首页|Atomic structures and stability of finite-size extended interstitial defects in silicon: Large-scale molecular simulations with a neural-network potential
Atomic structures and stability of finite-size extended interstitial defects in silicon: Large-scale molecular simulations with a neural-network potential
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NSTL
Elsevier
The energetics of various extended interstitial (I) defects in crystalline Si is examined by constructing an artificial-neural-network (ANN) potential trained with density-functional-theory (DFT) data, enabling us to perform accurate large-scale simulations and to obtain well-converged formation energies (E-f). By varying the number of interstitials n to around 1,000, E-f & nbsp;is calculated for the compact cluster, I-12-like, (001)-plane, (311)-rod-like and Frank-loop defects. For n <=& nbsp;36, the compact cluster or (311)-rod-like defect is found to be most stable, depending on n. This trend strongly depends on simulation cell sizes, suggesting the importance of sufficiently large cells. For 36 < n ?& nbsp;860, the (311)-rod-like defect is most stable whereas the Frank-loop defect becomes most stable for larger n. The ANN potential is demonstrated to outperform empirical potentials in prediction of E-f and defect structures. Furthermore, ANN values of E-f are fitted to analytic functions with the aim of refining macroscopic simulations for device manufacturing processes.
NSTL
Elsevier
