Nanoscale Conductive Filament with Alternating Rectification as an Artificial Synapse Building Block

Scalable Layer-Controlled Oxidation of Bi2O2Se for Self-Rectifying Memristor Arrays With sub-pA Sneak Currents

Smart memristors with innovative properties are crucial for the advancement of next-generation information storage and bioinspired neuromorphic computing. However, the presence of significant sneak currents in large-scale memristor arrays results in operational errors and heat accumulation, hindering their practical utility. This study successfully synthesizes a quasi-free-standing Bi2O2Se single-crystalline film and achieves layer-controlled oxidation by developing large-scale UV-assisted intercalative oxidation, resulting β-Bi2SeO5/Bi2O2Se heterostructures. The resulting β-Bi2SeO5/Bi2O2Se memristor demonstrates remarkable self-rectifying resistive switching performance (over 105 for ON/OFF and rectification ratios, as well as nonlinearity) in both nanoscale (through conductive atomic force microscopy) and microscale (through memristor array) regimes. Furthermore, the potential for scalable production of self-rectifying β-Bi2SeO5/Bi2O2Se memristor, achieving sub-pA sneak currents to minimize cross-talk effects in high-density memristor arrays is demonstrated. The memristors also exhibit ultrafast resistive switching (sub-100 ns) and low power consumption (1.2 pJ) as characterized by pulse-mode testing. The findings suggest a synergetic effect of interfacial Schottky barriers and oxygen vacancy migration as the self-rectifying switching mechanism, elucidated through controllable β-Bi2SeO5 thickness modulation and theoretical ab initio calculations.

Constructing artificial gap junctions to mediate intercellular signal and mass transport

Natural gap junctions are a type of channel protein responsible for intercellular signalling and mass communication. However, the scope of applications for these proteins is limited as they cannot be prepared at a large scale and are unable to spontaneously insert into cell membranes in vitro. The construction of artificial gap junctions may provide an alternative strategy for preparing analogues of the natural proteins and bottom-up building blocks necessary for the synthesis of artificial cells. Here we show the construction of artificial gap junction channels from unimolecular tubular molecules consisting of alternately arranged positively and negatively charged pillar[5]arene motifs. These molecules feature a hydrophobic-hydrophilic-hydrophobic triblock structure that allows them to efficiently insert into two adjacent plasma membranes and stretch across the gap between the two membranes to form gap junctions. Similar to natural gap junction channels, the synthetic channels could mediate intercellular signal coupling and reactive oxygen species transmission, leading to cellular activity.

Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors

The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.

Site-Specific Regulated Memristors via Electron-Beam-Induced Functionalization of HfO2

Emerging nonvolatile resistive switching, also known as the memristor, works with a distinct concept that relies mainly on the change in the composition of the active materials, rather than to store the charge. Particularly for oxide-based memristors, the switching is often governed by the random and unpredicted temporal/spatial migration of oxygen defects, resulting in possessing limitations in terms of control over conduction channel formation and inability to regulate hysteresis loop opening. Therefore, site specific dynamic control of defect concentration in the active materials can offer a unique opportunity to realize on-demand regulation of memory storage and artificial intelligence capabilities. Here, high-performance, site-specific spatially scalable memristor devices are fabricated by stabilizing the conduction channel via manipulation of oxygen defects using electron-beam irradiation. Specifically, the memristors exhibit highly stable and electron-beam dose-regulated multilevel analog hysteresis loop opening with adjustable switching ratios even higher than 10⁴ . Additionally, broad modulation of neural activities, including short- and long-term plasticity, paired-pulse facilitation, spike-timing-dependent plasticity, and dynamic multipattern memory processing, are demonstrated. The work opens a new possibility to regulate the resistive switching behavior and control mimicking of neural activities, providing a hitherto unseen tunability in two-terminal oxide-based memristors.

