Monte-Carlo study of electronic transport in non-σh-symmetric two-dimensional materials: Silicene and germanene

硅烯 日耳曼 凝聚态物理 散射 声子 物理 电子 材料科学 石墨烯 量子力学 光电子学
作者
Gautam Gaddemane,William G. Vandenberghe,Maarten L. Van de Put,Edward Chen,Massimo V. Fischetti
出处
期刊:Journal of Applied Physics [American Institute of Physics]
卷期号:124 (4) 被引量:31
标识
DOI:10.1063/1.5037581
摘要

The critical role of silicon and germanium in the semiconductor industry, combined with the need for extremely thin channels for scaled electronic devices, has motivated research towards monolayer silicon (silicene) and monolayer germanium (germanene). The lack of horizontal mirror (σh) symmetry in these two-dimensional crystals results in a very strong coupling—in principle diverging—of electrons to long wavelength flexural branch (ZA) phonons. For semi-metallic Dirac materials lacking σh symmetry, like silicene and germanene, this effect is further exacerbated by strong back-scattering at the Dirac cone. In order to gauge the intrinsic transport limitations of silicene and germanene, we perform low- and high-field transport studies using first-principles Monte-Carlo simulations. We take into account the full band structure and solve the electron-phonon matrix elements to treat correctly the material anisotropy and wavefunction overlap-integral effects. We avoid the divergence of the ZA phonon scattering rate through the introduction of an optimistic (1 nm long wavelength) cutoff for the ZA phonons. Even with this cutoff for long-wavelength ZA phonons, essentially prohibiting intravalley scattering, we observe that intervalley ZA phonon scattering dominates the overall transport properties. We obtain relatively large electron mobilities of 701 cm2 V−1 s−1 for silicene and 2327 cm2 V−1 s−1 for germanene. Our results show that silicene and germanene may exhibit electronic transport properties that could surpass those of many other two-dimensional materials, if intravalley ZA phonon scattering could be suppressed.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Jasper应助阿符采纳,获得10
1秒前
烟花应助文艺小蕊采纳,获得10
2秒前
单纯的雁芙完成签到,获得积分10
2秒前
2秒前
xuan发布了新的文献求助10
2秒前
5秒前
称心不尤发布了新的文献求助10
5秒前
5秒前
仓颉完成签到 ,获得积分20
6秒前
6秒前
7秒前
7秒前
thirty发布了新的文献求助10
8秒前
MQL发布了新的文献求助10
9秒前
Honcy完成签到,获得积分20
10秒前
will_li完成签到,获得积分10
10秒前
CN发布了新的文献求助10
11秒前
11秒前
12秒前
12秒前
xuan发布了新的文献求助10
12秒前
13秒前
molihuakai应助Honcy采纳,获得10
14秒前
14秒前
全球发布了新的文献求助10
16秒前
颂歌998发布了新的文献求助10
16秒前
cdercder应助kk采纳,获得10
17秒前
科目三应助草上飞采纳,获得10
18秒前
lucky发布了新的文献求助30
18秒前
mlzmlz发布了新的文献求助10
18秒前
20秒前
xuan发布了新的文献求助10
21秒前
顾矜应助成功的院士采纳,获得10
22秒前
23秒前
全球完成签到,获得积分10
23秒前
lailai发布了新的文献求助10
27秒前
Honcy发布了新的文献求助10
28秒前
YLLL完成签到 ,获得积分10
29秒前
dde发布了新的文献求助10
31秒前
xuan发布了新的文献求助10
32秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583572
求助须知:如何正确求助?哪些是违规求助? 9162318
关于积分的说明 19606672
捐赠科研通 7165624
什么是DOI,文献DOI怎么找? 3266302
关于科研通互助平台的介绍 2431200
邀请新用户注册赠送积分活动 2257778