First principles study of high-performance sub-5-nm monolayer SnS field-effect transistors

Guo Ying; Pan Feng; Yao Bin-Bin; Meng Hao; Lü Jin

doi:10.7498/aps.73.20241004

Abstract

Authors and contacts

FullText HTML

1. 引　言
2. 模型构建和计算方法
2.1 模型构建
2.2 计算方法及相关参数
3. 计算结果分析与讨论
3.1 掺杂元素占位与结构稳定性
3.2 差分电荷密度分析
3.3 掺杂元素种类对基体总磁矩的影响
3.4 偏态密度分析
3.5 元素的筛选
3.6 掺杂元素浓度对结构稳定性和总磁矩的影响
4. 结　论

References

Abstract

Currently, Si-based field-effect transistors (FET) are approaching their physical limit and challenging Moore's law due to their short-channel effect, and further reducing their gate length to the sub-10 nm is extremely difficult. Two-dimensional (2D) layered semiconductors with atom-scale uniform thickness and no dangling bonds on the interface are considered potential channel materials to support further miniaturization and integrated electronics. Wu et al. [Wu F, et al. 2022 Nature 603 259] successfully fabricated an FET with gate length less than 1 nm by using atomically thin molybdenum disulfide with excellent device performance. This breakthrough has greatly encouraged further theoretical predictions regarding the performance of 2D devices. Additionally, 2D SnS has high carrier mobility, anisotropic electronic properties, and is stable under ambient condition, which is conducive to advanced applications in 2D semiconductor technology. Herein, we explore the quantum transport properties of sub-5 nm monolayer (ML) SnS FET by using first-principles quantum transport simulation. Considering the anisotropic electronic SnS, the double-gated-two-probe device model is constructed along the armchair direction and the zigzag direction of ML SnS. After testing five kinds of doping concentrations, a doping concentration of 5×10¹³ cm^–2 is the best one for SnS FET. We also use the underlaps (ULs) with lengths of 0, 2, and 4 nm to improve the device performance. On-state current (I_on) is an important parameter for evaluating the transition speed of a logic device. A higher I_on of a device can help to increase the switching speed of high-performance (HP) servers. The main conclusions are drawn as follows.1) I_on values of the n-type 2 nm (UL = 4 armchair), 3 nm (UL = 2), 4 nm (UL = 3), 5 nm (UL = 0) and the p-type 1 nm (UL = 2 zigzag), 2 nm (UL = 2 zigzag), 3 nm (UL = 2, 4 zigzag), 4 nm (UL = 2, 4 zigzag), and 5 nm (UL = 0, armchair/zigzag) gate-length devices can meet the standards for HP applications in the next decade in the International Technology Roadmap for semiconductors (ITRS, 2013 version).2) I_on values of the n-type device along the armchair direction (31–2369 μA/μm) are larger than those in the zigzag direction (4.04–1943 μA/μm), while I_on values of the p-type along the zigzag direction (545–4119 μA/μm) are larger than those in the armchair direction (0.7–924 μA/μm). Therefore, the p-type ML GeSe MOSFETs have a predominantly anisotropic current.3) I_on value of the p-type 3 nm gate-length (UL = 0) device along the zigzag direction has the highest value 4119 μA/μm, which is 2.93 times larger than that in the same gate-length UL = 2 (1407 μA/μm). Hence, an overlong UL will weaken the performance of the device because the gate of the device cannot well control the UL region. Thus, a suitable length of UL for FET is very important.4) Remarkably, I_on values of the p-type devices (zigzag), even with a gate-length of 1 nm, can meet the requirements of HP applications in the ITRS for the next decade, with a value as high as 1934 μA/μm. To our knowledge, this is the best-performing device material reported at a gate length of 1 nm.5) Subthreshold swing (SS) evaluates the control ability of the gate in the subthreshold region. The better the gate control, the smaller the SS of the device is. The limit of SS for traditional FET is 60 mV/dec (at room temperature). Values of SS for ML SnS FET alone zigzag direction are less than those along the armchair direction because the leakage current is influenced by the effective mass.

Keywords:

PACS:

81.15.Gh	(Chemical vapor deposition (including plasma-enhanced CVD, MOCVD, ALD, etc.))
85.60.Gz	(Photodetectors (including infrared and CCD detectors))
81.07.-b	(Nanoscale materials and structures: fabrication and characterization)

Authors and contacts

1.
School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723001, China
2.
School of Physics, Peking University, Beijing 100867, China

Corresponding author: Guo Ying, guosophia@163.com ; Lü Jin, jinglu@pku.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. Z20230015, 12174238), the Natural Science Basic Research Project of Shaanxi Province, China (Grant No. 2022JM-051), and the Shaanxi University of Technology Talent Introduction Programm, China (Grant No. SLGRC202401).

FullText HTML

1. 引　言

Sm-Co基稀土永磁合金具有优异的高温磁性能, 被广泛应用于航空航天、新能源和国防军工等领域, 尤其是在工作温度超过500 ℃的条件下有着无可替代的地位^[1–6]. 因此, 其从1967年^[7]发现至今得到了广泛的关注和研究. Sm-Co基稀土永磁合金具有多种构型, 如SmCo₃, SmCo₅和Sm₂Co₁₇等. SmCo₃晶体结构可以由SmCo₅晶体结构通过Sm原子替代一些Co原子位点得到^[8], 其轴比c/a > 4.8^[9], 具有较大的晶格各向异性, 有利于磁性能的提高. 但是由于其较低的饱和磁化强度^[10]限制了最大磁能积, 从而阻碍了实际应用.

前人的研究表明, 掺杂合金元素是增强Sm-Co基稀土永磁合金的稳定性和磁性能的理想途径之一^[11–14]. 实验方面, Guo等^[15]采用Mn掺杂SmCo₇纳米晶结构, 发现Mn元素掺杂既可以提高结构稳定性, 又可以提升体系综合磁性能, 并探究出最佳掺杂浓度. Wang等^[16]以Fe, Ni和Nb为掺杂元素, 发现元素Fe和Ni可以有效提高Sm₅Co₁₉基纳米合金的综合磁性能. 理论计算方面, Das等^[17]基于第一性原理计算, 在SmCo₅体系中用Fe替代Co, 对不同浓度Fe掺杂SmCo₅合金的晶体结构稳定性、磁化强度和磁晶各向异性能进行研究, 计算发现高浓度Fe的掺杂不利于晶体结构稳定性, 同时使磁晶各向异性能下降, 但是可以提高体系的饱和磁化强度, Liu和Altounian^[18]也得出类似的结论. Landa等^[19]通过第一性原理计算, 在SmCo₅体系中用Fe替代大部分Co并使用Ni作为热力学稳定剂, 产生了具有优异磁性的SmCoNiFe₃合金, 磁化强度与SmCo₅合金相比从8.27 μ_B增至10 μ_B, 且具有高的居里温度和强的磁各向异性, 其磁各向异性能大小约为Nd₂Fe₁₄B合金的两倍. 无独有偶, Antoniou等^[20]也同样发现在SmCo₅体系中掺杂Ni可以提高掺杂体系的结构稳定性. 综上所述, 实验探索和理论计算两方面均表明掺杂过渡元素对于提升Sm-Co基稀土永磁合金的结构稳定性和磁性能有着非常重要的作用. 而第一性原理计算可以从体系的能量、晶体结构和电子结构等微观层面分析材料的稳定性和相关磁性能, 从而筛选出有利于提升Sm-Co基稀土永磁合金综合磁性能的掺杂元素, 有望从理论上为解决国内外稀土永磁新材料与新产品研发周期长和成本高的缺点提供有益指导.

