Title |
Theoretical Investigation of Biaxially Tensile-Strained Germanium Nanowires
|
---|---|
Published in |
Discover Nano, July 2017
|
DOI | 10.1186/s11671-017-2243-1 |
Pubmed ID | |
Authors |
Zhongyunshen Zhu, Yuxin Song, Qimiao Chen, Zhenpu Zhang, Liyao Zhang, Yaoyao Li, Shumin Wang |
Abstract |
We theoretically investigate highly tensile-strained Ge nanowires laterally on GaSb. Finite element method has been used to simulate the residual elastic strain in the Ge nanowire. The total energy increment including strain energy, surface energy, and edge energy before and after Ge deposition is calculated in different situations. The result indicates that the Ge nanowire on GaSb is apt to grow along 〈100〉 rather than 〈110〉 in the two situations and prefers to be exposed by {105} facets when deposited a small amount of Ge but to be exposed by {110} when the amount of Ge exceeds a critical value. Furthermore, the conduction band minima in Γ-valley at any position in both situations exhibits lower values than those in L-valley, leading to direct bandgap transition in Ge nanowire. For the valence band, the light hole band maxima at Γ-point is higher than the heavy hole band maxima at any position and even higher than the conduction band minima for the hydrostatic strain more than ∼5.0%, leading to a negative bandgap. In addition, both electron and hole mobility can be enhanced by owing to the decrease of the effective mass under highly tensile strain. The results suggest that biaxially tensile-strained Ge nanowires hold promising properties in device applications. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 29% |
Professor > Associate Professor | 3 | 21% |
Other | 1 | 7% |
Professor | 1 | 7% |
Student > Doctoral Student | 1 | 7% |
Other | 2 | 14% |
Unknown | 2 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 4 | 29% |
Materials Science | 4 | 29% |
Computer Science | 1 | 7% |
Physics and Astronomy | 1 | 7% |
Unknown | 4 | 29% |