Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer

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.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Ni Single-Atoms Based Memristors with Ultrafast Speed and Ultralong Data Retention

Memristor with low-power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration-based memristor is expected to provide a compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, high-performance memristor based on the structure of ITO/Ni single-atoms (NiSAs/N-C)/Polyvinyl pyrrolidone (PVP)/Au is reported. By utilizing well-distributed trapping sites , small tunneling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, ultralong retention capability of 106  s, a low set voltage (Vset ) of ≈0.7 V, a substantial ON/OFF ration of 103 , and low spatial variation in cycle-to-cycle (500 cycles) and device-to-device characteristics (128 devices) is demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. Finally, a memristor ratioed logic-based combinational memristor array to realize the one-bit full adder is further implemented.

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.

Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin

The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.

Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Organizing Reliable Polymer Electrode Lines in Flexible Neural Networks via Coffee Ring-Free Micromolding in Capillaries

With an increase in the demand for smart wearable systems, artificial synapse arrays for flexible neural networks have received considerable attention. A synaptic device with a two-terminal configuration is promising for complex neural networks because of its ability to scale to a crossbar array architecture. To realize practical crossbar arrays with a high density, it is essential to achieve reliable electrode lines that act as signal terminals. However, an effective method to develop intrinsically flexible signal lines in artificial neural networks has not been developed. In this study, we achieved reliable polymer signal lines for flexible neural networks using coffee ring-free micromolding in capillaries (MIMIC). In a typical MIMIC, the outward convective flow of the polymer solution inherently deteriorates the pattern fidelity. To achieve reliable conducting polymer (CP) lines, we precisely controlled the flow of the polymer solution in the MIMIC by inducing the Marangoni force. When the convective and Marangoni flows for the solution were balanced in the MIMIC, the CP line patterns were reliably produced with high fidelity. The developed CP lines exhibited superior conductivity and high mechanical flexibility. Moreover, flexible memristor arrays consisting of CP signal lines demonstrated a high potential for realizing practical neuromorphic systems linked to artificial intelligence.

Ultralow Set Voltage and Enhanced Switching Reliability for Resistive Random-Access Memory Enabled by an Electrodeposited Nanocone Array

Resistive random-access memory (RRAM) has been extensively investigated for 20 years due to its excellent advantages, including scalability, switching speed, compatibility with the complementary metal oxide semiconductor process, and neuromorphic computing application. However, the issue of memristor reliability for cycle to cycle and device to device resulting from the random ion drift and diffusion in solid-state thin films is still a great challenge for commercialization. Therefore, controlling the internal ionic process to improve the memristor performance and reliability is a primary and urgent task. Here, a Ni nanocone array prepared by an electrodeposition method is integrated with an Ag/Al₂O₃/Pt resistive switching device. The nanocone-array-based memristor yields superior switching performance, including an ultralow set voltage (-0.37 V), a concentrated voltage/resistance distribution (C_V 14.8%/32.7%), robust endurance (>10⁵ cycles), and multilevel storage capability. A finite element analysis, transmission electron microscope observation, and current mapping test indicate that the local enhancement of the electric field confines the ionic migration process and yields a predictable formation and dissolution process of the conductive filament. The nanocone-array-based RRAM device provides a new and feasible method to control the conductive filament growth reliably, which paves the way for memristor development.

High Performance Full-Inorganic Flexible Memristor with Combined Resistance-Switching

Flexible memristors hold great promise for flexible electronics applications but are still lacking of good electrical performance together with mechanical flexibility. Herein, we demonstrate a full-inorganic nanoscale flexible memristor by using free-standing ductile α-Ag₂S films as both a flexible substrate and a functional electrolyte. The device accesses dense multiple-level nonvolatile states with a record high 10⁶ ON/OFF ratio. This exceptional memristor performance is induced by sequential processes of Schottky barrier modification at the contact interface and filament formation inside the electrolyte. In addition, it is crucial to ensure that the cathode junction, where Ag⁺ is reduced to Ag, dominates the total resistance and takes the most of setting bias before the filament formation. Our study provides a comprehensive insight into the resistance-switching mechanism in conductive-bridging memristors and offers a new strategy toward high performance flexible memristors.

Research Progress of Biomimetic Memristor Flexible Synapse

With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.