目前, 国内外对于SmCo₃体系的实验探索和理论研究还非常少, 尤其是掺杂过渡元素的理论研究, 且掺杂元素种类和掺杂浓度在SmCo₃合金中的优先占位情况和对磁性能的影响机制尚不明确. 本文基于第一性原理计算, 在SmCo₃体系中设计多种过渡元素的掺杂构型. 通过对掺杂体系的晶格常数、掺杂元素的优先占位、电子结构和磁矩等方面的计算, 得出了过渡元素掺杂对SmCo₃体系结构稳定性和磁性能的作用关系, 并揭示了其微观机理. 此外, 基于掺杂结构稳定性、磁性能和掺杂元素的浓度三方面作用因素, 筛选出了最佳的掺杂元素和其掺杂浓度, 为新型高性能Sm-Co基永磁合金的研究和开发提供了有益的理论指导.

2. 模型构建和计算方法

2.1 模型构建

SmCo₃的晶体结构属于PuNi₃型三方(菱方)晶系, 空间群为R $\bar 3$ m. 如图1(a)所示, 其单胞中有36个原子, 3个Sm原子占据3a晶格位置, 6个Sm原子占据6c晶格位置, 3个Co原子占据3b晶格位置, 6个Co原子占据6c晶格位置, 18个Co原子占据18h晶格位置. 在三维坐标系中, 5个不等价的晶格位置坐标分别为: Sm(3a)原子坐标为(0, 0, 0); Sm(6c)原子坐标为(2/3, 1/3, 0.193699); Co(3b)原子坐标为(1/3, 2/3, 1/6); Co(6c)原子坐标为(1/3, 2/3, 0.332163); Co(18h)原子坐标为(2/3, 5/6, 0.253176), 其实验晶格常数为a = b = 5.050 Å, c = 24.590 Å, c/a = 4.8693^[10]. 掺杂过渡元素以Fe, Ni, Cu和Zr为代表, 建立未掺杂的SmCo₃晶胞(1×1×1)和掺杂原子百分比为3.7%的Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr)的模型, 而且在计算过程中考虑掺杂元素占据Co的3b, 6c和18h的晶格位置3种情况, 如图1所示.

$图 1 (a) SmCo3的晶体结构和Sm9Co26M晶体结构中M占据(b) 3b, (c) 6c和(d) 18h晶格位置\r\nFig. 1. Crystal structures of SmCo3 (a) and Sm9Co26M with M occupied (b) 3b site, (c) 6c and (d) 18h site.$

图 1 (a) SmCo₃的晶体结构和Sm₉Co₂₆M晶体结构中M占据(b) 3b, (c) 6c和(d) 18h晶格位置

Fig. 1. Crystal structures of SmCo₃ (a) and Sm₉Co₂₆M with M occupied (b) 3b site, (c) 6c and (d) 18h site.

下载: 全尺寸图片幻灯片

2.2 计算方法及相关参数

本研究的计算都是在VASP (Vienna ab-initio simulation package)^[21,22]中的密度泛函理论(DFT)框架下, 通过平面波基投影增强波(PAW)方法^[23,24]进行的. 使用广义梯度近似下的平面波(PBE)泛函^[25,26]. 平面波截断能设置为500 eV, 原子的自洽总能量收敛标准和几何(弛豫)结构力的收敛标准分别设为10^–5 eV和0.01 eV/Å, K点设置为9×9×3. 在结构弛豫过程中, 原子位置以及晶胞的形状和体积都被允许弛豫. 为了更好地描述d轨道和f轨道电子, 本文采用Dudarev提出的DFT+U方法^[27], 对Sm, Fe, Co, Ni, Cu和Zr原子的有效U值分别设置为4.70 eV, 1.83 eV, 2.22 eV, 1.61 eV, 4.80 eV和1.50 eV, 这些U值的选取文献[28, 29]. 计算时各原子的电子组态分别为: Sm-5s²5p⁶5d¹6s², Fe-3d⁶4s², Co-3d⁷4s², Ni-3d⁸4s², Cu-3d¹⁰4s¹和Zr-4d²5s².

Sm元素的赝势选择Sm_3进行计算, 也是VASP官方推荐使用的赝势^[30,31]. 该赝势将4f电子作为原子核内电子, 不作为价电子, 本文研究的是掺杂过渡元素种类和浓度对SmCo₃体系结构稳定性和磁矩的影响, 而不需要考虑4f-3d轨道电子相互作用的影响, 因此选择该赝势是合理的, 也是研究Sm-Co合金体系常用的处理方式.

3. 计算结果分析与讨论

3.1 掺杂元素占位与结构稳定性

首先, 对掺杂前后的模型进行结构优化, SmCo₃晶胞的晶格常数a = b = 5.0123 Å, c = 24.6424 Å, 体积V = 536.17 Å³, 与实验晶格参数a = b = 5.050 Å, c = 24.590 Å, V = 543.09 Å^3[10]相吻合. 如表1所列, 优化后的晶格常数与实验结果相差仅1%左右, 计算得到的SmCo₃晶胞体积比实验体积约小2%, 说明计算结果是可靠的. 由于掺杂元素的原子半径不同, SmCo₃掺杂体系的c/a略有不同, 且掺杂体系的晶胞体积随着掺杂元素的原子半径的增大而增大.

表 1 SmCo₃和Sm₉Co₂₆M的晶格参数和晶胞体积

Table 1. Lattice parameters and cell volumes of SmCo₃ and Sm₉Co₂₆M.

