Room-Temperature Fabricated Multilevel Nonvolatile Lead-Free Cesium Halide Memristors for Reconfigurable In-Memory Computing

Quantum dot-based memristors for information processing and artificial intelligence applications

Traditional computing systems struggle to keep pace with the development of artificial intelligence, as well as the development of the economy and continuous innovation in science and technology. Therefore, there is an urgent need for a new generation of powerful yet low-power computing technologies to replace them. Quantum dots have been incorporated into memristors due to their unique electrical properties, and the development of quantum dot memristors is expected to solve the problems faced by traditional memristors, including cycle stability, high energy consumption, and conductivity uniformity. This article reviews the research progress of quantum dot memristors and their simulation applications in artificial synapses. It summarizes some of the current challenges faced in the development of quantum dot memristors and discusses the potential future applications of these memristors in the field of artificial intelligence.

Reconfigurable Neuromorphic Computing Using Methyl-Engineered One-Dimensional Covalent Organic Framework Memristors

The rapid evolution of neuromorphic devices seeks to bridge biological neural networks and artificial systems, enabling energy-efficient and scalable computing for next-generation artificial intelligence. Herein, we introduce methyl-engineered one-dimensional covalent organic framework (1D COF)-based memristors as a transformative platform for reconfigurable neuromorphic computing. The incorporation of methyl groups enhances localized polarization effects within the COF framework, effectively mitigating random Ag+ migration/diffusion and stabilizing conductive filament morphology. This strategic modification yields devices with exceptional multilevel storage capabilities, exhibiting superior stability, linearity, and reproducibility. Moreover, the highly ordered architecture and customizable chemical environment of the methyl-functionalized 1D COF allows for precise control over resistive switching behaviors, facilitating the emulation of synaptic functions and the development of artificial neural network architectures. Demonstrating exceptional performance in neuromorphic tasks such as high-accuracy image recognition, these devices showcase significant promise as the foundation for energy-efficient, next-generation neuromorphic computing systems.

Leveraging Dual Resistive Switching in Quasi-2D Perovskite Memristors for Integrated Non-volatile Memory, Synaptic Emulation, and Reservoir Computing

The increasing computational demands of artificial intelligence (AI) algorithms are exceeding the capabilities of conventional computing architectures, creating a strong need for novel materials and paradigms. Memristors that integrate diverse resistive switching (RS) behaviors provide a promising avenue for developing novel computing architectures. In this study, we achieve the coexistence of volatile and nonvolatile RS behaviors in quasi-2D perovskite memristor (Q-2DPM). The Q-2DPM exhibits competitive performance as a nonvolatile memory. Multiple synaptic functions have been successfully simulated on Q-2DPM, such as excitatory postsynaptic currents, paired-pulse facilitation, and long-term potentiation/depression. Furthermore, artificial neural networks using Q-2DPM synapses achieve high accuracy in MNIST image classification tasks. The Q-2DPM's inherent characteristics suitable for reservoir computing are also demonstrated through its application in a pulse-stream-based digital classification experiment, showcasing its impressive performance. The elucidation of the dual RS mechanisms within Q-2DPM provides fresh insights into memristor RS behavior and underscores the potential of achieving diverse computational units through a single device. This work paves the way for the implementation of physical neuromorphic hardware architectures and the advancement of sophisticated computational primitives, offering a significant step toward the next generation of computing technologies.

DC field-biased multibit/analog artificial synapse featuring an additional degree of freedom for performance tuning

Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO2/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term. The unbiased device is of short-term plasticity, positive biasing increases the remanence of the recorded events and the device gains long-term plasticity at a specific biasing level determined from the device geometry. The adjustability of the biasing field provides an additional degree of freedom allowing performance tuning; the paired-pulse facilitation index of the device is tuned by the biasing level adjustment providing further functional versatility. An appropriately biased segment provides more than 10 synaptic weight levels linearly depending on the number and duration of the stimulating spikes. The relationship with spike magnitude is exponential. The experimentally determined nonlinearity coefficient of the biased device for 50 potentiating spikes is comparable to the best published data. The spike-timing-dependent plasticity determined experimentally for the biased device in its long-term plasticity mode fits the mathematical relationship developed for biological synapses. Fabricated on a titanium metal foil, the produced memristors are sturdy and flexible making them suitable for wearable and implantable intelligent electronics. Our findings are anticipated to raise the potential of forming artificial synapses out of polycrystalline metal oxide thin films.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

Chiroptical Synaptic Perovskite Memristor as Reconfigurable Physical Unclonable Functions

Physical unclonable functions (PUFs), often referred to as digital fingerprints, are emerging as critical elements in enhancing hardware security and encryption. While significant progress has been made in developing optical and memory-based PUFs, integrating reconfigurability with sensitivity to circularly polarized light (CPL) remains largely unexplored. Here, we present a chiroptical synaptic memristor (CSM) as a reconfigurable PUF, leveraging a two-dimensional organic-inorganic halide chiral perovskite. The device combines CPL sensitivity with photoresponsive electrical behavior, enabling its application in optoneuromorphic systems, as demonstrated by its ability to perform image categorization tasks within neuromorphic computing. Furthermore, by leveraging a 10 × 10 crossbar array of the CSMs, we develop a PUF capable of generating reconfigurable cryptographic keys based on the combination of neuromorphic potentiation and polarized light conditions. This work demonstrates an integrated approach to optoneuromorphic functionality, data storage, and encryption, providing an alternative approach for reconfigurable memristor-based PUFs.

