arXiv subjects

Zongwei Xu

Publications and source records attributed to Zongwei Xu.

3 recordsLinked to original sources

arXiv2026

Machine-learning-guided molecular dynamics simulations of point defect evolution in beta-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.

cond-mat.mtrl-sci↗

arXiv2024

Crater-shaped Enrichment of $\mathrm{V}_\mathrm{Si}$ Color Centers in $4H$-SiC using Single-Pulse Near-Infrared Femtosecond Laser Processing

Currently, Si vacancy ($\mathrm{V}_\mathrm{Si}$) color centers in SiC are of significant interest due to their potential applications in quantum sensing and quantum communication. Meanwhile, the qualities of laser-induced color centers are well guaranteed. Femtosecond laser processing suffices for increasing the yield of $\mathrm{V}_\mathrm{Si}$ color centers in bulk materials and forms crater-shaped enriched regions on the surface. However, there is a notable absence of existing simulation methods to explain the mechanisms behind laser-assisted $\mathrm{V}_\mathrm{Si}$ color center generation. In this work, we design a three-dimensional molecular dynamics (3D-MD) model using an integral hemi-ellipsoidal shell mathematical model to simulate the interaction of Gaussian laser beams with bulk materials. Furthermore, we calculate the transmittance, absorption coefficient, refractive index, and reflectivity of $4H$-SiC. Then, the absorptance of a 1030 nm laser in 350 μm-thick $4H$-SiC material is abtained to simulate the energy loss during the actual processing. Finally, the study analyzes the movement trajectories of $\mathrm{V}_\mathrm{Si}$ color centers and explains the source of $\mathrm{V}_\mathrm{Si}$ on the surface. This analysis explains the reasons for the enrichment of color centers in the crater-shaped regions formed after laser deposition. Our work provides an effective 3D-MD modeling approach to study the processing mechanisms of laser interaction with semiconductor materials, offering insights into efficient $\mathrm{V}_\mathrm{Si}$ color center creation processes.

physics.optics↗

arXiv2024

Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale β-$\rm {Ga_2O_3}$

The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of $β$-$\rm {Ga_2O_3}$, a vital ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. In this work, we present an effective algorithm designed explicitly for identifying various intrinsic point defects in the $β$-$\rm {Ga_2O_3}$ lattice. By integrating particle swarm optimization and hierarchical clustering methods, our algorithm attains a recognition accuracy exceeding 95% for discrete point defect configurations. Furthermore, we have developed an efficient technique for randomly generating diverse intrinsic defects in large-scale $β$-$\rm {Ga_2O_3}$ systems. This approach facilitates the construction of an extensive atomic database, crucially instrumental in validating the recognition algorithm through a substantial number of statistical analyses. Finally, the recognition algorithm is applied to a molecular dynamics simulation, accurately describing the evolution of the point defects during high-temperature annealing. Our work provides a useful tool for investigating the complex dynamical evolution of intrinsic point defects in $β$-$\rm {Ga_2O_3}$, and moreover, holds promise for understanding similar material systems, such as $\rm {Al_2O_3}$, $\rm {In_2O_3}$, and $\rm {Sb_2O_3}$.

cond-mat.mtrl-sci↗