论文标题
使用有限元方法对量子点进行建模
Modelling of Quantum Dots with the Finite Element Method
论文作者
论文摘要
考虑到具有不同几何形状的量子点的制造过程中的实验结果越来越多,以及可用于调查具有非平凡几何形状的量子点的大多数数值方法需要较大的计算能力,有限元方法成为建模半径QD的一种非常有吸引力的工具。在当前文章中,作者使用FEM来获得以下GAAS结构的前26个概率密度和能量值:矩形,球形,圆柱形,椭圆形,球形,球形QD和圆锥形QD,量子环,量子环,纳米螺旋体和纳米骨骼。将数值计算的结果与精确的分析溶液进行了比较,并获得了良好的偏差。获得基态能量对元素大小的依赖性,以找到研究结构的最佳参数。上述计算结果用于获得对大小量化对QD形状的影响的有价值的见解。另外,考虑到分段木材 - 撒克逊电势对潜在深度的扩散效应时,获得了球形CDSE/CDS量子点的波形和能量。通过使用正常木材 - 撒克逊电位,可以获得有效质量和介电介电常数的扩散。在2.2nm的核心尺寸下获得了具有准型II带比对的结构。该结果与实验数据一致。
Considering the increasing number of experimental results in the manufacturing process of quantum dots with different geometries, and the fact that most numerical methods that can be used to investigate quantum dots with non-trivial geometries require large computational capacities the finite element method becomes an incredibly attractive tool for modeling semiconductor QDs. In the current article, the authors have used the FEM to obtain the first twenty-six probability densities and energy values for the following GaAs structures: rectangular, spherical, cylindrical, ellipsoidal, spheroidal, and conical QDs, quantum rings, nanotadpoles, and nanostars. The results of the numerical calculations were compared with the exact analytical solutions and a good deviation was obtained. The ground states energies dependence on the element size was obtained to find the optimal parameter for the investigated structures. The abovementioned calculation results were used to obtain valuable insight into the effects of the size quantization s dependence on the shape of the QDs. Additionally, the wavefunctions and energies of spherical CdSe/CdS quantum dots were obtained while taking into account the diffusion effects on the potential depth with the use of a piecewise Woods-Saxon potential. The diffusion of the effective mass and the dielectric permittivity is obtained with the use of a normal Woods-Saxon potential. A structure with a quasi-type-II band alignment was obtained at the core size of 2.2nm. This result is consistent with the experimental data.