Search Results (87)

Simulated spiking neural networks have been explored for over a hundred years. Many of these networks are driven by biological considerations and an attempt to simulate brains, but others are used with little biological consideration. This paper gives some history of the development of spiking neural models, their use for modelling biological and cognitive phenomena, and for machine learning. It introduces the current state of the art in computational biological neuron and synapse modelling and plasticity. It introduces and reviews balanced spiking networks and their engineering applications. Spiking networks are also used for machine learning, with the hope that their implementation on neuromorphic hardware will bring energy and time savings. Similarly, neuromorphic hardware can enable massive parallelism, supporting larger spiking networks. The use of spiking nets for machine learning, both with biologically plausible models and without, is discussed, showing that effective models already exist. The paper concludes with some notes about implementing spiking nets and a discussion including open questions and future work. Full article

In event-based neuromorphic processing, computer vision finds an efficient alternative capable of optimizing computational and energy resources, inspired by the dynamics of biological neural systems. In the development of real-time processing systems, it is crucial to visually represent the information captured by sensors and to explore its content with precision. Thus, machine learning models are implemented with the capability of being deployed on hardware devices with limited capabilities, depending on the intended purpose, ensuring savings in computational resources. The aim of this work was to evaluate the limits of the implemented neuron model, leaky-integrate and fire (LIF), for fitting convolutional layers of a neural network. To this end, the characteristics of the LIF neuron model used are summarized, as well as the details of its implementation in a hardware design, using configurable parameters. The experimental phase considered two convolution approaches to compare performance, Matlab R2022a software and a spiking convolutional processor for an FPGA, using sample recordings from the MNIST-DVS dataset and Sobel kernels for edge detection. The results reflect that the number of spikes generated by both approaches is very similar and their distribution by frame addresses is directly proportional. Full article

Monolayer ${\mathrm{MoS}}_2$ memtransistors offer gate-tunable hysteresis for neuromorphic reservoir computing, yet the role of operating window and fading-memory dynamics in CVD devices remains underexplored. We grow CVD monolayer ${\mathrm{MoS}}_2$, fabricate back-gated memtransistors, and use a single device as a time-multiplexed reservoir node for one-step Lorenz-63 prediction. Mobility, ON/OFF, hysteresis, and drift are quantified to identify stable, tunable bias regimes. We used a transistor with field-effect mobility on the order of $10 {\mathrm{cm}}^2 {\mathrm{V}}^{-1} {\mathrm{s}}^{-1}$, an ON/OFF ratio above ${10}^5$, and a moderate hysteresis window quantified by $H\approx 2.1 \mathsf{\mu }$A·V at $V_{\mathrm{DS}} = 50$ mV and $H\approx 17 \mathsf{\mu }$A·V at $V_{\mathrm{DS}} = 500$ mV over $V_{\mathrm{GS}}\in [-10,30]$ V. Performance is bias/memory-limited rather than FET-metric-limited. Sweeping gate-window and reservoir hyperparameters shows an optimum at intermediate hysteresis with moderate drift. Performance improves when the input clock matches the fading-memory time, achieving normalized root mean square error (NRMSE) = 0.09 for one-step Lorenz-63 x-prediction. Device-level statistics (discussed in the main text) show that, despite substantial scattering in electrical parameters, the resulting device-to-device NRMSE variation remains very small under fixed operating conditions. Classical FET metrics are not limiting here; NRMSE improvement instead requires engineering the hysteresis spectrum and gate stack. The demonstration of Lorenz-63 prediction using CVD-grown monolayer ${\mathrm{MoS}}_2$ memtransistors highlights their potential as a wafer-scalable platform for compact chaotic time-series predictions. Full article

