神经形态工程学
记忆电阻器
晶体管
铁电性
材料科学
范德瓦尔斯力
纳米技术
光电子学
物理
电气工程
计算机科学
工程类
人工神经网络
量子力学
电介质
分子
人工智能
电压
作者
Yinchang Ma,Maolin Chen,Fernando Aguirre,Yuan Yan,Sebastián Pazos,Chen Liu,Heng Wang,Tao Yang,Baoyu Wang,Cheng Gong,Kai Liu,Jefferson Zhe Liu,Mario Lanza,Fei Xue,Xixiang Zhang
出处
期刊:Nano Letters
[American Chemical Society]
日期:2025-02-03
卷期号:25 (6): 2528-2537
被引量:7
标识
DOI:10.1021/acs.nanolett.4c06118
摘要
Two-dimensional-material-based memristor arrays hold promise for data-centric applications such as artificial intelligence and big data. However, accessing individual memristor cells and effectively controlling sneak current paths remain challenging. Here, we propose a van der Waals engineering approach to create one-transistor-one-memristor (1T1M) cells by assembling the emerging two-dimensional ferroelectric CuCrP2S6 with MoS2 and h-BN. The memory cell exhibits high resistance tunability (106), low sneak current (120 fA), and low static power (12 fW). A neuromorphic array with greatly reduced crosstalk is experimentally demonstrated. The nonvolatile resistance switching is driven by electric-field-induced ferroelectric polarization reversal. This van der Waals engineering approach offers a universal solution for creating compact and energy-efficient 2D in-memory computation systems for next-generation artificial neural networks.
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