Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing

Radio frequency switching devices based on two-dimensional materials for high-speed communication applications

Two-dimensional (2D) materials, with their atomic-scale thickness, high carrier mobility, tunable wide bandgap, and excellent electrical and mechanical properties, have demonstrated vast application prospects in research on radio frequency (RF) switch devices. This review summarizes the recent advances in 2D materials for RF switch applications, focusing on the performance and mechanisms of 2D material-based RF switch devices at high frequencies, wide bandwidths, and high transmission rates. The analysis includes the design and optimization of devices based on graphene, transition metal dichalcogenides, hexagonal boron nitride, and their heterojunctions. By comparing the key performance parameters such as insertion loss, isolation, and cutoff frequency of the switches, this review reveals the influence of material selection, structural design, and defect control on device performance. Furthermore, it discusses the challenges of 2D material-based RF switches in practical applications, including material defect control, reduction of contact resistance, and the technical bottlenecks of large-scale industrial production. Finally, this review envisions future research directions, proposing potential pathways for improving device performance through heterojunction structure design, multifunctional integration, and process optimization. This study is of great significance for advancing the development of high-performance RF switches and the application of communication technologies in 6G and higher frequency bands.

Enhanced Memristive Performance via a Vertically Heterointerface in Nanocomposite Thin Films for Artificial Synapses

Memristors can be used to mimic synaptic behavior in artificial neural networks, which makes them a key component in neuromorphic computing and holds promise for advancing the field. In this study, a memory artificial synaptic device based on ZnO-BaTiO3 (ZnO-BTO) vertically aligned nanocomposite thin films was prepared. The vertical interface between the two phases can be used as a conduit for oxygen vacancy (OV) accumulation and a channel for OV movement, which greatly optimizes the resistive switching performance of the device and has the potential for multistage storage. By applying different pulse sequences to the device, the conductance of the device is adjusted from multiple angles, and a variety of synaptic functions are simulated, such as paired-pulse facilitation, spike-timing-dependent plasticity, short-term plasticity to long-term plasticity (STP-LTP), and long-term potentiation/depression (LTP/LTD). Finally, we construct a neural network for image recognition, and the recognition accuracy can reach 91%. Our study demonstrates the feasibility of using composite thin-film vertical interface to regulate the resistive performance of memristors and its great potential in artificial synaptic simulation and neuromorphic computing.