Model	Lattice parameter/Å			c/a	V/Å³
Model	a	b	c	c/a	V/Å³
SmCo₃	5.0123	5.0123	24.6424	4.9164	536.17
Experiments^[10]	5.050	5.050	24.590	4.8693	543.09
Sm₉Co₂₆Ni	4.9852	4.9852	24.5962	4.9338	536.76
Sm₉Co₂₆Fe	5.0428	5.0428	24.6771	4.8935	537.62
Sm₉Co₂₆Cu	4.9793	4.9793	24.5967	4.9398	538.11
Sm₉Co₂₆Zr	5.0670	5.0670	24.8397	4.9022	548.71

下载: 导出CSV

| 显示表格

为研究掺杂元素种类和占位对晶体结构稳定性的影响, 计算了掺杂Sm₉Co₂₆M体系的替代能, 具体公式为^[32]

$\begin{split} E\left({\mathrm{s}}{\mathrm{u}}{\mathrm{b}}\right)=\;&E\left({{\mathrm{S}}{\mathrm{m}}}_{9}{{\mathrm{C}}{\mathrm{o}}}_{27-n}{M}_{n}\right)-E\left({{\mathrm{S}}{\mathrm{m}}{\mathrm{C}}{\mathrm{o}}}_{3}\right)\\ &+nE\left({\mathrm{C}}{\mathrm{o}}\right)-nE\left(M\right), \end{split}$

(1)

式中, E(Sm₉Co_27–nM_n)表示掺杂体系的总能量, E(SmCo₃)表示未掺杂体系的总能量, E(Co)和E(M)分别表示Co和过渡金属元素M处于单质状态时每个原子的基态能量, n表示原子个数. 替代能为负值表示该元素掺杂较容易, 且有利于提升掺杂体系的结构稳定性; 反之正值则表示该元素掺杂较难同时不利于体系结构稳定性. 元素在不同晶格位置取代Co原子时, 优先占据替代能较低的位点, 不同掺杂体系的替代能如图2所示. 从图2可以发现Fe, Ni, Cu和Zr都优先占据18h位点. 替代能越低, 晶体结构越稳定, Ni, Cu和Fe的掺杂提高了SmCo₃结构的稳定性, 与之前的研究结果一致^[17,20]; 而Zr的掺杂则不利于SmCo₃结构的稳定性. 插图显示掺杂元素在优先占位的体系替代能, 由此可以得出掺杂元素对SmCo₃结构的稳定能力由大到小依次为: Ni, Cu, Fe, Zr.

$图 2 掺杂元素在3b, 6c和18h位置的替代能, 插图表示不同元素在优先占位的Sm9Co26M的替代能; 插图中红色虚线表示未掺杂SmCo3的基态能量, 其替代能为0作为参考值\r\nFig. 2. Substitution energy of the doping element at 3b, 6c and 18h sites, the insert shows the substitution energy of Sm9Co26M with different elements at the preferential site. The red dashed line in the illustration represents the ground state energy of undoped SmCo3, and its substitution energy is 0 eV as a reference value.$

图 2 掺杂元素在3b, 6c和18h位置的替代能, 插图表示不同元素在优先占位的Sm₉Co₂₆M的替代能; 插图中红色虚线表示未掺杂SmCo₃的基态能量, 其替代能为0作为参考值

Fig. 2. Substitution energy of the doping element at 3b, 6c and 18h sites, the insert shows the substitution energy of Sm₉Co₂₆M with different elements at the preferential site. The red dashed line in the illustration represents the ground state energy of undoped SmCo₃, and its substitution energy is 0 eV as a reference value.

下载: 全尺寸图片幻灯片

3.2 差分电荷密度分析

为阐明SmCo₃掺杂M (M = Fe, Ni, Cu, Zr)后体系中各类原子间的电荷转移机制, 计算了不同M原子掺杂在18h晶位(001)晶面的差分电荷密度, 结果如图3所示. 图3(c), (d)中Ni和Cu原子替代Co原子后, 在Co-Ni和Co-Cu原子间聚集大量电子, 且倾向于向掺杂原子Ni和Cu原子转移. 这表明图3(c), (d)中Co-Ni和Co-Cu原子间金属键得到明显加强, 且大于图3(a)中本身的Co—Co键, 从而提升了掺杂体系稳定性; 图3(e)中Zr原子替代Co原子后, Zr原子周围表现为明显的电子缺失, 且在Co-Zr原子间的差分电荷密度没有重叠, 表明Co-Zr原子间的相互作用减弱, 从而降低了掺杂体系稳定性; 图3(b)中Fe原子替代Co原子与未掺杂图3(a)中Co原子之间电荷密度差异并不明显; 上述分析与3.1节中替代能计算结果定性一致. 造成原子间电子转移情况的不同主要归因于每个原子的电负性各不相同, 从大到小排列顺序依次为Ni, Cu, Co, Fe, Zr.

$图 3 (001)面上(a) SmCo3和(b)—(e) Sm9Co26M (M = Fe, Ni, Cu, Zr)的差分电荷密度; 红色表示电荷的积累, 蓝色表示电荷的损耗\r\nFig. 3. Difference charge density of (a) SmCo3 and (b)—(e) Sm9Co26M (M = Fe, Ni, Cu, Zr) on (001) plane; the red indicates enrichment of electrons , blue indicates loss of electrons.$

图 3 (001)面上(a) SmCo₃和(b)—(e) Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr)的差分电荷密度; 红色表示电荷的积累, 蓝色表示电荷的损耗

Fig. 3. Difference charge density of (a) SmCo₃ and (b)—(e) Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr) on (001) plane; the red indicates enrichment of electrons , blue indicates loss of electrons.

下载: 全尺寸图片幻灯片

3.3 掺杂元素种类对基体总磁矩的影响

图4展现了各个掺杂体系总磁矩的计算结果, 图中虚线表示未掺杂SmCo₃体系的总磁矩为38.74 μ_B, 与实验值37.35 μ_B接近^[10]. 导致理论和实验磁矩的微小差别可能有以下两种原因: 一是在实验制备SmCo₃合金的过程中, 并不能完美地控制Sm:Co的含量比为1∶3; 另一种是计算中只考虑了原子的自旋磁矩, 而忽略了原子的轨道磁矩. 从图4可知: Ni, Cu和Zr元素的掺杂使体系的总磁矩减弱, 主要原因是Cu和Zr元素是非磁性元素; 而Ni作为磁性元素并没有增大体系的总磁矩, 这与传统磁性元素^[12,13]掺杂提升体系饱和磁化强度的认识不符; 磁性元素Fe掺杂使体系的总磁矩大幅增大. 此外, 当M原子在不同晶格位置取代Co原子时, 对总磁矩的影响也各不相同. 从图4可以看出, 掺杂元素Fe, Ni, Cu在不同位置总磁矩的差异很小. 但是掺杂元素Zr的体系在取代不同位置的Co时, 对总磁矩的影响很大, 其中掺杂在3b, 6c和18h晶格位置体系的总磁矩分别为38.73 μ_B, 37.43 μ_B和37.02 μ_B. 主要原因是Zr的原子半径远远大于Co原子和不同位置的晶格间隙不同, Zr原子掺杂后造成晶格内附近的Co原子位置发生不同程度的位移, 其中3b晶格位置间隙最小, 使周围Co原子之间距离减小, 增强了其周围Co原子的磁矩, 因此对体系总磁矩的减小程度产生了一定的补偿作用.

