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Tunable and nonvolatile multibit data storage memory based on MoTe2/Boron Nitride/Graphene heterostructures through contact engineering
Nanotechnology ( IF 2.9 ) Pub Date : 2020-09-15 , DOI: 10.1088/1361-6528/aba92b
Enxiu Wu 1 , Yuan Xie , Shijie Wang , Chen Wu , Daihua Zhang , Xiaodong Hu , Jing Liu
Affiliation  

Heterostructures formed by stacking atomically thin two-dimensional materials are promising candidates for flash memory devices to achieve premium performances, due to the capability of effective carrier modulation and unique charge trapping behavior at the interfaces with atomic level flatness. Here, we report a nonvolatile floating-gate flash memory based on MoTe2/h-BN/graphene van der Waals heterostructure, which possesses increased data storage capacity per cell and versatile tunability. The decent memory behavior of the device is enabled by the carriers stored in the floating gate of graphene layer, which tunnel through the dielectric layer of h-BN from the channel layer of MoTe2 under static-electrical field. Consequently, the developed memory device is capable to store 2 bits per cell by applying varied gate bias to implement multi-distinctive current levels. The device also exhibits remarkable erase/program current ratio of ~ 105 with 1µs switch speed and stable retention with estimated ~30% charge loss after 10 years. Furthermore, the device can operate in both p- and n-type modes through contact engineering, offering wide adaptability for emerging applications in electronic technologies, such as neuromorphic computing, data-adaptive energy efficient memory, and complex digital circuits.

中文翻译:

通过接触工程基于 MoTe2/氮化硼/石墨烯异质结构的可调谐非易失性多位数据存储存储器

通过堆叠原子级薄的二维材料形成的异质结构是闪存器件实现优质性能的有希望的候选者,因为它具有有效的载流子调制能力和具有原子级平坦度的界面处独特的电荷俘获行为。在这里,我们报告了一种基于 MoTe2/h-BN/石墨烯范德华异质结构的非易失性浮栅闪存,它具有增加的每个单元的数据存储容量和通用的可调性。该器件的良好存储行为是由存储在石墨烯层浮栅中的载流子实现的,这些载流子在静电场下从 MoTe2 的沟道层穿过 h-BN 的介电层。最后,开发的存储设备能够通过应用不同的栅极偏置来实现多不同的电流水平,从而每个单元存储 2 位。该器件还具有约 105 的显着擦除/编程电流比,开关速度为 1μs,保持稳定,估计 10 年后电荷损失约 30%。此外,该器件可以通过接触工程在 p 型和 n 型模式下运行,为电子技术中的新兴应用提供广泛的适应性,例如神经形态计算、数据自适应节能存储器和复杂的数字电路。
更新日期:2020-09-15
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