Electronically Reconfigurable Memristive Neuron Capable of Operating in Both Excitation and Inhibition Modes.

Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.

Self-Assembled Au Nanoelectrodes: Enabling Low-Threshold-Voltage HfO2-Based Artificial Neurons.

Filamentary-type resistive switching devices, such as conductive bridge random-access memory and valence change memory, have diverse applications in memory and neuromorphic computing. However, the randomness in filament formation poses challenges to device reliability and uniformity. To overcome this issue, various defect engineering methods have been explored, including doping, metal nanoparticle embedding, and extended defect utilization. In this study, we present a simple and effective approach using self-assembled uniform Au nanoelectrodes to controll filament formation in HfO2 resistive switching devices. By concentrating the electric field near the Au nanoelectrodes within the BaTiO3 matrix, we significantly enhanced the device stability and reduced the threshold voltage by up to 45% in HfO2-based artificial neurons compared to the control devices. The threshold voltage reduction is attributed to the uniformly distributed Au nanoelectrodes in the insulating matrix, as confirmed by COMSOL simulation. Our findings highlight the potential of nanostructure design for precise control of filamentary-type resistive switching devices.

High Amplitude Spike Generator in Au Nanodot-Incorporated NbOx Mott Memristor.

NbOx-based Mott memristors exhibit fast threshold switching behaviors, making them suitable for spike generators in neuromorphic computing and stochastic clock generators in security devices. In these applications, a high output spike amplitude is necessary for threshold level control and accurate signal detection. Here, we propose a materialwise solution to obtain the high amplitude spikes by inserting Au nanodots into the NbOx device. The Au nanodots enable increasing the threshold voltage by modulating the oxygen contents at the electrode-oxide interface, providing a higher ON current compared to nanodot-free NbOx devices. Also, the reduction of the local switching region volume decreases the thermal capacitance of the system, allowing the maximum spike amplitude generation. Consequently, the Au nanodot incorporation increases the spike amplitude of the NbOx device by 6 times, without any additional external circuit elements. The results are systematically supported by both a numerical model and a finite-element-method-based multiphysics model.

Demonstration of the threshold-switching memory devices using EMIm(AlCl3)Cl and ZnO for neuromorphic applications.

The threshold-switching behaviors of the synapses lead to energy-efficient operation in the neural computing system. Here, we demonstrated the threshold-switching memory devices by inserting the ZnO layer into the ionic synaptic devices. The EMIm(AlCl3)Cl is utilized as the electrolyte because its conductance can be tuned by the charge states of the Al-based ions. The redox reactions of the Al ions in the electrolyte can lead to the analog resistive switching characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression. By inserting the ZnO layer into the EMIm(AlCl3)-based ionic synaptic devices, the threshold switching behaviors are demonstrated. Using the resistivity difference between ZnO and EMIm(AlCl3)Cl, the analog resistive switching behaviors are tunned as the threshold-switching behaviors. The threshold-switching behaviors are achieved by applying the spike stimuli to the device. Demonstration of the threshold-switching behaviors of the ionic synaptic devices has a possibility to achieve high energy-efficiency for the ion-based artificial synapses.

Physically Transient Threshold Switching Device Based on Magnesium Oxide for Security Application.

Transient memristors are prospective candidates for both secure memory systems and biointegrated electronics, which are capable to physically disappear at a programmed time with a triggered operation. However, the sneak current issue has been a considerable obstacle to achieve high-density transient crossbar array of memristors. To solve this problem, it is necessary to develop a transient switch device to turn the memory device on and off controllably. Here, a dissolvable and flexible threshold switching (TS) device with a vertically crossed structure is introduced, which exhibits a high selectivity of 107 , steep turn-on slope of <8 mV dec-1 , and fast ON/OFF switch speed within 50/25 ns. Triggered failure could be achieved after soaking the device in deionized water for 8 min at room temperature. Furthermore, a water-assisted transfer printing method is used to fabricate flexible and transient TS device arrays for bioresorbable systems, in which none of any significant degradation is observed under a bending radius of 2 mm. Integrating the selector with a transient memristor is capable of 107 Gb memory implementation, indicating that the transient TS device could provide great opportunities to achieve highly integrated transient memory arrays.

Optoelectronic neuron based on transistor combined with volatile threshold switching memristors for neuromorphic computing.

The human perception and learning heavily rely on the visual system, where the retina plays a vital role in preprocessing visual information. Developing neuromorphic vision hardware is based on imitating the neurobiological functions of the retina. In this work, an optoelectronic neuron is developed by combining a gate-modulated PDVT-10 channel with a volatile threshold switching memristor, enabling the achievement of optoelectronic performance through a resistance-matching mechanism. The optoelectronic spiking neuron exhibits the ability to alter its spiking behavior in a manner resembling that of a retina. Incorporating electrical and optical modulation, the artificial neuron accurately replicates neuronal signal transmission in a biologically manner. Moreover, it demonstrates inhibition of neuronal firing during darkness and activation upon exposure to light. Finally, the evaluation of a perceptron spiking neural network utilizing these leaky integrate-and-fire neurons is conducted through simulation to assess its capability in classifying image recognition algorithms. This research offers a hopeful direction for the development of easily expandable and hierarchically structured spiking electronics, broadening the range of potential applications in biomimetic vision within the emerging field of neuromorphic hardware.

