Search Results (102)

Drawing inspiration from biological brains’ energy-efficient information-processing mechanisms, photonic integrated circuits (PICs) have facilitated the development of ultrafast artificial neural networks. This in turn is envisaged to offer potential solutions to the growing demand for artificial intelligence employing machine learning in various domains, from nonlinear optimization and telecommunication to medical diagnosis. In the meantime, silicon photonics has emerged as a mainstream technology for integrated chip-based applications. However, challenges still need to be addressed in scaling it further for broader applications due to the requirement of co-integration of electronic circuitry for control and calibration. Leveraging physics in algorithms and nanoscale materials holds promise for achieving low-power miniaturized chips capable of real-time inference and learning. Against this backdrop, we present the State of the Art in neuromorphic photonic computing, focusing primarily on architecture, weighting mechanisms, photonic neurons, and training, while giving an overall view of recent advancements, challenges, and prospects. We also emphasize and highlight the need for revolutionary hardware innovations to scale up neuromorphic systems while enhancing energy efficiency and performance. 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

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

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

This study proposes Resource-Adaptive Differential Evolution (RADE), a novel optimization algorithm for training lightweight and interpretable dendritic neuron models (DNMs) in classification tasks. RADE introduces dynamic population partitioning, poor-individual-guided mutation, adaptive parameter control, and lightweight archiving to achieve efficient and robust learning. Inspired by biological and algorithmic symmetry, RADE leverages structural and behavioral balance in both the evolutionary process and the DNM architecture. DNMs inherently exhibit symmetric processing through multiple dendritic branches that independently and equivalently aggregate localized inputs. RADE preserves and enhances this structural symmetry by promoting balanced learning dynamics and pruning redundant dendritic components, leading to compact and interpretable neuron morphologies. Extensive experiments on real-world and synthetic datasets demonstrate that RADE consistently outperforms existing methods in terms of classification accuracy, convergence stability, and model compactness. Furthermore, the resulting neuron structures can be mapped to logical circuits, making the RADE-DNM highly suitable for neuromorphic and edge computing applications. This work highlights the synergistic role of symmetry in achieving resource-efficient and transparent artificial intelligence. 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

Several materials widely used in scientific research and industrial applications, including nano-filters and neuromorphic circuits, consist of fiber structures. Despite the fundamental structural similarity, the key feature that should be considered depends on the specific application. In the case of membranes and filters, the main concern has been on the pores among fibers, whereas in neuromorphic networks the main functionality is performed through the junctions of nanowires simulating neuron synapses for information dissemination. Precise metrological characterization of these structural features, along with methods for their effective control and replication, is essential for optimizing performance across various applications. This paper presents a comprehensive metrological framework for characterizing the spatial point patterns formed by pores or junctions within fibrous materials. The aim is to probe the influence of fiber randomness on both the point patterns of intersections (ppi) and pores (ppp). Our findings indicate a strong tendency of ppi toward aggregation, contrasting with a tendency of ppp toward periodicity and consequent pore uniformity. Both patterns are characterized by peculiarities related to collinearity effects on neighboring points that cannot be captured by the conventional anisotropy analysis of point patterns. To characterize local collinearity, we develop a method that counts the number of collinear triplets of nearest neighbor points in a pattern and designs an appropriate parameter to quantify them, also applied to scanning electron microscopy (SEM) images of membranes, demonstrating consistency with simulated data. Full article

The avalanche multiplication effect, capable of significantly amplifying weak optical or electrical signals, plays a pivotal role in enhancing the performance of electronic and optoelectronic devices. This effect has been widely employed in devices such as avalanche photodiodes, impact ionization avalanche transit time diode, and impact ionization field-effect transistors, enabling diverse applications in biomedical imaging, 3D LIDAR, high-frequency microwave circuits, and optical fiber communications. However, the evolving demands in these fields require avalanche devices with superior performance, including lower power consumption, reduced avalanche threshold energy, higher efficiency, and improved sensitivity. Over the years, significant efforts have been directed towards exploring novel device architectures and multiplication mechanisms. The emergence of two-dimensional (2D) materials, characterized by their exceptional light-matter interaction, tunable bandgaps, and ease of forming junctions, has opened up new avenues for developing high-performance avalanche devices. This review provides an overview of carrier multiplication mechanisms and key performance metrics for avalanche devices. We discuss several device structures leveraging the avalanche multiplication effect, along with their electrical and optoelectronic properties. Furthermore, we highlight representative applications of avalanche devices in logic circuits, optoelectronic components, and neuromorphic computing systems. By synthesizing the principles and applications of the avalanche multiplication effect, this review aims to offer insightful perspectives on future research directions for 2D material-based avalanche devices. Full article

