Ma Yinchang, Chen Maolin, Aguirre Fernando, Yan Yuan, Pazos Sebastian, Liu Chen, Wang Heng, Yang Tao, Wang Baoyu, Gong Cheng, Liu Kai, Liu Jefferson Zhe, Lanza Mario, Xue Fei, Zhang Xixiang
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Intrinsic Semiconductor Technologies, Ltd., Buckinghamshire HP18 9SU, United Kingdom.
Nano Lett. 2025 Feb 12;25(6):2528-2537. doi: 10.1021/acs.nanolett.4c06118. Epub 2025 Feb 3.
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 CuCrPS with MoS and -BN. The memory cell exhibits high resistance tunability (10), 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.
基于二维材料的忆阻器阵列在人工智能和大数据等以数据为中心的应用中具有潜力。然而，访问单个忆阻器单元并有效控制潜行电流路径仍然具有挑战性。在此，我们提出一种范德华工程方法，通过将新兴的二维铁电体CuCrPS与MoS和 -BN组装来创建单晶体管单忆阻器（1T1M）单元。该存储单元具有高电阻可调性（10）、低潜行电流（120 fA）和低静态功耗（12 fW）。通过实验展示了一个串扰大大降低的神经形态阵列。非易失性电阻切换由电场诱导的铁电极化反转驱动。这种范德华工程方法为为下一代人工神经网络创建紧凑且节能的二维内存计算系统提供了通用解决方案。