An Ion-Mediated Spiking Chemical Neuron based on Mott Memristor.

Artificial spiking neurons capable of interpreting ionic information into electrical spikes are critical to mimic biological signaling systems. Mott memristors are attractive for constructing artificial spiking neurons due to their simple structure, low energy consumption, and rich neural dynamics. However, challenges remain in achieving ion-mediated spiking and biohybrid-interfacing in Mott neurons. Here, a biomimetic spiking chemical neuron (SCN) utilizing an NbOx Mott memristor and oxide field-effect transistor-type chemical sensor is introduced. The SCN exhibits both excitation and inhibition spiking behaviors toward ionic concentrations akin to biological neural systems. It demonstrates spiking responses across physiological and pathological Na+ concentrations (1-200 × 10-3 m). The Na+-mediated SCN enables both frequency encoding and time-to-first-spike coding schemes, illustrating the rich neural dynamics of Mott neuron. In addition, the SCN interfaced with L929 cells facilitates real-time modulation of ion-mediated spiking under both normal and salty cellular microenvironments.

GeSe ovonic threshold switch: the impact of functional layer thickness and device size.

Three-dimensional phase change memory (3D PCM), possessing fast-speed, high-density and nonvolatility, has been successfully commercialized as storage class memory. A complete PCM device is composed of a memory cell and an associated ovonic threshold switch (OTS) device, which effectively resolves the leakage current issue in the crossbar array. The OTS materials are chalcogenide glasses consisting of chalcogens such as Te, Se and S as central elements, represented by GeTe6, GeSe and GeS. Among them, GeSe-based OTS materials are widely utilized in commercial 3D PCM, their scalability, however, has not been thoroughly investigated. Here, we explore the miniaturization of GeSe OTS selector, including functional layer thickness scalability and device size scalability. The threshold switching voltage of the GeSe OTS device almost lineally decreases with the thinning of the thickness, whereas it hardly changes with the device size. This indicates that the threshold switching behavior is triggered by the electric field, and the threshold switching field of the GeSe OTS selector is approximately 105 V/µm, regardless of the change in film thickness or device size. Systematically analyzing the threshold switching field of Ge-S and Ge-Te OTSs, we find that the threshold switching field of the OTS device is larger than 75 V/µm, significantly higher than PCM devices (8.1-56 V/µm), such as traditional Ge2Sb2Te5, Ag-In-Sb-Te, etc. Moreover, the required electric field is highly correlated with the optical bandgap. Our findings not only serve to optimize GeSe-based OTS device, but also may pave the approach for exploring OTS materials in chalcogenide alloys.

Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks.

The artificial tactile perception system of this work utilizes a fully connected spiking neural network (SNN) comprising two layers. Its architecture is streamlined and energy-efficient as it directly integrates spiking tactile neurons with piezoresistive sensors and Pt/NbOx/TiN memristors as input neurons. These spiking tactile neurons possess the ability to perceive and integrate pressure stimuli from multiple sensors and encode the information into rate-coded electrical spikes, closely resembling the behavior of a biological tactile neuron. The system's real-time information processing capability is demonstrated through an artificial perceptual learning system that successfully encodes and decodes the Morse code; the artificial perceptual learning system accurately recognizes and displays 26 English letters. Furthermore, the artificial tactile perception system is evaluated for the recognition of the MNIST data set, achieving a classification accuracy of 85.7% with the supervised spiking-rate-dependent plasticity learning rule. The key advantages of this artificial tactile perception system are its simple structure and high efficiency, which contributes to its practicality for various real-world applications.

Self-Assembled Monolayer and Nanoparticles Coenhanced Fragmented Silver Nanowire Network Memristor.

Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of the network is unstable due to locally enhanced Joule heating and the Gibbs-Thomson effect, which poses an obstacle to the integration of threshold switching and memory function in the same AgNW memristor. Here, fragmented AgNW networks combined with Ag nanoparticles (AgNPs) and mercapto self-assembled monolayers (SAMs) are devised to construct memristors with stable threshold switching and memory behavior. In the above design, the planar gaps between NW segments are for resistive switching, the AgNPs act as metal islands in the gaps to reduce threshold voltage (Vth) and holding voltage (Vhold), and the SAMs suppress surface atom diffusion to avoid Oswald ripening of the AgNPs, which improves switching stability. The fragmented NW-NP/SAM memristors not only circumvent the side effects of conventional NW-stacked junctions to provide durable threshold switching at >Vth but also exhibit synaptic characteristics such as long-term potentiation at ultralow voltage (≪Vth). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.