Data encryption/decryption and medical image reconstruction based on a sustainable biomemristor designed logic gate circuit

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory

With the increasing demands and complexity of the neuromorphic computing schemes utilizing highly efficient analog resistive switching devices, understanding the apparent capacitive and inductive effects in device operation is of paramount importance. Here, we present a systematic array of characterization methods that unravel two distinct voltage-dependent regimes demonstrating the complex interplay between the dynamic capacitive and inductive effects in volatile perovskite-based memristors: (1) a low voltage capacitance-dominant and (2) an inductance-dominant regime evidenced by the highly correlated hysteresis type with nonzero crossing, the impedance responses, and the transient current characteristics. These dynamic capacitance- and inductance-dominant regimes provide fundamental insight into the resistive switching of memristors governing the synaptic depression and potentiation functions, respectively. More importantly, the pulse width-dependent and long-term transient current measurements further demonstrate a dynamic transition from a fast capacitive to a slow inductive response, allowing for the tailored stimulus programming of memristor devices to mimic synaptic functionality.

High-Density, Nonvolatile, Flexible Multilevel Organic Memristor Using Multilayered Polymer Semiconductors

Nonvolatile organic memristors have emerged as promising candidates for next-generation electronics, emphasizing the need for vertical device fabrication to attain a high density. Herein, we present a comprehensive investigation of high-performance organic memristors, fabricated in crossbar architecture with PTB7/Al-AlOx-nanocluster/PTB7 embedded between Al electrodes. PTB7 films were fabricated using the Unidirectional Floating Film Transfer Method, enabling independent uniform film fabrication in the Layer-by-Layer (LbL) configuration without disturbing underlying films. We examined the charge transport mechanism of our memristors using the Hubbard model highlighting the role of Al-AlOx-nanoclusters in switching-on the devices, due to the accumulation of bipolarons in the semiconducting layer. By varying the number of LbL films in the device architecture, the resistance of resistive states was systematically altered, enabling the fabrication of novel multilevel memristors. These multilevel devices exhibited excellent performance metrics, including enhanced memory density, high on-off ratio (>108), remarkable memory retention (>105 s), high endurance (87 on-off cycles), and rapid switching (∼100 ns). Furthermore, flexible memristors were fabricated, demonstrating consistent performance even under bending conditions, with a radius of 2.78 mm for >104 bending cycles. This study not only demonstrates the fundamental understanding of charge transport in organic memristors but also introduces novel device architectures with significant implications for high-density flexible applications.

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

In Situ Unveiling of the Resistive Switching Mechanism of Halide Perovskite-Based Memristors

The organic-inorganic halide perovskite has become one of the most promising candidates for next-generation memory devices, i.e. memristors, with excellent performance and solution-processable preparation. Yet, the mechanism of resistive switching in perovskite-based memristors remains ambiguous due to a lack of in situ visualized characterization methods. Here, we directly observe the switching process of perovskite memristors with in situ photoluminescence (PL) imaging microscopy under an external electric field. Furthermore, the corresponding element composition of conductive filaments (CFs) is studied, indicating that the metallic CFs with respect to the activity of the top electrode are essential for device performance. Finally, electrochemical impedance spectroscopy (EIS) is conducted to reveal that the transition of ion states is associated with the formation of metallic CFs. This study provides in-depth insights into the switching mechanism of perovskite memristors, paving a pathway to develop and optimize high-performance perovskite memristors for large-scale applications.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

Tuning Electrochemical Stability of 5,10-Ditolylphenazine-Based Antiaromatic Materials for Unipolar Memristor toward Artificial Synapses Application

Three organic conjugated small molecules, DTA-DTPZ, Cz-DTPZ, and DTA-me-DTPZ comprising an antiaromatic 5,10-ditolylphenazine (DTPZ) core and electron-donating peripheral substituents with high HOMOs (-4.2 to -4.7 eV) and multiple reversible oxidative potentials are reported. The corresponding films sandwiched between two electrodes show unipolar and switchable hysteresis current-voltage (I-V) characteristics upon voltage sweeping, revealing the prominent features of nonvolatile memristor behaviors. The numerical simulation of the I-V curves suggests that the carriers generated by the oxidized molecules lead to the increment of conductance. However, the accumulated carriers tend to deteriorate the device endurance. The electroactive sites are fully blocked in the dimethylated molecule DTA-me-DTPZ, preventing the irreversible electrochemical reaction, thereby boosting the endurance of the memristor device over 300 cycles. Despite the considerable improvement in endurance, the decrement of on/off ratio from 105 to 101 after 250 cycles suggests that the excessive charge carriers (radical cations) remains a problem. Thus, a new strategy of doping an electron-deficient material, CN-T2T, into the unipolar active layer was introduced to further improve the device stability. The device containing DTA-me-DTPZ:CNT2T (1:1) blend as the active layer retained the endurance and on/off ratio (∼104) upon sweeping 300 cycles. The molecular designs and doping strategy demonstrate effective approaches toward more stable metal-free organic conjugated small-molecule memristors.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.