Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems

Exploring Moiré Superlattices and Memristive Switching in Non-van der Waals Twisted Bilayer Bi2O2Se

The discovery of moiré physics in two-dimensional (2D) materials has opened new avenues for exploring unique physical and chemical properties induced by intralayer/interlayer interactions. This study reports the experimental observation of moiré patterns in 2D bismuth oxyselenide (Bi2O2Se) nanosheets grown through one-pot chemical reaction methods and a sonication-assisted layer separations technique. Our findings demonstrate that these moiré patterns result from the angular stacking of the nanosheets at various twist angles, leading to the formation of moiré superlattices (MSLs) with distinct periodicities. The presence of these superlattices was confirmed using transmission electron microscopy (TEM) images. The observation of moiré patterns in 2D Bi2O2Se nanosheets highlights the potential of tuning the band structures of the non-van der Waals material and thus unlocking new material properties through precise control of intralayer/interlayer interactions. Furthermore, the stacked 2D Bi2O2Se nanosheets show interesting memristive switching characteristics, presenting a promising candidate for artificial synapses and neuromorphic computing. Traditional memristors typically utilize a vertical metal-insulator-metal (MIM) structure, which relies on the formation of conductive filaments for resistive switching (RS). This configuration, however, often results in abrupt switching during various cycles and significant variation from device to device. Herein, defective BOSe moiré material exhibits a nonfilamentary RS switching characteristic in a two-terminal lateral device configuration. This design reveals an RS mechanism driven by the modulation of the Schottky barrier height (SBH) due to the movement of Se vacancies (VSe) under an external electric field. The fabricated device exhibits excellent RS behavior, achieving an RS ratio of ∼20 with a high degree of control and consistency across multiple cycles and from device to device. Interestingly, the device shows a stable negative differential resistance effect in the high-voltage region due to the carrier trapping process. Finally, we studied the stability of the MSL in BOSe through TEM imaging and electrical characterization on different device configurations to evaluate the repeatability of the switching characteristics.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Optical Bio-Inspired Synaptic Devices

The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous "von Neumann storage wall" problem. Inspired by neural synapses, neuromorphic computing has emerged as a promising solution to address the von Neumann problem due to its excellent adaptive learning and parallel capabilities. Notably, in 2016, researchers integrated light into neuromorphic computing, which inspired the extensive exploration of optoelectronic and all-optical synaptic devices. These optical synaptic devices offer obvious advantages over traditional all-electric synaptic devices, including a wider bandwidth and lower latency. This review provides an overview of the research background on optoelectronic and all-optical devices, discusses their implementation principles in different scenarios, presents their application scenarios, and concludes with prospects for future developments.

Memristive properties and synaptic plasticity in substituted pyridinium iodobismuthates

This study explores the impact of organic cations in bismuth iodide complexes on their memristive behavior in metal-insulator-metal (MIM) type thin-layer devices. The presence of electron-donating and electron withdrawing functional groups (-CN, -CH3, -NH2, and -N(CH3)2) on pyridinium cations induces morphological alterations in crystals, thus influencing the electronic or ionic conductivity of devices comprising sandwiched thin layers (thickness = 200 nm ± 50) between glass/ITO as bottom electrode (∼110 nm) and copper (∼80 nm) as the top electrode. It was found that the current-voltage (I-V) scans of the devices reveal characteristic pinched hysteresis loops, a distinct signature of memristors. The working voltage windows are significantly influenced by both the types of cation and the dimensionality of ionic fragments (0D or 1D) in the solid-state form. Additionally, the temperature alters the surface area of the I-V loops by affecting resistive switching mechanisms, corresponding log-log plots at three temperatures (-30 °C, room temperature and 150 °C) are fully studied. Given that a memristor can operate as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigates long-term depression, potentiation, and spike-time-dependent plasticity-a specific form of the Hebbian learning rule-to mimic biologically synaptic plasticity. Different polar pulses, such as triangle, sawtooth, and square waveforms were employed to generate Hebbian learning rules. The research demonstrates how the shape of the applied pulse series, achieved by overlapping pre- and post-pulses at different time scales, in association with the composition and dimensionality of ionic fragments, lead to changes in the synaptic weight percentages of the devices.

Neuromorphic learning and recognition in WO3-xthin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

Transforming medicine: artificial intelligence integration in the peripheral nervous system

In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.

Resistive switching transparent SnO2 thin film sensitive to light and humidity

Designing and manufacturing memristor devices with simple and less complicated methods is highly promising for their future development. Here, an Ag/SnO2/FTO(F-SnO2) structure is used through the deposition of the SnO2 layer attained by its sol via the air-brush method on an FTO substrate. This structure was investigated in terms of the memristive characteristics. The negative differential resistance (NDR) effect was observed in environment humidity conditions. In this structure, valance change memory and electrometalization change memory mechanisms cause the current peak in the NDR region by forming an OH- conductive filament. In addition, the photoconductivity effect was found under light illumination and this structure shows the positive photoconductance effect by increasing the conductivity. Memristivity was examined for up to 100 cycles and significant stability was observed as a valuable advantage for neuromorphic computing. Our study conveys a growth mechanism of an optical memristor that is sensitive to light and humidity suitable for sensing applications.

Graphene-based RRAM devices for neural computing

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.