Ion Channels and Electroosmosis in Porous Geopolymers: A Novel Category of Low-Cost Memristors

Memristors are electric components that emulate the memory and computational properties of biological synapses by remembering the current that flows through them. Here, for the first time, the memristive properties of geopolymers, inexpensive ceramic materials manufactured at room temperature from alkaline activation of amorphous aluminosilicate precursors, are presented. It is demonstrated that geopolymers present all the fingerprints of memristors, and a physics-based model is proposed, which demonstrates that electroosmosis in the bulk geopolymer pores induces ion channels that foster change in the overall conductance of the bulk material, contributing to the observed memristive behavior. This model opens the door to a new category of porous electroosmosis-based bulk memristors. Synaptic functions such as short-term plasticity and long-term plasticity, as well as endurance and retention capabilities are also demonstrated. The reported findings pave the way to the use of geopolymers for low-cost applications in neuromorphic computing.     

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low-Dimensional Semiconductors

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low-dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light-matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two-terminal neuromorphic memristors, three-terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low-dimensional materials is prospected.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

Direct Observation of Oxygen Ion Dynamics in a WO3-x based Second-Order Memristor with Dendritic Integration Functions

Direct observation of oxygen dynamics in an oxide-based second-order memristor can provide the valid evidence to clarify the memristive mechanism, however, which is still limited for now. In this study, the migration and diffusion of oxygen ions in the region of Pt/WO_3-x Schottky interface are observed in the WO_3-x second-order memristor by using the technique of in situ transmission electron microscopy (TEM) and the electron energy loss spectroscopy. Interestingly, the coexistence of memristive and memcapacitive switching can be implemented in this memristor. Combined with the analysis of depth-profile X-ray photoelectron spectroscopy (XPS), an interface-barrier-modulation second-order memristive model is proposed based on the above results. Notably, temporally correlative oxygen dynamics in the memristor offers the platform to integrate signals from multiple inputs, enabling the realization of the dendritic functions of synchronous and asynchronous integration for the application of logic operations with fault-tolerance capability and associative learning. These findings provide the experimental evidence to in-depth understanding of oxygen dynamics and switching mechanism in second-order memristor, which can support the optimization of memristive performance and the achievement of biorealistic synaptic functions.     

Emerging MXene-Based Memristors for In-Memory,Neuromorphic Computing,and Logic Operation

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data-intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in-memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high-performance memristors. Herein, the review focuses on the emerging 2D MXene materials-based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene-based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene-based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene-based memristors and more engaging information technology applications.     

Single Nanoparticle Magnetic Spin Memristor

There is an increasing demand for the development of a simple Si‐based universal memory device at the nanoscale that operates at high frequencies. Spin‐electronics (spintronics) can, in principle, increase the efficiency of devices and allow them to operate at high frequencies. A primary challenge for reducing the dimensions of spintronic devices is the requirement for high spin currents. To overcome this problem, a new approach is presented that uses helical chiral molecules exhibiting spin‐selective electron transport, which is called the chiral‐induced spin selectivity (CISS) effect. Using the CISS effect, the active memory device is miniaturized for the first time from the micrometer scale to 30 nm in size, and this device presents memristor‐like nonlinear logic operation at low voltages under ambient conditions and room temperature. A single nanoparticle, along with Au contacts and chiral molecules, is sufficient to function as a memory device. A single ferromagnetic nanoplatelet is used as a fixed hard magnet combined with Au contacts in which the gold contacts act as soft magnets due to the adsorbed chiral molecules.