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Novel Strategy of Edge Saturation Hamiltonian for Graphene Nanoribbon Devices

机译:石墨烯纳米带器件边缘饱和哈密顿量的新策略

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Tight-binding (TB) Hamiltonian is widely used in the simulations of many low-dimensional devices, such as graphene nanoribbon (GNR)-based FETs, and usually, it is an empirical parameterized matrix. In this paper, we propose a novel strategy to construct the edge TB parameters of armchair GNR (a-GNR) under different edge saturations. Different from the traditional way's only edge bonds analysis, this method also considers the diversity of three a-GNR families and the saturation atoms impact on the subedge bonds. The effects of several common elements or groups on the edge of GNRs are studied numerically and among them, hydrogen (H), fluorine (F), and hydroxyl group (OH) show better saturation properties. Through elaborate verifications of these three types of saturations, the TB fitting errors of the proposed new strategy are drastically reduced, in comparison with the traditional parameters. These verifications, which are subjected to the ab initio results, go from an energy-band level to a device-performance level. Simulation results also show that saturated by different atoms, the band structure of GNR obviously varies. Thus, edge saturation can be another effective method of tuning GNR device properties, such as bandgap, current on/off ratio, and carrier mobility.
机译:紧密结合(TB)哈密顿量被广泛用于许多低维器件的仿真中,例如基于石墨烯纳米带(GNR)的FET,通常它是一个经验参数化矩阵。在本文中,我们提出了一种新颖的策略来构造不同边缘饱和度下扶手椅GNR(a-GNR)的边缘TB参数。与传统方法唯一的边缘键分析不同,该方法还考虑了三个a-GNR族的多样性以及饱和原子对次边缘键的影响。数值研究了几种常见元素或基团对GNR边缘的影响,其中氢(H),氟(F)和羟基(OH)具有更好的饱和特性。通过对这三种饱和度的精心验证,与传统参数相比,该新策略的TB拟合误差得到了大幅降低。这些验证从头开始,从能带级别到设备性能级别。仿真结果还表明,GNR在不同原子饱和下的能带结构明显不同。因此,边缘饱和度可能是调整GNR器件属性(如带隙,电流开/关比和载流子迁移率)的另一种有效方法。

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