Ultra-large memory window for non-volatile memory based on ReS2/hBN/Multilayer Graphene heterojunction

IF 5.9 3区 材料科学 Q2 CHEMISTRY, PHYSICAL
Jiawang You , Wenxiang Wang , Xiaohuan Li , Yushi Xu , Jinjin He , Han Mao , Zheng Wei , Lianfeng Sun , Xiaoqing Chen , Yong Jun Li , Zheng Liu , Hang Wei , Mei Xue
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引用次数: 0

Abstract

With the rapid advancement of technology and the exponential growth of big data, the demand for high-performance memory devices intensifies. Non-volatile memories based on van der Waals materials garner significant attention due to their superior data retention and long-term storage capabilities. However, current floating-gate (FG) memories typically exhibit a memory window of less than 60 %, which limits data storage stability and device lifespan. Therefore, developing non-volatile FG memories with larger memory windows is crucial for modern digital technologies. In this work, we fabricate a non-volatile FG memory device based on a rhenium disulfide (ReS2)/hexagonal boron nitride (hBN)/multilayer graphene (MLG) heterostructure, ReS2 serves as the channel material, hBN acts as the tunneling dielectric, and multilayer graphene functions as the floating gate. Due to the high carrier mobility of ReS2 and the excellent charge storage and release capabilities of graphene, the device demonstrates a high on/off ratio (106) and outstanding long-term data retention (>1000 s). It also exhibits low programming current and the potential for multi-level storage applications. Most notably, the device achieves a significant memory window of 85.5 %, enabling enhanced charge storage capacity and improved stability. This performance is attributed to the effective charge injection and retention enabled by Fowler–Nordheim tunneling through the hBN tunneling barrier These exceptional properties support the realization of efficient and stable data storage, which paves the way for developing next-generation memory technologies.
基于ReS2/hBN/多层石墨烯异质结的非易失性存储器超大存储窗口
随着科技的飞速发展和大数据的指数级增长,对高性能存储设备的需求日益增加。基于范德华材料的非易失性存储器因其优越的数据保留和长期存储能力而备受关注。然而,当前的浮动门(FG)存储器通常表现出小于60%的内存窗口,这限制了数据存储的稳定性和设备的使用寿命。因此,开发具有更大存储窗口的非易失性FG存储器对于现代数字技术至关重要。在这项工作中,我们制作了一种基于二硫化铼(ReS2)/六方氮化硼(hBN)/多层石墨烯(MLG)异质结构的非易失性FG存储器件,ReS2作为通道材料,hBN作为隧道介质,多层石墨烯作为浮栅。由于ReS2的高载流子迁移率和石墨烯优异的电荷存储和释放能力,该器件具有高开/关比(106)和出色的长期数据保留(>1000 s)。它还具有低编程电流和多层次存储应用的潜力。最值得注意的是,该设备实现了85.5%的显著内存窗口,从而增强了电荷存储容量并提高了稳定性。这种性能归功于Fowler-Nordheim通过hBN隧道势垒实现的有效电荷注入和保持。这些卓越的特性支持实现高效稳定的数据存储,为开发下一代存储技术铺平了道路。
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来源期刊
FlatChem
FlatChem Multiple-
CiteScore
8.40
自引率
6.50%
发文量
104
审稿时长
26 days
期刊介绍: FlatChem - Chemistry of Flat Materials, a new voice in the community, publishes original and significant, cutting-edge research related to the chemistry of graphene and related 2D & layered materials. The overall aim of the journal is to combine the chemistry and applications of these materials, where the submission of communications, full papers, and concepts should contain chemistry in a materials context, which can be both experimental and/or theoretical. In addition to original research articles, FlatChem also offers reviews, minireviews, highlights and perspectives on the future of this research area with the scientific leaders in fields related to Flat Materials. Topics of interest include, but are not limited to, the following: -Design, synthesis, applications and investigation of graphene, graphene related materials and other 2D & layered materials (for example Silicene, Germanene, Phosphorene, MXenes, Boron nitride, Transition metal dichalcogenides) -Characterization of these materials using all forms of spectroscopy and microscopy techniques -Chemical modification or functionalization and dispersion of these materials, as well as interactions with other materials -Exploring the surface chemistry of these materials for applications in: Sensors or detectors in electrochemical/Lab on a Chip devices, Composite materials, Membranes, Environment technology, Catalysis for energy storage and conversion (for example fuel cells, supercapacitors, batteries, hydrogen storage), Biomedical technology (drug delivery, biosensing, bioimaging)
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