Ion Implantation and Annealing

Machine-learning-guided molecular dynamics simulations of point defect evolution in β-Ga2O3 during ion implantation and annealing

In beta-gallium oxide (beta-Ga2O3), Ga-ion implantation and annealing induce abundant point defects. To overcome conventional Wigner-Seitz (WS) defect analysis limitations, a defect identification algorithm based on similarity matching and DBSCAN clustering is developed for beta-Ga2O3. It distinguishes lattice atoms from defects at high concentrations and identifies eight Ga interstitial configurations (Gaia to Gaih). Comparing SRIM and MD data highlights electronic stopping effects: neglecting them overestimates ion range and defect concentration. Across five fluences (1 to 5 x 10^14 cm-2), 1373 K is the optimal recovery temperature. Multiscale analyses using hydrostatic stress, PRDF, and defect concentration reveal defect evolution. Ga interstitials (Gai) occupy tetrahedral and octahedral sites, driving a defect-mediated phase transition from beta- to gamma-Ga2O3. Increasing fluences reduce beta-phase recovery and increase gamma-phase transformation irreversibly. Oxygen interstitial (Oi) migration is sensitive to annealing temperature, which enhances O-sublattice recrystallization.