AI 中文总结
针对β-Ga2O3离子注入与退火的点缺陷演化，采用机器学习引导的MD模拟，开发了缺陷识别算法，揭示了电子停止效应、最优退火温度及缺陷诱导的相变规律。
AI 中文摘要
在β-氧化镓（beta-Ga2O3）中，镓离子注入与退火会诱发大量点缺陷。为克服传统维格纳-赛茨（WS）缺陷分析的局限性，本文开发了一种基于相似度匹配与DBSCAN聚类的β-Ga2O3缺陷识别算法，该算法可区分高浓度下的晶格原子与缺陷，并识别出8种镓间隙构型（Gai a至Gai h）。对比SRIM与分子动力学（MD）数据凸显了电子停止效应：忽略该效应会高估离子射程与缺陷浓度。在5种注量（1×10^14至5×10^14 cm-2）下，1373 K是最优恢复温度。通过流体静应力、偏径向分布函数（PRDF）与缺陷浓度的多尺度分析揭示了缺陷演化规律：镓间隙（Gai）占据四面体与八面体位置，驱动缺陷介导的β相向γ-Ga2O3的相变；注量升高会降低β相恢复程度并加剧γ相转变的不可逆性；氧间隙（Oi）的迁移对退火温度敏感，可促进氧亚晶格的再结晶。
英文摘要
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.
CommentsAccepted for publication in Acta Materialia. Published version: Acta Materialia 318 (2026) 122596, https://doi.org/10.1016/j.actamat.2026.122596
Journal refActa Materialia 318 (2026) 122596
DOI：10.1016/j.actamat.2026.122596
