Effect of long-range Coulomb interactions on electron transport in a nanoscale one-dimensional ring

Shiokawa, Taro; Muraguchi, Masahiro; Shiraishi, Kenji

doi:10.7567/1347-4065/ab49aa

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…In our previous studies, the usefulness of electron time evolution calculation for design of nanoscale devices has been shown. [18][19][20][21] The advantages of the electron time evolution calculation are that the time evolution of the electron wavefunction can be visualized and that the time evolution of the wavefunction is treated as an initial value problem, so that the phenomena that occur with the time evolution can be naturally captured. For some of these advantages, device design using the time evolution calculation presents several challenges.…”

Section: Introductionmentioning

confidence: 99%

Investigation of features for prediction modeling of nanoscale conduction with time-dependent calculation of electron wave packet

Muraguchi

Nakaya

Kawahara

et al. 2022

Jpn. J. Appl. Phys.

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The model to predict the electron transmission probability from the random impurity distribution in a two-dimensional nanowire system by combining the time evolution of the electron wave function and machine learning is proposed. We have shown that the intermediate state of the time evolution calculation is a great advantage for efficient modeling by machine learning. The features for machine learning are extracted by analyzing the time variation of the electron density distribution using time evolution calculations. Consequently, the prediction error of the model is improved by performing machine learning based on the features. The proposed method provides a useful perspective for analyzing the motion of electrons in nanoscale semiconductors.

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Section: Introductionmentioning

confidence: 99%