神经形态工程学
材料科学
激光阈值
纳米技术
金属有机骨架
记忆电阻器
光电子学
计算机体系结构
人工智能
计算机科学
电子工程
人工神经网络
有机化学
工程类
波长
吸附
化学
作者
Seung Woo Han,Chang Taek Lee,Young‐Woong Song,Yeowon Yoon,Jang‐Yeon Kwon,Lianqiao Yang,Moo Whan Shin
标识
DOI:10.1002/adfm.202406088
摘要
Abstract Recently, metal–organic frameworks (MOFs) have gained attention in the field of electronics owing to their capability to tune their electrical characteristics. However, conventional methods for synthesizing MOFs pose challenges for their integration into electronic devices because of their long synthesis times and complex transfer steps. In this study, for the first time, lasing‐assisted synthesis (LAS) is used to rapidly and directly synthesize MOFs. These are applied to resistive random access memory (RRAM) devices. Using the LAS method, Cu(BDC) and Cu(BTC) are synthesized in a remarkably short time (≈5 min) and formed directly on metal substrates as thin films. This simplified their integration into RRAMs. The Cu(BDC)‐ and Cu(BTC)‐based RRAMs are evaluated for their potential in memory and neuromorphic applications. Both devices demonstrated nonvolatile memory capabilities with a remarkable data retention time of 10 4 s and long‐term plasticity (LTP) in response to voltage stimuli. However, the suitability of each device for a specific application varies depending on the type of MOFs used. The Cu(BTC)‐based RRAM is more suitable for memory applications because of its higher on/off ratio, longer endurance, and more data storage capacity. Conversely, Cu(BDC)‐based RRAM is highly effective in neural network simulation, achieving higher classification accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI