High-Speed and Low-Energy Resistive Switching with Two-Dimensional Cobalt Phosphorus Trisulfide for Efficient...

Two-dimensional (2D) materials hold significant potential for the development of neuromorphic computing architectures owing to their exceptional electrical tunability, mechanical flexibility, and compatibility with heterointegration. However, the practical implementation of 2D memristors in neuromorphic computing is often hindered by the challenges of simultaneously achieving low latency and low energy consumption. Here, we demonstrate memristors based on 2D cobalt phosphorus trisulfide (CoPS₃), which achieve impressive performance metrics including high switching speed (20 ns), low switching energy (1.15 pJ), high switching ratio (>400), and low switching voltages (1.05 V for set and -0.89 V for reset). The creation of sulfur vacancies in CoPS₃ through an electroforming process facilitates the formation of conductive filaments, leading to uniform fast switching with minimal energy requirements. The CoPS₃ memristors also show linear conductance modulation and long-term memory retention, enabling high-accuracy modeling of artificial neural networks for handwritten digit recognition and convolutional neural networks for image processing. Furthermore, robust memristive switching is achieved in solution-processed large-scale CoPS₃ films, underscoring their potential for wafer-scale, low-temperature integration. The combination of rapid switching, low energy consumption, extended memory retention, high switching ratio, linear conductance update, and scalability manifests the potential of 2D CoPS₃ materials for energy-efficient neuromorphic computing circuits.