High-Performance Flexible Polymer Memristor Based on Stable Filamentary Switching

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

High-Yield Production of Solution-Processed Highly Robust Organic Artificial Synapses by Thermal Treatments

A promising approach for implementing biomimetic systems relies on organic electronic devices designed to emulate neural synapses. However, organic artificial synapses face challenges in achieving high yield and robustness, rendering them difficult to use in practical applications. In this work, a high-yield and highly stable bulk heterojunction (BHJ) synaptic device composed of Poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) was fabricated via a simple solution process followed by thermal treatments. The crystallinity of P3HT and the precipitation of PCBM in BHJ films can be controlled by the thermal annealing temperatures. At 80 °C, P3HT reaches its highest crystallinity, while PCBM remains uniformly distributed. This thermal treatment significantly contributes to the fabrication of devices characterized by a high yield rate, reaching 98.43%. Additionally, this device remained operational even after being immersed in deionized water, ethanol, and seawater for 100 h. More importantly, it exhibited high elasticity over a wide temperature range from -90 to 310 °C. Finally, this device was utilized to construct a biomimetic vehicle with autonomous memory learning capabilities. After repeated training, the avoidance time was optimized by 31.4%. The robust P3HT:PCBM artificial synapses hold great promise for advancing the development of biomimetic electronic products in extreme environments.

Solid polymer electrolyte-based atomic switches: from materials to mechanisms and applications

As miniaturization of semiconductor memory devices is reaching its physical and technological limits, there is a demand for memory technologies that operate on new principles. Atomic switches are nanoionic devices that show repeatable resistive switching between high-resistance and low-resistance states under bias voltage applications, based on the transport of metal ions and redox reactions in solids. Their essential structure consists of an ion conductor sandwiched between electrochemically active and inert electrodes. This review focuses on the resistive switching mechanism of atomic switches that utilize a solid polymer electrolyte (SPE) as the ion conductor. Owing to the superior properties of polymer materials such as mechanical flexibility, compatibility with various substrates, and low fabrication costs, SPE-based atomic switches are a promising candidate for the next-generation of volatile and nonvolatile memories. Herein, we describe their operating mechanisms and key factors for controlling the device performance with different polymer matrices. In particular, the effects of moisture absorption in the polymer matrix on the resistive switching behavior are addressed in detail. As potential applications, atomic switches with inkjet-printed SPE and quantum conductance behavior are described. SPE-based atomic switches also have great potential in use for neuromorphic devices. The development of these devices will be enhanced using nanoarchitectonics concepts, which integrate functional materials and devices.

Formation of Cluster-Structured Metallic Filaments in Organic Memristors for Wearable Neuromorphic Systems with Bio-Mimetic Synaptic Weight Distributions

With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural networks utilizing several types of flexible artificial synapses, it is still challenging to develop wearable systems for complex computations due to the difficulties in emulating continuous memory states in a synaptic component. In this study, polymer conductivity is analyzed as a crucial factor in determining the growth dynamics of metallic filaments in organic memristors. Moreover, flexible memristors with bio-mimetic synaptic functions such as linearly tunable weights are demonstrated by engineering the polymer conductivity. In the organic memristor, the cluster-structured filaments are grown within the polymer medium in response to electric stimuli, resulting in gradual resistive switching and stable synaptic plasticity. Additionally, the device exhibits the continuous and numerous non-volatile memory states due to its low leakage current. Furthermore, complex hardware neural networks including ternary logic operators and a noisy image recognitions system are successfully implemented utilizing the developed memristor arrays. This promising concept of creating flexible neural networks with bio-mimetic weight distributions will contribute to the development of a new computing architecture for energy-efficient wearable smart electronics.

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.