Circuit implementation is a widely accepted method for validating theoretical insights observed in chaotic systems. It also serves as a basis for numerous chaos-based engineering applications, including data encryption, random number generation, secure communication, neuromorphic computing, and so forth. To get feasible, compact, and cost-effective circuit implementations of chaotic systems, the underlying mathematical model may be simplified while preserving all rich nonlinear behaviors. In this framework, this manuscript presents a simplified Hopfield Neural Network (HNN) capable of generating a broad spectrum of complex behaviors using a minimal number of electronic elements. Based on Shil’nikov’s theorem for heteroclinic orbits, the number of non-zero synaptic connections in the matrix weights is reduced, while simultaneously using only one nonlinear activation function. As a result of these simplifications, we obtain the most compact electronic implementation of a tri-neuron HNN with the lowest component count but retaining complex dynamics. Comprehensive theoretical and numerical analyses by equilibrium points, density-colored continuation diagrams, basin of attraction, and Lyapunov exponents, confirm the presence of periodic oscillations, spiking, bursting, and chaos. Such chaotic dynamics range from single-scroll chaotic attractors to double-scroll chaotic attractors, as well as coexisting attractors to transient chaos. A brief security application of an S-Box utilizing the presented HNN is also given. Finally, a physical implementation of the HNN is given to confirm the proposed approach. Experimental observations are in good agreement with numerical results, demonstrating the usefulness of the proposed approach. Full article

Hash tables embody a paradox of deterministic structure that emerges from controlled randomness. They have evolved from simple associative arrays into algorithmic engines that operate near the physical and probabilistic limits of computation. This review unifies five decades of developments across universal and perfect hashing, collision-resolution strategies, and concurrent and hardware-aware architectures. The synthesis shows that modern hash tables act as thermodynamic regulators of entropy, able to transform stochastic mappings into predictable constant-time access. Recent advances in GPU and NUMA-aware designs, lock-free and persistent variants, and neural or quantum-assisted approaches further expand their capabilities. The analysis presents hash tables as models that evolve order within randomness and expand their relevance from classical computation to quantum and neuromorphic frontiers. Full article

With the discovery of amorphous oxide semiconductors, a new era of electronics opened. Indium gallium zinc oxide (IGZO) overcame the problems of amorphous and poly-silicon by reaching mobilities of ~10 cm²/Vs and demonstrating thin-film transistors (TFTs) are easy to manufacture on transparent and flexible substrates. However, mobilities over 30 cm²/Vs have been difficult to reach and other materials have been introduced. Recently, polycrystalline In₂O₃ has demonstrated breakthroughs in the field. In₂O₃ TFTs have attracted attention because of their high mobility of over 100 cm²/Vs, which has been achieved multiple times, and because of their use in scaled devices with channel lengths down to 10 nm for high integration in back-end-of-the-line (BEOL) applications and others. The present review focuses first on the material properties with the understanding of the bandgap value, the importance of the position of the charge neutrality level (CNL), the doping effect of various atoms (Zr, Ge, Mo, Ti, Sn, or H) on the carrier concentration, the optical properties, the effective mass, and the mobility. We introduce the effects of the non-parabolicity of the conduction band and how to assess them. We also introduce ways to evaluate the CNL position (usually at ~E_C + 0.4 eV). Then, we describe TFTs’ general properties and parameters, like the field effect mobility, the subthreshold swing, the measurements necessary to assess the TFT stability through positive and negative bias temperature stress, and the negative bias illumination stress (NBIS), to finally introduce In₂O₃ TFTs. Then, we will introduce vacuum and non-vacuum processes like spin-coating and liquid metal printing. We will introduce the various dopants and their applications, from mobility and crystal size improvements with H to NBIS improvements with lanthanides. We will also discuss the importance of device engineering, introducing how to choose the passivation layer, the source and drain, the gate insulator, the substrate, but also the possibility of advanced engineering by introducing the use of dual gate and 2 DEG devices on the mobility improvement. Finally, we will introduce the recent breakthroughs where In₂O₃ TFTs are integrated in neuromorphic applications and 3D integration. Full article