$图 4 SmCo3和Sm9Co26M (M = Fe, Ni, Cu, Zr)的总磁矩; 红色虚线表示未掺杂SmCo3体系的总磁矩为38.74 μB\r\nFig. 4. Total magnetic moments of SmCo3 and Sm9Co26M (M = Fe, Ni, Cu, Zr); the red dotted line indicates that the total magnetic moment of the undoped SmCo3 system is 38.74 μB.$

图 4 SmCo₃和Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr)的总磁矩; 红色虚线表示未掺杂SmCo₃体系的总磁矩为38.74 μ_B

Fig. 4. Total magnetic moments of SmCo₃ and Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr); the red dotted line indicates that the total magnetic moment of the undoped SmCo₃ system is 38.74 μ_B.

下载: 全尺寸图片幻灯片

3.4 偏态密度分析

为进一步揭示掺杂元素对SmCo₃体系总磁矩影响的微观机制, 选择最稳定的掺杂结构(18h)进行元素电子的偏态密度(partial density of state)分析, 如图5所示. 灰色阴影代表上下自旋态密度对称部分, 而红色阴影代表不同部分. 体系的净磁矩源于上下自旋态密度的不对称性. 如图5(a), (b)所示, Fe在18h晶格位置的上下自旋态密度对称性远低于Co, 说明Fe原子磁矩大于Co原子磁矩, 所以Fe掺杂可以提升体系的总磁矩; 图5(c)表示Ni元素在18h晶格位置的偏态密度, 由于其费米能级附近上下自旋电子数较少, 且上下自旋态密度对称性较高, 说明其磁矩很小, 远远小于Co元素在18h晶格位置的磁矩, 因此Ni的掺杂降低了体系的总磁矩; 图5(d), (e)分别表示Cu元素和Zr元素在18h晶格位置的偏态密度, 两者上下自旋态密度都高度对称. 前者在费米能级附近上下自旋电子数稀少, 对应红色区域表示的自旋差值部分也非常少, 且在较深能级处出现了上下自旋对称灰色部分, 说明Cu原子本身具有的磁矩非常小. 而后者在费米能级附近上下自旋电子数相对较多, 且红色自旋差值部分也较大, 但是远远小于Co元素在该位置的红色区域, 说明Zr原子具有一定大小的磁矩但小于Co原子的磁矩. 原子Cu, Zr与原子Co的本征自旋磁矩方向相反, 造成体系总磁矩下降, 但是Zr原子磁矩大于Cu原子磁矩, 所以Zr原子掺杂对体系的总磁矩影响更大. 经过上述PDOS分析, 与上文中磁矩计算结果一致.

$图 5 未掺杂体系中Co原子和掺杂体系中掺杂原子M在18h晶格位置的PDOS\r\nFig. 5. PDOS of Co atom in undoped system and doped atom M in different doped systems at 18h lattice position.$

图 5 未掺杂体系中Co原子和掺杂体系中掺杂原子M在18h晶格位置的PDOS

Fig. 5. PDOS of Co atom in undoped system and doped atom M in different doped systems at 18h lattice position.

下载: 全尺寸图片幻灯片

3.5 元素的筛选

如图6所示, 给出不同掺杂体系最稳定掺杂结构(18h)的替代能和总磁矩. 蓝色虚线代表未掺杂SmCo₃体系的总磁矩, 红色虚线代表未掺杂SmCo₅体系的替代能, 其值为0. 从图6可以看出, 红线左侧掺杂元素的体系具有较低的替代能, 有利于PuNi₃型晶体结构稳定性. 掺杂体系替代能从小到大排列依次是Ni , Cu, Fe以及不利于结构稳定的Zr. 所以从结构稳定性方面选择的最佳元素是Ni, Cu次之, 最后是Fe. 另一方面, 蓝色虚线以上的掺杂体系, 只有Sm₉Co₂₆Fe体系总磁矩比未掺杂的总磁矩高, 而蓝色虚线以下的体系总磁矩则比未掺杂的低. 因此, 在4种过渡金属元素中只有Fe元素掺杂既有利于提升结构稳定性, 又能提高SmCo₃体系的总磁矩.

$图 6 Sm9Co26M (M = Fe, Ni, Cu, Zr)合金的替代能和总磁矩\r\nFig. 6. Substitution energy and total magnetic moment of the Sm9Co26M (M = Fe, Ni, Cu, Zr) alloys.$

图 6 Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr)合金的替代能和总磁矩

Fig. 6. Substitution energy and total magnetic moment of the Sm₉Co₂₆M (M = Fe, Ni, Cu, Zr) alloys.

下载: 全尺寸图片幻灯片

3.6 掺杂元素浓度对结构稳定性和总磁矩的影响

通过上述对4种过渡元素掺杂SmCo₃体系进行系统计算分析, 得到掺杂不同种类过渡元素对SmCo₃体系结构稳定性、总磁矩和电子结构可以产生很大的影响. 在实际的Sm-Co稀土材料研发中, 仅仅了解掺杂元素的种类并不足以确保材料性能的最优化. 为实现最佳效果, 必须精确调控各组成的含量. 如在SmCo₅体系中掺杂Fe时, 在合适的浓度范围内可以提高饱和磁化强度和居里温度, 但过量掺杂会导致饱和磁化强度、居里温度和结构稳定性下降, 使其综合磁性能受到影响^[17,18,33]. 为了进一步分析4种过渡元素掺杂浓度对体系的影响, 并预测合适的掺杂浓度范围. 本文参照实验可行性掺杂浓度范围^[34], 在SmCo₃单原胞中, 掺杂原子百分比为0—22.22%范围内, 根据原子个数配比设置了计算原子百分比分别为x = 3.7%, 7.4%, 11.11%, 14.81%, 18.52%, 22.22%的掺杂体系.

如图7所示, 分别计算了SmCo_3–xM_x体系中替代能和体系总磁矩随着掺杂浓度的变化趋势(结构都是最稳定的), 红色(黑色)折线表示体系总磁矩(替代能)随着掺杂浓度的演化关系. 如图7(a)所示, 在SmCo_3–xFe_x体系中, 可看出随着Fe掺杂浓度的增大, 掺杂体系的总磁矩也随之增大, 同时替代能随之降低, 表明掺杂体系结构也越来越稳定; 图7(b), (c)分别表示SmCo_3–xNi_x和SmCo_3–xCu_x体系的演化情况, 可以看出, 随着掺杂浓度的增大, 体系的替代能几乎呈线性方式下降, 表明体系的结构越来越稳定, 但总磁矩下降; 图7(d)表示SmCo_3–xZr_x体系, 伴随着掺杂浓度的增大, 体系的替代能几乎呈线性方式上升, 表明体系的结构越来越不稳定, 同时掺杂体系的总磁矩也在下降. 综合4种SmCo₃基掺杂体系随着掺杂浓度变化的替代能和总磁矩特点, 可以预测在掺杂原子百分比为0—22.22%范围内, Fe元素为最佳的掺杂原子, 与3.5节叙述结果相对应, 且其最优的掺杂原子百分比为18.52%左右(考虑到实验的可行性掺杂浓度).

