Highly Reliable Threshold Switching Characteristics of Surface-Modulated Diffusive Memristors Immune to Atmospheric Changes

Memristive Ion Dynamics to Enable Biorealistic Computing

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention. Ion-based memristive devices (IMDs), owing to the intrinsic functional similarities to their biological counterparts, hold significant promise for implementing emerging neuromorphic learning and computing algorithms. In this article, we review the fundamental mechanisms of IMDs based on ion drift and diffusion to elucidate the origins of their diverse dynamics. We then examine how these mechanisms operate within different materials to enable IMDs with various types of switching behaviors, leading to a wide range of applications, from emulating biological components to realizing specialized computing requirements. Furthermore, we explore the potential for IMDs to be modified and tuned to achieve customized dynamics, which positions them as one of the most promising hardware candidates for executing bioinspired algorithms with unique specifications. Finally, we identify the challenges currently facing IMDs that hinder their widespread usage and highlight emerging research directions that could significantly benefit from incorporating IMDs.

Leveraging volatile memristors in neuromorphic computing: from materials to system implementation

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

Tunable Neuromorphic Switching Dynamics via Porosity Control in Mesoporous Silica Diffusive Memristors

In response to the growing need for efficient processing of temporal information, neuromorphic computing systems are placing increased emphasis on the switching dynamics of memristors. While the switching dynamics can be regulated by the properties of input signals, the ability of controlling it via electrolyte properties of a memristor is essential to further enrich the switching states and improve data processing capability. This study presents the synthesis of mesoporous silica (mSiO2) films using a sol-gel process, which enables the creation of films with controllable porosities. These films can serve as electrolyte layers in the diffusive memristors and lead to tunable neuromorphic switching dynamics. The mSiO2 memristors demonstrate short-term plasticity, which is essential for temporal signal processing. As porosity increases, discernible changes in operating currents, facilitation ratios, and relaxation times are observed. The underlying mechanism of such systematic control was investigated and attributed to the modulation of hydrogen-bonded networks within the porous structure of the silica layer, which significantly influences both anodic oxidation and ion migration processes during switching events. The result of this work presents mesoporous silica as a unique platform for precise control of neuromorphic switching dynamics in diffusive memristors.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.