quasi-periodicity, and chaos, offering a more accurate and general representation of neural adaptation. This opens the door to new insights in computational neuroscience and neuromorphic system design. Synaptic weights change in several contexts or mechanisms including, Bienenstock–Cooper–Munro (BCM) synaptic modification, where synaptic changes depend on the level of post-synaptic activity; homeostatic plasticity, where all of a neuron synapses simultaneously scale up or down to maintain stability; metaplasticity, or plasticity of plasticity; neuromodulation, where neurotransmitters influence synaptic weights; developmental processes, where synaptic connections are actively formed, pruned and refined; disease or injury; for example, neurological conditions can induce maladaptive synaptic changes; spike-time dependent plasticity (STDP), where changes depend on the precise timing of pre- and postsynaptic spikes; and structural plasticity, where changes in dendritic spines and axonal boutons can alter synaptic strength. The ability of synapses and neurons to change in response to activity is fundamental to learning, memory formation, and cognitive adaptation. This paper presents simple continuous and discrete neuro-modules with adapting feedback synapses which in turn are subject to feedback. The dynamics of continuous periodically driven Hopfield neural networks with adapting synapses have been investigated since the 1990s in terms of periodicity and chaotic behaviors. For the first time, one- and two-neuron models are considered as parameters are varied using a feedback mechanism which more accurately represents real-world simulation, as explained earlier. It is shown that these models are history dependent. A simple discrete two-neuron model with adapting feedback synapses is analyzed in terms of stability and bifurcation diagrams are plotted as parameters are increased and decreased. This work has the potential to improve learning algorithms, increase understanding of neural memory formation, and inform neuromorphic engineering research. Full article

In the era of artificial intelligence, the demand for rapid and efficient data processing is growing, and traditional computing architectures are increasingly struggling to meet these needs. Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. Despite this, memristors still face many challenges in biomimetic functionality and circuit integration. In this context, a starch–glycerol-based hydrogel memristor was developed using starch as the dielectric material. The starch–glycerol–water mixture employed in this study has been widely recognized in literature as a physically cross-linked hydrogel system with a three-dimensional network, and both high water content and mechanical flexibility. This memristor demonstrates a high current switching ratio and stable threshold voltage, showing great potential in mimicking the activity of biological neurons. The device possesses the functionality of auditory neurons, not only achieving artificial spiking neuron discharge but also accomplishing the spatiotemporal summation of input information. In addition, we demonstrate the application capabilities of this artificial auditory neuron in gain modulation and in the synchronization detection of sound signals, further highlighting its potential in neuromorphic engineering applications. These results suggest that starch-based hydrogel memristors offer a promising platform for the construction of bio-inspired auditory neuron circuits and flexible neuromorphic systems. Full article

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors—hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices. Full article

This study proposes an innovative design strategy for molybdenum disulfide (MoS₂) optoelectronic devices based on three-dimensional folded configurations. A “Z”-shaped folded MoS₂ device was fabricated through mechanical exfoliation combined with a pre-strain technique on elastic substrates. Experimental investigations reveal that the geometric folding deformation induces novel photocurrent response zones near folded regions beyond the Schottky junction area via band structure reconstruction, achieving triple polarity switching (negative–positive–negative–positive) of photocurrent. This breakthrough overcomes the single-polarity separation mechanism limitation in conventional planar devices. Scanning photocurrent microscopy demonstrates a 40-fold enhancement in photocurrent intensity at folded regions compared to flat areas, attributed to the optimization of carrier separation efficiency through a pn junction-like built-in electric field induced by the three-dimensional configuration. Voltage-modulation experiments show that negative bias (−150 mV) expands positive response regions, while +200 mV bias induces a global negative response, revealing a dynamic synergy between folding deformation and electric field regulation. Theoretical analysis identifies that the band bending and built-in electric field in folded regions constitutes the physical origin of multiple polarity reversals. This work establishes a design paradigm integrating “geometric deformation-band engineering” for regulating optoelectronic properties of two-dimensional materials, demonstrating significant application potential in programmable photoelectric sensing and neuromorphic devices. Full article

