Ji Yun, Tang Baoshan, Wang Jinyong, Zheng Haofei, Weng Zhengjin, Wu Yangwu, Li Sifan, Thean Aaron Voon-Yew, Ang Kah-Wee
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.
ACS Nano. 2025 Jan 14;19(1):722-735. doi: 10.1021/acsnano.4c11890. Epub 2024 Dec 31.
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 (CoPS), 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 CoPS through an electroforming process facilitates the formation of conductive filaments, leading to uniform fast switching with minimal energy requirements. The CoPS 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 CoPS 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 CoPS materials for energy-efficient neuromorphic computing circuits.
二维（2D）材料因其卓越的电学可调性、机械柔韧性以及与异质集成的兼容性，在神经形态计算架构的发展中具有巨大潜力。然而，二维忆阻器在神经形态计算中的实际应用常常受到同时实现低延迟和低能耗挑战的阻碍。在此，我们展示了基于二维三硫化钴磷（CoPS）的忆阻器，其实现了令人瞩目的性能指标，包括高开关速度（20纳秒）、低开关能量（1.15皮焦）、高开关比（>400）以及低开关电压（设置为1.05伏，复位为 -0.89伏）。通过电形成过程在CoPS中产生硫空位有助于导电细丝的形成，从而实现均匀快速切换且能量需求最小。CoPS忆阻器还表现出线性电导调制和长期记忆保持能力，能够对手写数字识别的人工神经网络和图像处理的卷积神经网络进行高精度建模。此外，在溶液处理的大规模CoPS薄膜中实现了稳健的忆阻开关，突出了其在晶圆级低温集成方面的潜力。快速切换、低能耗、延长记忆保持、高开关比、线性电导更新和可扩展性的结合体现了二维CoPS材料在节能神经形态计算电路中的潜力。