An ultralow-power, high-performance online-learning and anomaly-detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks—one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. The system demonstrates 87% anomaly-detection accuracy; achieves a performance of 16.1 GOPS—774× faster than the ASUS Tinker Board edge processor; and delivers an energy efficiency of 783 GOPS/W, consuming only 20.5 mW during anomaly detection. Full article

Neuromorphic devices emulating the temperature-sensing capabilities of biological thermoreceptors hold significant promise for neuron-like artificial sensory systems. In this work, Bi₂Se₃-based threshold-switching memristors were presented in constructing temperature-sensing neuron circuits, leveraging its exceptional attributes, such as high switching ratio (>10⁶), low threshold voltage, and thermoelectric response. The spiking oscillation response of the devices to resistance and temperature variations was analyzed using Hspice simulation of the memristor model based on its resistance in on/off states, threshold voltage (V_th), and hold voltage (V_hold). These results show the great potential of the Bi₂Se₃-based memristor in enabling biorealistic thermoreception applications. Full article

Memristors have emerged as prospective two-terminal elements, having applications in memory, neuromorphic systems, and analog circuits. Biological materials such as egg albumin exhibit memristive behavior, displaying a distinctive pinched hysteresis signature in their current-voltage characteristics. However, such memristive behavior must be mathematically modeled to gain insights into the material’s operation and utilize it in various circuit applications. This article proposes a novel SPICE-level framework for fabricated egg albumin memristors using Joglekar’s memristor model. Experimental current-voltage characteristics are used to calibrate the SPICE model, ensuring accurate reproducibility of the experimental results. Additionally, the impact of variations in model-specific parameters on dynamic resistance and device performance is explored. Full article

Locally active memristors with an Edge-of-Chaos kernel (EOCK) represent a significant advancement in the simulation of neuromorphic dynamics. However, current research on memristors with an EOCK remains at the circuit level, without further analysis of their feasibility. In this context, we designed a memristor and installed it in a third-order circuit, where it showed local activity and stability under defined voltage and inductance parameters. This behavior ensured that by varying the input voltage and inductance, the memristor could effectively simulate various neural activities, including inhibitory postsynaptic potential and chaotic waveforms. By subsequently integrating the memristor with an EOCK into a Hopfield neural network (HNN) framework and substituting the self-coupling weight, we observed a rich spectrum of dynamic behaviors, including the rare phenomenon of antimonotonicity bubble bifurcation. Finally, we used hardware circuits to realize these generated dynamic phenomena, confirming the feasibility of the memristor. By introducing the HNN and studying its dynamic behavior and hardware circuit implementation, this study provides theoretical insights into and an empirical basis for developing circuits and systems that replicate the complexity of human brain functions. This study provides a reference for the development and application of EOCK in the future. Full article

Metal oxide semiconductors, such as indium gallium zinc oxide (IGZO), have attracted significant attention from researchers in the fields of liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) for decades. This interest is driven by their high electron mobility of over ~10 cm²/V·s and excellent transmittance of more than ~80%. Amorphous IGZO (a-IGZO) offers additional advantages, including compatibility with various processes and flexibility making it suitable for applications in flexible and wearable devices. Furthermore, IGZO-based thin-film transistors (TFTs) exhibit high uniformity and high-speed switching behavior, resulting in low power consumption due to their low leakage current. These advantages position IGZO not only as a key material in display technologies but also as a candidate for various next-generation electronic devices. This review paper provides a comprehensive overview of IGZO-based electronics, including applications in gas sensors, biosensors, and photosensors. Additionally, it emphasizes the potential of IGZO for implementing logic gates. Finally, the paper discusses IGZO-based neuromorphic devices and their promise in overcoming the limitations of the conventional von Neumann computing architecture. 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

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