Organic electrochemical transistors (OECTs) are compelling artificial synapses because mixed ionic–electronic coupling and transport enables low-voltage, analog weight updates that mirror biological plasticity. Here, we engineered solid-state, polymer electrolyte-gated vertical OECTs (vOECTs) and elucidate how electrolyte molecular weight influences synaptic dynamics. Using Pg2T-T as the redox-active channel and pDADMAC polymer electrolytes spanning low- (~100 k), medium- (~300 k), and high- (~500 k) molecular weights, cyclic voltammetry reveals reversible Pg2T-T redox, while peak separation and current density systematically track ion transport kinetics. Increasing electrolyte molecular weight enlarges the transfer curve hysteresis (memory window ΔV_mem from ~0.15 V to ~0.50 V) but suppresses on-current, consistent with slower, more confining ion motion and stabilized partially doped states. Devices exhibit rich short- and long-term plasticity: paired-pulse facilitation (A₂/A₁ ≈ 1.75 at Δt = 50 ms), frequency-dependent EPSCs (low-pass accumulation), cumulative potentiation, and reversible LTP/LTD. A device-aware CrossSim framework built from continuous write/erase cycles (probabilistic LUT) supports Fashion-MNIST inference with high accuracy and bounded update errors (mean −0.02; asymmetry 0.198), validating that measured nonidealities remain algorithm-compatible. These results provide a materials-level handle on polymer–ion coupling to deterministically tailor temporal learning in compact, robust neuromorphic hardware. Full article

Polymeric semiconductors have rapidly evolved from early conductive polymers, such as polyacetylene, to high-performance donor–acceptor copolymers, offering a unique combination of mechanical flexibility, solution processability, and tunable optoelectronic properties. These advancements have positioned polymeric semiconductors as versatile materials for next-generation electronics, including wearable, stretchable, and bio-integrated devices, IoT systems, and soft robotics. In this review, we systematically present the fundamental principles of polymeric semiconductors, including electronic structure, charge transport mechanisms, molecular packing, and solid-state morphology, and elucidate how these factors collectively govern device performance. We further discuss recent advances in synthesis strategies, thin-film processing techniques, molecular doping, and interface engineering, emphasizing their critical roles in improving operational stability, charge-carrier mobility, and energy efficiency. Key applications—such as organic photovoltaics, field-effect transistors, neuromorphic devices, and memristors—are analyzed, with a focus on the intricate structure–property–performance relationships that dictate functionality. Finally, we highlight emerging directions and scientific innovations, including sustainable and degradable polymers, hybrid and two-dimensional polymer systems, and novel strategies to enhance device stability and performance. By integrating fundamental polymer science with device engineering, this review provides a comprehensive, structured, and forward-looking perspective, identifying knowledge gaps and offering insights to guide future breakthroughs and the rational design of high-performance, multifunctional, and environmentally responsible polymeric electronic devices. Full article

Neural and synaptic models that are relatively biologically accurate are easy to use to run efficient and distributed programs. The mechanism described in this paper uses these models to develop cell assemblies with a small number of neurons that persist indefinitely unless stopped. These in turn can be used to implement finite state automata and many other useful components, including cognitive maps and natural language parsers. These components support the development of, among other things, agents in virtual environments. Two spiking neuron agents are described, both able to run using either a standard neural simulator or using neuromorphic hardware. Examples of their behavior are described touching the individual spike level. The component model supports step-wise development, and the example of extending the cognitive mapping mechanism from the simple agent to the full agent is described. Spiking nets support parallelism, use on neuromorphic platforms, and engineering and exploration of multiple subsystems, which in turn can help explore the neural basis of cognitive phenomena. Relationships between these spiking nets and biology are discussed. The code is available to ease reuse by other researchers. Full article

