Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences , Beijing, 100083, China.
School of Material Science and Engineering, Georgia Institute of Technology , Atlanta, Georgia 30332, United States.
ACS Nano. 2016 Jan 26;10(1):1546-51. doi: 10.1021/acsnano.5b07121. Epub 2015 Dec 29.
Two-dimensional (2D) molybdenum disulfide (MoS2) is an exciting material due to its unique electrical, optical, and piezoelectric properties. Owing to an intrinsic band gap of 1.2-1.9 eV, monolayer or a-few-layer MoS2 is used for fabricating field effect transistors (FETs) with high electron mobility and on/off ratio. However, the traditional FETs are controlled by an externally supplied gate voltage, which may not be sensitive enough to directly interface with a mechanical stimulus for applications in electronic skin. Here we report a type of top-pressure/force-gated field effect transistors (PGFETs) based on a hybrid structure of a 2D MoS2 flake and 1D ZnO nanowire (NW) array. Once an external pressure is applied, the piezoelectric polarization charges created at the tips of ZnO NWs grown on MoS2 act as a gate voltage to tune/control the source-drain transport property in MoS2. At a 6.25 MPa applied stimulus on a packaged device, the source-drain current can be tuned for ∼25%, equivalent to the results of applying an extra -5 V back gate voltage. Another type of PGFET with a dielectric layer (Al2O3) sandwiched between MoS2 and ZnO also shows consistent results. A theoretical model is proposed to interpret the received data. This study sets the foundation for applying the 2D material-based FETs in the field of artificial intelligence.
二维(2D)二硫化钼(MoS2)由于其独特的电学、光学和压电性能而备受关注。由于其固有带隙为 1.2-1.9 eV,单层或少数层 MoS2 用于制造具有高电子迁移率和开/关比的场效应晶体管(FETs)。然而,传统的 FETs 由外部提供的栅极电压控制,对于直接与机械刺激接口应用于电子皮肤可能不够敏感。在这里,我们报告了一种基于二维 MoS2 薄片和一维 ZnO 纳米线(NW)阵列的混合结构的顶压/力门控场效应晶体管(PGFET)。一旦施加外部压力,生长在 MoS2 上的 ZnO NW 尖端产生的压电极化电荷将充当栅极电压,以调节/控制 MoS2 中的源漏传输特性。在封装器件上施加 6.25 MPa 的刺激时,源漏电流可调节约 25%,相当于施加额外的-5 V 背栅电压的结果。在 MoS2 和 ZnO 之间夹有介电层(Al2O3)的另一种 PGFET 也显示出一致的结果。提出了一个理论模型来解释所得到的数据。这项研究为在人工智能领域应用基于二维材料的 FET 奠定了基础。