We report an ion-gel-gated amorphous indium gallium zinc oxide (a-IGZO) optoelectronic neuromorphic transistors capable of synaptic emulation in both photoelectric dual modes. The ion-gel dielectric in the coplanar-structured transistor, fabricated via ink-jet printing, exhibits excellent double-layer capacitance (>1 μF/cm²) and supports low-voltage operation through lateral gate coupling. The integration of ink-jet printing technology enables scalable and large-area fabrication, highlighting its industrial feasibility. Electrical stimulation-induced artificial synaptic behaviors were successfully demonstrated through ion migration in the gel matrix. Through a simple and controllable oxygen vacancy engineering process involving low-temperature oxygen-free growth and post-annealing process, a sufficient density of stable subgap states was generated in IGZO, extending its responsivity spectrum to the visible-red region and enabling wavelength-discriminative photoresponses to 450/532/638 nm visible light. Notably, the subgap states exhibited unique interaction dynamics with low-energy photons in optically triggered pulse responses. Critical synaptic functionalities—including short-term plasticity (STP), long-term plasticity (LTP), and paired-pulse facilitation (PPF)—were successfully simulated under both optical and electrical stimulations. The device achieves low energy consumption while maintaining compatibility with flexible substrates through low-temperature processing (≤150 °C). This study establishes a scalable platform for multimodal neuromorphic systems utilizing printed iontronic architectures. Full article

This review examines the distinct advantages of organic semiconductors over conventional insulating polymers as optically active materials in photonic applications. We analyze the fundamental principles governing their unique optical and electronic properties, from basic conjugated polymer systems to advanced molecular architectures. The review systematically explores key material classes, including polyfluorenes, polyphenylene vinylenes, and polythiophenes, highlighting their dual electrical–optical functionality unavailable in passive polymer systems. Particular attention is given to polymer blends, composites, and hybrid organic–inorganic systems, demonstrating how semiconductor properties enable enhanced performance through materials engineering. We contrast passive components with active photonic devices, illustrating how the semiconductor nature of these polymers facilitates novel functionalities beyond simple light guiding. The review explores emerging applications in neuromorphic photonics, quantum systems, and bio-integrated devices, where the combined electronic–optical properties of organic semiconductors create unique capabilities impossible with insulating polymers. Finally, we discuss design strategies for optimizing these distinctive properties and present perspectives on future developments. This review establishes organic semiconductors as transformative materials for advancing photonic technologies through their combined electronic–optical functionality. Full article

This study presents the fabrication and characterization of ZnO-MoS₂ heterostructure-based ultra-broadband photodetectors capable of operating across the ultraviolet (UV) to mid-infrared (MIR) spectral range (365 nm–10 μm). The p-n heterojunction was synthesized via RF magnetron sputtering and spin coating, followed by annealing. Structural and optical analyses confirmed their enhanced light absorption, efficient charge separation, and strong built-in electric field. The photodetectors exhibited light-controlled hysteresis in their I-V characteristics, attributed to charge trapping and interfacial effects, which could enable applications in optical memory and neuromorphic computing. The devices operated self-powered, with a peak responsivity at 940 nm, which increased significantly under an applied bias. The response and recovery times were measured at approximately 100 ms, demonstrating their fast operation. Density functional theory (DFT) simulations confirmed the type II band alignment, with a tunable bandgap that was reduced to 0.20 eV with Mo vacancies, extending the detection range. The ZnO-MoS₂ heterostructure’s broad spectral response, fast operation, and defect-engineered bandgap tunability highlight its potential for imaging, environmental monitoring, and IoT sensing. This work provides a cost-effective strategy for developing high-performance, ultra-broadband, flexible photodetectors, paving the way for advancements in optoelectronics and sensing technologies. 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

Biological neural circuits are based on the interplay of excitatory and inhibitory events to achieve functionality. Axons form long-range information highways in neural circuits. Axon pruning, i.e., the removal of exuberant axonal connections, is essential in network remodeling. We propose the photocatalytic growth and chemical dissolution of gold lines as a building block for neuromorphic computing mimicking axon growth and pruning. We predefine photocatalytic growth areas on a surface by structuring titanium dioxide (TiO₂) patterns. Placing the samples in a gold chloride (HAuCl₄) precursor solution, we achieve the controlled growth of gold microstructures along the edges of the indium tin oxide (ITO)/TiO₂ patterns under ultraviolet (UV) illumination. A potassium iodide (KI) solution is employed to dissolve the gold microstructures. We introduce a real-time monitoring setup based on an optical transmission microscope. We successfully observe both the growth and dissolution processes. Additionally, scanning electron microscopy (SEM) analysis confirms the morphological changes before and after dissolution, with dissolution rates closely aligned to the growth rates. These findings demonstrate the potential of this approach to emulate dynamic biological processes, paving the way for future applications in adaptive neuromorphic systems. Full article