Organic field-effect transistors (OFETs) have emerged as a transformative platform for high-performance sensing technologies, yet their full potential can be realized only through coordinated performance optimization. This article provides a comprehensive review of recent strategies employed in polymer OFETs to enhance key parameters, including carrier mobility (μ), threshold voltage (Vth), on/off current ratio (I_on/I_off), and operational stability. These strategies encompass both physical and chemical approaches, such as annealing, self-assembled monolayers (SAMs), modification of main and side polymer chains, dielectric-layer engineering, buffer-layer insertion, and blending or doping techniques. The development of high-performance devices requires precise integration of physical processing and chemical design, alongside the anticipation of processing compatibility during the molecular design phase. This article further highlights the limitations of focusing solely on high mobility and advocates a balanced optimization across multiple dimensions—mobility, mechanical flexibility, environmental stability, and consistent functional performance. Adopting a multi-scale optimization framework spanning molecular, film, and device levels can substantially enhance the adaptability of OFETs for emerging applications such as flexible sensing, bioelectronic interfaces, and neuromorphic computing. Full article

This review examines the state of spiking neural networks (SNNs) for imaging, combining a structured literature survey, a comparative meta-analysis of reported datasets, training strategies, hardware platforms, and applications and a case study on LMU-based depth estimation in direct Time-of-Flight (dToF) imaging. While SNNs demonstrate promise for energy-efficient, event-driven computation, current progress is constrained by reliance on small or custom datasets, ANN-SNN conversion inefficiencies, simulation-based hardware evaluation, and a narrow focus on classification tasks. The analysis highlights scaling trade-offs between accuracy and efficiency, persistent latency bottlenecks, and limited sensor–hardware integration. These findings were synthesised into key challenges and future directions, emphasising benchmarks, hardware-aware training, ecosystem development, and broader application domains. Full article

We demonstrate photogating in a graphene/Si–SiO₂ stack, where vertical motion of photogenerated charge is converted into a corresponding change in graphene channel conductance in real time. Under pulsed illumination, holes accumulate at the Si/SiO₂ interface, creating a surface photovoltage that shifts the flat-band condition and electrostatically suppresses graphene conductance. A dual-readout scheme—simultaneously tracking interfacial charging dynamics and the graphene channel—cleanly separates optical charge injection (cause) from electronic transduction (effect). This separation allows for the direct extraction of practical figures of merit without conventional transfer sweeps, including flat-band shift per pulse, retention time constants, and trap occupancy. Interface kinetics then define two operating regimes: a fast, resettable detector when traps are sparse or rapid, and a trap-assisted analog-memory state when slow traps retain charge between pulses. The mechanism is complementary metal-oxide–semiconductor compatible (CMOS-compatible) and needs no cryogenics or exotic materials. Together, these results outline a compact route to engineer integrating photodetectors, pixel-level memory for adaptive imaging, and neuromorphic optoelectronic elements that couple sensing with in situ computation. 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

Two-dimensional (2D) materials are promising candidates for neuromorphic computing owing to their atomically thin structure and tunable optoelectronic properties. However, achieving controllable synaptic behavior via defect engineering remains challenging. In this work, we introduce X-ray irradiation as a facile strategy to modulate defect states and enhance synaptic plasticity in WSe₂-based optoelectronic synapses. The introduction of selenium vacancies via irradiation significantly improved both electrical and optical responses. Under electrical stimulation, short-term potentiation (STP) exhibited enhanced excitatory postsynaptic current (EPSC) retention exceeding 10%, measured 20 s after the stimulation peak. In addition, the nonlinearity of long-term potentiation (LTP) and long-term depression (LTD) was reduced, and the signal decay time was extended. Under optical stimulation, STP showed more than 4% improvement in EPSC retention at 16 s with similar relaxation enhancement. These effects are attributed to irradiation-induced defect states that facilitate charge carrier trapping and extend signal persistence. Moreover, the reduced nonlinearity in synaptic weight modulation improved the recognition accuracy of handwritten digits in a CrossSim-simulated MNIST task, increasing from 88.5% to 93.75%. This study demonstrates that X-ray irradiation is an effective method for modulating synaptic weights in 2D materials, offering a universal strategy for defect engineering in neuromorphic device applications. Full article

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