Manipulation of current rectification in van der Waals ferroionic CuInP2S6

Developing a single-phase self-rectifying memristor with the continuously tunable feature is structurally desirable and functionally adaptive to dynamic environmental stimuli variations, which is the pursuit of further smart memristors and neuromorphic computing. Herein, we report a van der Waals ferroelectric CuInP2S6 as a single memristor with superior continuous modulation of current and self-rectifying to different bias stimuli (sweeping speed, direction, amplitude, etc.) and external mechanical load. The synergetic contribution of controllable Cu+ ions migration and interfacial Schottky barrier is proposed to dynamically control the current flow and device performance. These outstanding sensitive features make this material possible for being superior candidate for future smart memristors with bidirectional operation mode and strong recognition to input faults and variations.

Flexible and recyclable bio-based transient resistive memory enabled by self-healing polyimine membrane

The recyclable, self-healing and easily-degradable transient electronic technology has aroused tremendous attention in flexible electronic products. However, integrating the above advantages into one single flexible electronic device is still a huge challenge. Herein, we demonstrate a flexible and recyclable bio-based memory device using fish colloid as the resistive switching layer on a polyimine substrate, which affords reliable mechanical and electrical properties under repetitive conformal deformation operation. This flexible bio-based memory device presents potential analog behaviors including memory characteristics and excitatory current response, which undergoes incremental potentiation in conductance under successive electrical pulses. Moreover, this device is expected to greatly alleviate the environmental problems caused by electronic waste. It can be decomposed rapidly in water and well recycled, which is a promising candidate for transient memories and information security. We believe that this study can provide new possibilities to the field of high-performance transient electronics and flexible resistive memory devices.

Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing

Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.

Machine Learning in Nanoscience: Big Data at Small Scales

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this Mini Review, we highlight some recent efforts to connect the ML and nanoscience communities by focusing on three types of interaction: (1) using ML to analyze and extract new insights from large nanoscience data sets, (2) applying ML to accelerate material discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers.

Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes

It is now known that mammalian brains leverage plasticity of both chemical and electrical synapses (ES) for collocating memory and processing. Unlike chemical synapses, ES join neurons via gap junction ion channels that permit fast, threshold-independent, and bidirectional ion transport. Like chemical synapses, ES exhibit activity-dependent plasticity, which modulates the ionic conductance between neurons and, thereby, enables adaptive synchronization of action potentials. Many types of adaptive computing devices that display discrete, threshold-dependent changes in conductance have been developed, yet far less effort has been devoted to emulating the continuously variable conductance and activity-dependent plasticity of ES. Here, we describe an artificial electrical synapse (AES) that exhibits voltage-dependent, analog changes in ionic conductance at biologically relevant voltages. AES plasticity is achieved at the nanoscale by linking dynamical geometrical changes of a host lipid bilayer to ion transport via gramicidin transmembrane ion channels. As a result, the AES uniquely mimics the composition, biophysical properties, bidirectional and threshold-independent ion transport, and plasticity of ES. Through experiments and modeling, we classify our AES as a volatile memristor, where the voltage-controlled conductance is governed by reversible changes in membrane geometry and gramicidin channel density. Simulations show that AES plasticity can adaptively synchronize Hodgkin-Huxley neurons. Finally, by modulating the molecular constituents of the AES, we show that the amplitude, direction, and speed of conductance changes can be tuned. This work motivates the development and integration of ES-inspired computing devices for achieving more capable neuromorphic hardware.

Rectifying Resistive Memory Devices as Dynamic Complementary Artificial Synapses

Brain inspired computing is a pioneering computational method gaining momentum in recent years. Within this scheme, artificial neural networks are implemented using two main approaches: software algorithms and designated hardware architectures. However, while software implementations show remarkable results (at high-energy costs), hardware based ones, specifically resistive random access memory (RRAM) arrays that consume little power and hold a potential for enormous densities, are somewhat lagging. One of the reasons may be related to the limited excitatory operation mode of RRAMs in these arrays as adjustable passive elements. An interesting type of RRAM was demonstrated recently for having alternating (dynamic switching) current rectification properties that may be used for complementary operation much like CMOS transistors. Such artificial synaptic devices may be switched dynamically between excitatory and inhibitory modes to allow doubling of the array density and significantly reducing the peripheral circuit complexity.