Transmission Mechanism and Logical Operation of Graphene-Doped Poly(vinyl alcohol) Composite-Based Memristor

Memristors are considered the best candidates for nonvolatile memory and advanced computing technologies, and polymer and two-dimensional (2D) materials have been developed as functional layer materials in memristors with high-performance resistive switching characteristics. In this work, a polymer memristor with a graphene (Gr)-doped poly(vinyl alcohol) (PVA) composite acting as the functional layer was prepared. The memristor device exhibited superior performance with good retention and a comparatively large ON/OFF ratio at room temperature. Additionally, excellent logic operations were achieved. These satisfactory properties can be attributed to trap-induced carrier trapping and detrapping. In addition, the device exhibited stable bipolar resistive switching behavior over a moderate temperature range. This work provides insight into the transmission mechanism of polymer-based memristors and the reasons why they become unstable at high temperatures, demonstrating the potential applications of PVA-Gr-based polymer memristors as logic circuit units in integrated chips and artificial intelligence.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Controllability of the Conductive Filament in Porous SiOx Memristors by Humidity-Mediated Silver Ion Migration

Oxide-based memristors composed of Ag/porous SiOx/Si stacks are fabricated using different etching time durations between 0 and 90 s, and the memristive properties are analyzed in the relative humidity (RH) range of 30-60%. The combination of humidity and porous structure provides binding sites to control silver filament formation with a confined nanoscale channel. The memristive properties of devices show high on/off ratios up to 108 and a dispersion coefficient of 0.1% of the high resistance state (CHRS) when the RH increases to 60%. Humidity-mediated silver ion migration in the porous SiOx memristors is investigated, and the mechanism leading to the synergistic effects between the porous structure and environmental humidity is elucidated. The artificial neural network constructed theoretically shows that the recognition rate increases from 60.9 to 85.29% in the RH range of 30-60%. The results and theoretical understanding provide insights into the design and optimization of oxide-based memristors in neuromorphic computing applications.

Rationally Designing High-Performance Versatile Organic Memristors through Molecule-Mediated Ion Movements

Organic memory has attracted tremendous attention for next-generation electronic elements for the molecules' striking ease of structural design. However, due to them being hardly controllable and their low ion transport, it is always essential and challenge to effectively control their random migration, pathway, and duration. There are very few effective strategies, and specific platforms with a view to molecules with specific coordination-groups-regulating ions have been rarely reported. In this work, as a generalized rational design strategy, the well-known tetracyanoquinodimethane (TCNQ) is introduced with multiple coordination groups and small plane structure into a stable polymers framework to modulate Ag migration and then achieve high-performance devices with ideal productivity, low operation voltage and power, stable switching cycles, and state retention. Raman mapping demonstrates that the migrated Ag can specially coordinate with the embedded TCNQ molecules. Notably, the TCNQ molecule distribution can be modulated inside the polymer framework and regulate the memristive behaviors through controlling the formed Ag conductive filaments (CFs) as demonstrated by Raman mapping, in situ conductive atomic force microscopy (C-AFM), X-ray diffraction (XRD) and depth-profiling X-ray photoelectron spectroscopy (XPS). Thus the controllable molecule-mediated Ag movements show its potential in rationally designing high-performance devices and versatile functions and is enlightening in constructing memristors with molecule-mediated ion movements.

Highly Reliable Textile-Type Memristor by Designing Aligned Nanochannels

Information-processing devices are the core components of modern electronics. Integrating them into textiles is the indispensable demand for electronic textiles to form close-loop functional systems. Memristors with crossbar configuration are regarded as promising building blocks to design woven information-processing devices that seamlessly unify with textiles. However, the memristors always suffer from severe temporal and spatial variations due to the random growth of conductive filaments during filamentary switching processes. Here, inspired by the ion nanochannels across synaptic membranes, a highly reliable textile-type memristor made of Pt/CuZnS memristive fiber with aligned nanochannels, showing small set voltage variation (<5.6%) under ultralow set voltage (≈0.089 V), high on/off ratio (≈106 ), and low power consumption (0.1 nW), is reported. Experimental evidence indicate that nanochannels with abundant active S defects can anchor silver ions and confine their migrations to form orderly and efficient conductive filaments. Such memristive performances enable the resultant textile-type memristor array to have high device-to-device uniformity and process complex physiological data like brainwave signals with high recognition accuracy (95%). The textile-type memristor arrays are mechanically durable to withstand hundreds of bending and sliding deformations, and seamlessly unified with sensing, power-supplying, and displaying textiles/fibers to form all-textile integrated electronic systems for new generation human-machine interactions.

A review of memristor: material and structure design, device performance, applications and prospects

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

Optoelectronic Memristive Synapse Behavior for the Architecture of Cu2ZnSnS4@BiOBr Embedded in Poly(methyl methacrylate)

The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu₂ZnSnS₄@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >10⁴ s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.