$图 7 SmCo3–xMx (M = Fe (a), Ni (b), Cu (c), Zr (d))体系替代能和体系总磁矩随掺杂浓度的变化\r\nFig. 7. Relationship between the substitution energy and total magnetic moment of SmCo3–xMx (M = Fe (a), Ni (b), Cu (c), Zr (d) system with doping concentration.$

图 7 SmCo_3–xM_x (M = Fe (a), Ni (b), Cu (c), Zr (d))体系替代能和体系总磁矩随掺杂浓度的变化

Fig. 7. Relationship between the substitution energy and total magnetic moment of SmCo_3–xM_x (M = Fe (a), Ni (b), Cu (c), Zr (d) system with doping concentration.

下载: 全尺寸图片幻灯片

4. 结　论

采用第一性原理计算方法, 研究过渡金属M = Fe, Ni, Cu, Zr掺杂的SmCo₃型永磁合金的结构稳定性和磁性能. 主要结论如下:

1)通过计算各掺杂体系的替代能可得, 过渡元素Fe, Ni, Cu和Zr都优先占据18h晶格位置, 元素Ni, Cu和Fe的掺杂可以降低掺杂体系的替代能, 从而提升SmCo₃体系结构稳定性; 而元素Zr掺杂不利于体系结构稳定性.

2)添加磁性元素Fe可以使体系总磁矩从38.74 μ_B增至40.16 μ_B, 而添加磁性元素Ni却使体系总磁矩从38.74 μ_B降低到37.67 μ_B. 这说明在SmCo₃体系并不是所有的铁磁元素都可以增加体系的总磁矩. 掺杂Cu和Zr这种非磁性元素通常会在一定程度上减弱SmCo₃体系的总磁矩.

3)在SmCo₃单原胞中, 掺杂原子百分比为0—22.22%范围内, 通过计算获得了SmCo₃不同掺杂体系结构稳定性和磁矩随着掺杂元素浓度变化规律. 最后筛选出既能提高掺杂体系结构稳定性, 又可以提升磁性能的元素: Fe, 并预测了其最合适的掺杂原子百分比为18.52%.

References

[1]	Cao W, Bu H M, Vinet M, Cao M, Takagi S, Hwang S, Ghani T, Banerjee K 2023 Nature 620 501 Google Scholar
[2]	Liu Y, Duan X D, Shin H J, Park S, Huang Y, Duan X F 2021 Nature 591 43 Google Scholar
[3]	Wang Y Y, Liu S Q, Li Q W, Quhe R, Yang C, Guo Y, Zhang X Y, Pan Y Y, Li J S, Zhang H, Xu L, Shi B W, Tang H, Li Y, Yang J, Zhang Z Y, Xiao L, Pan F, Lu J 2021 Rep. Prog. Phys. 84 056501 Google Scholar
[4]	Jayachandran D, Pendurthi R, Sadaf M U K, Sakib N U, Pannone A, Chen C, Han Y, Trainor N, Kumari S, Mc Knight T V, Redwing J M, Yang Y, Das S 2024 Nature 625 276 Google Scholar
[5]	https://irds.ieee.org/editions/2022
[6]	Desai S B, Madhvapathy S R, Sachid A B, Llinas J P, Wang Q X, Ahn G H, Pitner G, Kim M J, Bokor J, Hu C H, Wong H S P, Javey A 2016 Science 354 99 Google Scholar
[7]	Wu F, Tian H, Shen Y, Hou Z, Ren J, Gou G Y, Sun Y B, Yang Y, Ren T L 2022 Nature 603 259 Google Scholar
[8]	Jiang J F, Xu L, Qiu C G, Peng L M 2023 Nature 616 470 Google Scholar
[9]	Xin C, Zheng J X, Su Y T, Li S K, Zhang B K, Feng Y C, Pan F 2016 J. Phys. Chem. C 120 22663 Google Scholar
[10]	Sarkar A S, Konidakis I, Gagaoudakis E, Maragkakis G M, Psilodimitrakopoulos S, Katerinopoulou D, Sygellou L, Deligeorgis G, Binas V, Oikonomou I M, Komninou P, Kiriakidis G, Kioseoglou G, Stratakis E 2022 Adv. Sci. 10 2201842
[11]	Li S B, Xiao W J, Pan Y Y, Jie J S, Xin C, Zheng J X, Lu J, Pan F 2018 J. Phys. Chem. C 122 12322
[12]	Chang Y R, Nishimura T, Taniguchi T, Watanabe K, Nagashio K 2022 ACS Appl. Mater. Interfaces 14 19928 Google Scholar
[13]	Sucharitakul S, Rajesh Kumar U, Sankar R, Chou F C, Chen Y T, Wang C H, He C, He R, Gao X P 2016 Nanoscale 8 19050 Google Scholar
[14]	Dragoman M, Dinescu A, Avram A, Dragoman D, Vulpe S, Aldrigo M, Braniste T, Suman V, Rusu E, Tiginyanu I 2022 Nanotechnology 33 405207 Google Scholar
[15]	Pandit A, Hamad B 2021 Appl. Surface Sci. 538 147911 Google Scholar
[16]	Xu L, Yang M, Wang S J, Feng Y P 2017 Phys. Rev. B 95 235434 Google Scholar
[17]	Zhao P D, Kiriya D, Azcatl A, Zhang C X, Tosun M, Liu Y S, Hettick M, Kang J S, McDonnell S, KC S, Guo J H, Cho K, Wallace R M, Javey A 2014 ACS Nano 8 10808 Google Scholar
[18]	Fathipour S, Pandey P, Fullerton-Shirey S, Seabaugh A 2016 J. Appl. Phys. 120 234902 Google Scholar
[19]	Quhe R, Li Q H, Zhang Q X, Wang Y Y, Zhang H, Li J Z, Zhang X Y, Chen D X, Liu K H, Ye Y, Dai L, Pan F, Lei M, Lu J 2018 Phys. Rev. Appl. 10 024022 Google Scholar
[20]	Das S, Chen H Y, Penumatcha A V, Appenzeller J 2013 Nano Lett. 13 100 Google Scholar
[21]	Kaushik N, Nipane A, Basheer F, Dubey S, Grover S, Deshmukh M M, Lodha S 2014 Appl. Phys. Lett. 105 113505 Google Scholar
[22]	Liu Y, Guo J, Zhu E B, Liao L, Lee S J, Ding M N, Shakir I, Gambin V, Huang Y, Duan X F 2018 Nature 557 696 Google Scholar
[23]	Kim C, Moon I, Lee D, Choi M S, Ahmed F, Nam S, Cho Y, Shin H J, Park S, Yoo W J 2017 ACS Nano 11 1588 Google Scholar
[24]	Pan Y Y, Wang Y Y, Ye M, Quhe R, Zhong H X, Song Z G, Peng X Y, Yu D P, Yang J B, Shi J J, Lu J 2016 Chem. Mater. 28 2100 Google Scholar
[25]	Pan Y Y, Dan Y, Wang Y Y, Ye M, Zhang H, Quhe R, Zhang X Y, Li J Z, Guo W L, Yang L, Lu J 2017 ACS Appl. Mater. Interfaces 9 12694 Google Scholar
[26]	Zhang X Y, Pan Y Y, Ye M, Quhe R, Wang Y Y, Guo Y, Zhang H, Dan Y, Song Z G, Li J Z, Yang J B, Guo W L, Lu J 2017 Nano Res. 11 707
[27]	Das S, Zhang W, Demarteau M, Hoffmann A, Dubey M, Roelofs A 2014 Nano Lett. 14 5733 Google Scholar
[28]	Liu H, Neal A T, Zhu Z, Luo Z, Xu X F, Tomanek D, Ye P D 2014 ACS Nano 8 4033 Google Scholar
[29]	Guo Y, Pan F, Zhao G Y, Ren Y J, Yao B B, Li H, Lu J 2020 Nanoscale 12 15443 Google Scholar
[30]	Guo Y, Zhao G Y, Pan F, Quhe R, Lu J 2022 J. Electron. Mater. 51 4824 Google Scholar

