ACS Applied Materials&Interfaces|面向神经形态计算的鲁棒Ag/ZrO2/WS2/Pt忆阻器 ...

文章链接：https://pubs.acs.org/doi/10.1021/acsami.9b17160

摘要

信息时代的发展使电阻式随机存取存储器(RRAM)成为一种关键的纳米级忆阻器件(MD)。然而，由于导电丝(CFs)形成的区域的随机性，RRAM MD仍然存在可靠性不足的问题。本研究首次提出了Ag/ ZrO2/WS2/Pt结构的忆阻器，在忆阻器中插入一层二维(2D) WS2纳米片形成二维材料和氧化物双层忆阻器 (2DOMD)，以提高单层器件的可靠性。结果表明，该电化学金属化存储单元具有高度稳定的忆敏开关、集中的通、关状态电压分布、高速(约10 ns)和强大的耐久性(>10^9循环)。这一结果优于单层ZrO2或WS2膜的忆阻器，因为两层膜具有不同的离子输运速率，从而将导电丝的破裂/再生限制在双层界面区域，从而大大降低了导电丝在忆阻器中的随机性。此外，我们使用手写识别数据集(即修改的国家标准与技术研究所(MNIST)数据库)进行神经形态模拟。此外，生物突触的功能和可塑性，包括脉冲时间依赖可塑性（SDTP）和配对脉冲促进（PPF），已成功实现。将二维材料和氧化物结合到双层忆阻器中，可以显著增强RRAM忆阻器的实际应用，为未来脑增强计算系统的人工突触开发提供便利。

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ ZrO2/WS2/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS2 nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON- and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>109 cycles). This result is superior to MDs with a single-layer ZrO2 or WS2 film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a double layer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future.