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Machine-learning-guided molecular dynamics simulations of point defect evolution in β‑Ga2O3 during ion implantation and annealing
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DOI：10.1016/j.actamat.2026.122596.png)
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Abstract
En 中文
In beta-gallium oxide (β-Ga2O3), Ga-ion implantation and subsequent annealing induce abundant point defects, including interstitials and vacancies. To overcome the limitations of conventional Wigner-Seitz (WS) defect analysis, a defect identification algorithm based on similarity matching and spatial clustering via density-based spatial clustering of applications with noise (DBSCAN) is developed specifically for β-Ga2O3. This algorithm accurately distinguishes lattice atoms from point defects under thermal perturbations and at high defect concentrations, and further identifies eight configurations of Ga interstitials (Gaia to Gaih) by analyzing atomic coordination environments. Simulations comparing Stopping and Range of Ions in Matter (SRIM) and molecular dynamics (MD) data, along with single-ion implantation analysis, highlight the significance of electronic stopping effects: neglecting electronic stopping leads to overestimated ion range, defect concentration, and temperature rise. Across five implantation fluences (1 to 5 × 1014 cm−2) and corresponding annealing processes, 1373 K is identified as the optimal recovery temperature. Multiscale analyses based on hydrostatic stress, partial radial distribution function (PRDF), and defect concentration reveal the evolution and spatial distribution of point defects. The results demonstrate that Ga interstitials (Gai) tend to occupy tetrahedral and octahedral interstitial sites, and the accumulation and recombination of point defects drive a defect-mediated phase transition from β- to γ-Ga2O3. With increasing fluences followed by annealing, β-phase recovery declines while γ-phase transformation rises, leading to an irreversible phase transition. In contrast, the migration of oxygen interstitials (Oi) is more sensitive to annealing temperature, and appropriate annealing temperature significantly enhances recrystallization of the O-sublattice.
Keywords:
β-Ga2O3
Ion implantation
Point defects
Defect identification algorithm
Annealing
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9.3
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2.0W
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12.9W
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