图 1 (a) ML SnS的最佳优化结构侧视和俯视图, 黑色虚线的矩形框为单胞结构; (b) ML SnS的能带结构图, 费米能级(蓝色虚线)能量为零, Γ-X和Γ-Y分别表示armchair方向和zigzag方向; (c) ML SnS MOSFET器件的结构示意图

Figure 1. (a) Side and top view of the optimized ML SnS structure, the black dash rectangle represents the primitive cell; (b) band structure of the ML SnS, the blue dashed line indicates the Fermi level, and Γ-X and Γ-Y representing the armchair and zigzag directions, respectively; (c) schematic diagram of the ML SnS MOSFET.

DownLoad: Full-Size Img PowerPoint

图 2 测试器件掺杂浓度的转移特性曲线, 不同源极和漏极掺杂电子/空穴(N_e (N_h))浓度下n型和p型5 nm栅长DG ML SnS MOSFET沿zigzag (a)和armchair (b)方向的转移特性曲线

Figure 2. Transfer characteristics of the n- and p-type 5 nm gate-length DG ML SnS MOSFET for different source and drain doping concentrations of electron/hole (N_e (N_h)) along the zigzag (a) and armchair (b) directions.

DownLoad: Full-Size Img PowerPoint

图 3 n型和p型DG ML SnS MOSFET器件转移特性曲线　(a) 1—2 nm, (b) 3—5 nm栅极长沿armchair方向转移特性曲线; (c) 1—2 nm, (d) 3—5 nm栅极长沿zigzag方向的转移特性

Figure 3. Transfer characteristics curves of the n- and p-type DG ML SnS MOSFET: (a) 1–2 nm, (b) 3–5 nm gate-length along the armchair direction; (c) 1–2 nm, (d) 3–5 nm gate-length along the zigzag directions.

DownLoad: Full-Size Img PowerPoint

图 4 亚5 nm的n型和p型DG ML SnS MOSFET开态电流(a), (b)和亚阈值摆幅(c), (d)与栅极长度的关系

Figure 4. On-state current (a), (b) and subthreshold swing (c), (d) of the sub-5 nm DG n-type and p-type DG ML SnS MOSFET as a function of the gate-length.

DownLoad: Full-Size Img PowerPoint

图 5 亚5 nm的n型(a)和p型(b)DG ML SnS MOSFET的开态电流与栅极长度的关系

Figure 5. On-state current of the sub-5 nm n-type (a) and p-type (b) DG ML SnS MOSFET as a function of the L_g, the set of red and green lines indicate the armchair and zigzag directions, respectively.

DownLoad: Full-Size Img PowerPoint

图 6 n型和p型MOSFET在HP标准下亚5 nm栅长下的开态电流与ML二维沟道材料有效质量的关系, 所有数据均采基于密度泛函理论的量子输运模拟计算

Figure 6. On-state current of n- and p-type MOSFET for high-performance applications at sub-5 nm gate-lengths versus the effective mass of ML two dimensional channel materials, all the data are calculated by ab initio quantum transport simulations.

DownLoad: Full-Size Img PowerPoint

表 1 不同的测试掺杂浓度对应的器件开态电流

Table 1. I_on of the SnS MOSFET with different doping concentrations.

N_e/N_h doping concentration/cm^–2	I_on (HP/LP)/(μA·μm^–1)
	Zigzag		Armchair
	n-type	p-type	n-type	p-type
1×10¹²	32.24/33.69	45/40.9	129.66/21.55	31.82/35.72
5×10¹²	232.26/51.9	310.69/95.35	741.82/17.33	185.95/32.23
1×10¹³	1105.66/35.52	756.83/105.06	970.15/8.42	379.27/42.5
5×10¹³	1330.51/0.425	2693.37/0.12	1216.66/0.061	973.2/0.026
8×10¹³	1207.55/0.082	280.42/0.0013	1020.21/0.0074	676.8/5.37

DownLoad: CSV

表 2 n型和p型DG ML SnS MOSFET器件开态电流、开关比和亚阈值摆幅与ITRS HP和LP标准(2023版)的比较

Table 2. Benchmark of the ballistic performances upper limit of the sub-5 nm DG ML SnS MOSFET (zigzag- and armchair-directed) for HP and LP applications against the ITRS requirements (2023 version).

	L_g/nm	UL/nm	SS/(mV·dec^–1)		I_on/(μA·μm^–1)		I_on/I_off			I_on/(μA·μm^–1)		I_on/I_off
HP	5.1	0	—		900		9.00×10³		LP	295		5.9×10⁶
			n-type	p-type	n-type	p-type	n-type	p-type		n-type	p-type	n-type	p-type
Armchair	1	0	843	719	—	—	—	—		—	—	—	—
		2	260	276	31	56	3.1×10²	5.6×10²		—	—	—	—
		4	120	122	407	209	4.07×10³	2.09×10³		2.06	6.34	4.12×10⁴	1.27×10⁵
	2	0	561	418	—	0.7	—	7.0×10²		—	—	—	—
		2	212	172	41	319	4.1	3.19×10³		0.007	0.0031	1.40×10²	61.6
		4	101	90	938	285	9.38×10³	2.85×10³		45.7	50.83	9.14×10⁵	1.02×10⁶
	3	0	293	254	5.18	13	5.18×10¹	1.30×10²		—	—	—	—
	3	2	118	101	1204	665	1.20×10⁴	6.65×10³		0.406	0.86	8.12×10³	1.72×10⁴
	4	0	189	241	226	292	2.26×10³	2.92×10³		—	—	—	—
	4	2	93	78	2369	817	2.37×10⁴	8.17×10³		110.58	92.08	2.21×10⁶	1.84×10⁶
	5	0	125	112	1113	924	1.11×10⁴	9.24×10³		0.1	0.03	2.0×10³	6.00×10²
Zigzag	1	0	904	603	—	—	—	—		—	—	—	—
		2	259	96	72	1934	7.20×10²	1.93×10⁴		—	69.3	—	1.39×10⁶
		4	107	85	390	545	3.90×10³	5.45×10³		10.04	79.2	2.01×10⁵	1.58×10⁶
	2	0	530	252	—	—	—	—
		2	147	101	509	1236	5.09×10³	1.24×10⁴		0.035	0.0021	7.0×10²	42.8
		4	89	78	621	693	6.21×10³	6.93×10³		85.02	136.51	1.70×10⁶	2.73×10⁶
	3	0	233	89	4.04	4119	4.04×10¹	4.12×10⁴		—	171.91	—	3.44×10⁶
	3	2	117	66	1168	1407	1.17×10⁴	1.41×10⁴		7.73	516.18	1.55×10⁵	1.03×10⁷
	4	0	166	106	322	1648	3.22×10³	1.65×10⁴		—	—	—	—
	4	2	85	70	1843	1874	1.84×10⁴	1.87×10⁴		231.06	271.4	4.62×10⁶	5.43×10⁶
	5	0	118	78	1280	2463	1.28×10⁴	2.46×10⁴		0.67	0.13	1.34×10⁴	2.6×10³

DownLoad: CSV

[1]	Cao W, Bu H M, Vinet M, Cao M, Takagi S, Hwang S, Ghani T, Banerjee K 2023 Nature 620 501 Google Scholar
[2]	Liu Y, Duan X D, Shin H J, Park S, Huang Y, Duan X F 2021 Nature 591 43 Google Scholar
[3]	Wang Y Y, Liu S Q, Li Q W, Quhe R, Yang C, Guo Y, Zhang X Y, Pan Y Y, Li J S, Zhang H, Xu L, Shi B W, Tang H, Li Y, Yang J, Zhang Z Y, Xiao L, Pan F, Lu J 2021 Rep. Prog. Phys. 84 056501 Google Scholar
[4]	Jayachandran D, Pendurthi R, Sadaf M U K, Sakib N U, Pannone A, Chen C, Han Y, Trainor N, Kumari S, Mc Knight T V, Redwing J M, Yang Y, Das S 2024 Nature 625 276 Google Scholar
[5]	https://irds.ieee.org/editions/2022
[6]	Desai S B, Madhvapathy S R, Sachid A B, Llinas J P, Wang Q X, Ahn G H, Pitner G, Kim M J, Bokor J, Hu C H, Wong H S P, Javey A 2016 Science 354 99 Google Scholar
[7]	Wu F, Tian H, Shen Y, Hou Z, Ren J, Gou G Y, Sun Y B, Yang Y, Ren T L 2022 Nature 603 259 Google Scholar
[8]	Jiang J F, Xu L, Qiu C G, Peng L M 2023 Nature 616 470 Google Scholar
[9]	Xin C, Zheng J X, Su Y T, Li S K, Zhang B K, Feng Y C, Pan F 2016 J. Phys. Chem. C 120 22663 Google Scholar
[10]	Sarkar A S, Konidakis I, Gagaoudakis E, Maragkakis G M, Psilodimitrakopoulos S, Katerinopoulou D, Sygellou L, Deligeorgis G, Binas V, Oikonomou I M, Komninou P, Kiriakidis G, Kioseoglou G, Stratakis E 2022 Adv. Sci. 10 2201842
[11]	Li S B, Xiao W J, Pan Y Y, Jie J S, Xin C, Zheng J X, Lu J, Pan F 2018 J. Phys. Chem. C 122 12322
[12]	Chang Y R, Nishimura T, Taniguchi T, Watanabe K, Nagashio K 2022 ACS Appl. Mater. Interfaces 14 19928 Google Scholar
[13]	Sucharitakul S, Rajesh Kumar U, Sankar R, Chou F C, Chen Y T, Wang C H, He C, He R, Gao X P 2016 Nanoscale 8 19050 Google Scholar
[14]	Dragoman M, Dinescu A, Avram A, Dragoman D, Vulpe S, Aldrigo M, Braniste T, Suman V, Rusu E, Tiginyanu I 2022 Nanotechnology 33 405207 Google Scholar
[15]	Pandit A, Hamad B 2021 Appl. Surface Sci. 538 147911 Google Scholar
[16]	Xu L, Yang M, Wang S J, Feng Y P 2017 Phys. Rev. B 95 235434 Google Scholar
[17]	Zhao P D, Kiriya D, Azcatl A, Zhang C X, Tosun M, Liu Y S, Hettick M, Kang J S, McDonnell S, KC S, Guo J H, Cho K, Wallace R M, Javey A 2014 ACS Nano 8 10808 Google Scholar
[18]	Fathipour S, Pandey P, Fullerton-Shirey S, Seabaugh A 2016 J. Appl. Phys. 120 234902 Google Scholar
[19]	Quhe R, Li Q H, Zhang Q X, Wang Y Y, Zhang H, Li J Z, Zhang X Y, Chen D X, Liu K H, Ye Y, Dai L, Pan F, Lei M, Lu J 2018 Phys. Rev. Appl. 10 024022 Google Scholar
[20]	Das S, Chen H Y, Penumatcha A V, Appenzeller J 2013 Nano Lett. 13 100 Google Scholar
[21]	Kaushik N, Nipane A, Basheer F, Dubey S, Grover S, Deshmukh M M, Lodha S 2014 Appl. Phys. Lett. 105 113505 Google Scholar
[22]	Liu Y, Guo J, Zhu E B, Liao L, Lee S J, Ding M N, Shakir I, Gambin V, Huang Y, Duan X F 2018 Nature 557 696 Google Scholar
[23]	Kim C, Moon I, Lee D, Choi M S, Ahmed F, Nam S, Cho Y, Shin H J, Park S, Yoo W J 2017 ACS Nano 11 1588 Google Scholar
[24]	Pan Y Y, Wang Y Y, Ye M, Quhe R, Zhong H X, Song Z G, Peng X Y, Yu D P, Yang J B, Shi J J, Lu J 2016 Chem. Mater. 28 2100 Google Scholar
[25]	Pan Y Y, Dan Y, Wang Y Y, Ye M, Zhang H, Quhe R, Zhang X Y, Li J Z, Guo W L, Yang L, Lu J 2017 ACS Appl. Mater. Interfaces 9 12694 Google Scholar
[26]	Zhang X Y, Pan Y Y, Ye M, Quhe R, Wang Y Y, Guo Y, Zhang H, Dan Y, Song Z G, Li J Z, Yang J B, Guo W L, Lu J 2017 Nano Res. 11 707
[27]	Das S, Zhang W, Demarteau M, Hoffmann A, Dubey M, Roelofs A 2014 Nano Lett. 14 5733 Google Scholar
[28]	Liu H, Neal A T, Zhu Z, Luo Z, Xu X F, Tomanek D, Ye P D 2014 ACS Nano 8 4033 Google Scholar
[29]	Guo Y, Pan F, Zhao G Y, Ren Y J, Yao B B, Li H, Lu J 2020 Nanoscale 12 15443 Google Scholar
[30]	Guo Y, Zhao G Y, Pan F, Quhe R, Lu J 2022 J. Electron. Mater. 51 4824 Google Scholar

[1]	Zhang Wei-Xi, Li Yong, Tian Chang-Hai, She Yan-Chao. Room-temperature quantum anomalous Hall effect in monolayer BaPb with large magnetocrystalline anisotropy energies. Acta Physica Sinica, 2021, 70(15): 157502. doi: 10.7498/aps.70.20210014
[2]	Song Hang, Liu Jie, Chen Chao, Ba Long. Graphene-based field effect transistor with ion-gel film gate. Acta Physica Sinica, 2019, 68(9): 097301. doi: 10.7498/aps.68.20190058
[3]	Wu Chun-Yan, Du Xiao-Wei, Zhou Lin, Cai Qi, Jin Yan, Tang Lin, Zhang Han-Ge, Hu Guo-Hui, Jin Qing-Hui. Characterization and preliminary application of top-gated graphene ion-sensitive field effect transistors. Acta Physica Sinica, 2016, 65(8): 080701. doi: 10.7498/aps.65.080701
[4]	Shi Lei, Feng Shi-Wei, Shi Bang-Bing, Yan Xin, Zhang Ya-Min. Degradation induced by voltage and current for AlGaN/GaN high-electron mobility transistor under on-state stress. Acta Physica Sinica, 2015, 64(12): 127303. doi: 10.7498/aps.64.127303
[5]	Fan Min-Min, Xu Jing-Ping, Liu Lu, Bai Yu-Rong, Huang Yong. Models on threshold voltage/subthreshold swing and structural design of high-k gate dielectric GeOI MOSFET. Acta Physica Sinica, 2014, 63(8): 087301. doi: 10.7498/aps.63.087301
[6]	Tang Xin-Yue, Gao Hong, Pan Si-Ming, Sun Jian-Bo, Yao Xiu-Wei, Zhang Xi-Tian. Electrical characteristics of individual In-doped ZnO nanobelt field effect transistor. Acta Physica Sinica, 2014, 63(19): 197302. doi: 10.7498/aps.63.197302
[7]	Zhao Xiao-Hui, Cai Li, Zhang Peng. A HSPICE model of carbon nanotube field effect transistor. Acta Physica Sinica, 2013, 62(13): 130506. doi: 10.7498/aps.62.130506
[8]	Zhao Xiao-Hui, Cai Li, Zhang Peng. Modeling of carbon nanotube field effect transistor with phonon scattering. Acta Physica Sinica, 2013, 62(10): 100301. doi: 10.7498/aps.62.100301
[9]	Liu Jiang-Tao, Huang Jie-Hui, Xiao Wen-Bo, Hu Ai-Rong, Wang Jian-Hui. The influence of gate voltage on electron transport in the graphene field-effect transistor under strong laser field. Acta Physica Sinica, 2012, 61(17): 177202. doi: 10.7498/aps.61.177202
[10]	Sun Peng, Du Lei, Chen Wen-Hao, He Liang, Zhang Xiao-Fang. A radiation degradation model of metal-oxide-semiconductor field effect transistor. Acta Physica Sinica, 2012, 61(10): 107803. doi: 10.7498/aps.61.107803
[11]	Li Li, Liu Hong-Xia, Yang Zhao-Nian. Threshold-voltage and hole-sheet-density model of quantum well Si/SiGe/Si p field effect transistor. Acta Physica Sinica, 2012, 61(16): 166101. doi: 10.7498/aps.61.166101
[12]	Zhou Hai-Liang, Chi Ya-Qing, Zhang Min-Xuan, Fang Liang. Performance optimization of carbon nanotube field effect transistors based on stair-case doping strategy. Acta Physica Sinica, 2010, 59(11): 8104-8112. doi: 10.7498/aps.59.8104
[13]	Zhao Qi-Di, Zhang Zhen-Hua. Electronic transport properties of single-walled carbon nanotubes under a low bias. Acta Physica Sinica, 2010, 59(11): 8098-8103. doi: 10.7498/aps.59.8098
[14]	Chen Li-Bing, Tan Peng, Dong Shao-Guang, Lu Hong. Controlled implementation of a nonlocal and open-target destination quantum controlled-Not （CNOT） gate using partially entangled pairs. Acta Physica Sinica, 2009, 58(10): 6772-6778. doi: 10.7498/aps.58.6772
[15]	Zhang Wei, Li Meng-Ke, Wei Qiang, Cao Lu, Yang Zhi, Qiao Shuang-Shuang. Fabrication and I-V characteristics of ZnO nanowire-based field effect transistors. Acta Physica Sinica, 2008, 57(9): 5887-5892. doi: 10.7498/aps.57.5887
[16]	Liu Kui, Ding Hong-Lin, Zhang Xian-Gao, Yu Lin-Wei, Huang Xin-Fan, Chen Kun-Ji. Simulation of a triple-gate single electron FET memory with a quantum dot floating gate and a quantum wire channel. Acta Physica Sinica, 2008, 57(11): 7052-7056. doi: 10.7498/aps.57.7052
[17]	Li Ping-Jian, Zhang Wen-Jing, Zhang Qi-Feng, Wu Jin-Lei. Nanoelectronic logic circuits with carbon nanotube transistors. Acta Physica Sinica, 2007, 56(2): 1054-1060. doi: 10.7498/aps.56.1054
[18]	Xu Zhang-Cheng, Jia Guo-Zhi, Sun Liang, Yao Jiang-Hong, Xu Jing-Jun, J. M. Hvam, Wang Zhan-Guo. Time-resolved photoluminescence of sub-monolayer InGaAs/GaAs quantum-dot-quantum-well heterostructures. Acta Physica Sinica, 2005, 54(11): 5367-5371. doi: 10.7498/aps.54.5367
[19]	Yang Lin-An, Zhang Yi-Men, Yu Chun-Li, Zhang Yu-Ming. Trapping effect modeling for SiC power MESFETs. Acta Physica Sinica, 2003, 52(2): 302-306. doi: 10.7498/aps.52.302
[20]	LI HONG-WEI, WANG TAI-HONG. CURRENT TRANSPORT PROPERTIES OF GaAs SCHOTTKY DIODE CONTAINING InAs SELF-ASSEMBLED QUANTUM DOTS. Acta Physica Sinica, 2001, 50(2): 262-267. doi: 10.7498/aps.50.262

Catalog

Vol. 73, Issue 20 - 2024-10-20

Metrics

Abstract views: 2379
PDF Downloads: 115

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板