A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga₂O₃ Systems.

Area of Science:

• Materials Science
• Computational Materials Science
• Semiconductor Physics

Background:

• Intrinsic defects significantly impact semiconductor properties, especially in ultrawide bandgap materials like β-Ga₂O₃.
• Complex defect configurations in β-Ga₂O₃ hinder accurate characterization via traditional computational methods.

Purpose of the Study:

• To develop an accurate computational method for identifying intrinsic point defects in β-Ga₂O₃.
• To enable large-scale atomistic modeling and real-time defect detection in β-Ga₂O₃.

Main Methods:

• Integration of particle swarm optimization (PSO) and K-means clustering (K-MC) algorithms.
• Development of a novel algorithm for explicit identification of intrinsic point defects.
• Application of the algorithm to dynamic simulations for real-time detection feasibility.

Main Results:

• The developed algorithm achieves a recognition accuracy exceeding 95% for intrinsic point defects in β-Ga₂O₃.
• Demonstrated feasibility of dynamic real-time detection of defects through simulations.

Conclusions:

• The PSO-K-MC algorithm provides an effective solution for accurate defect identification in β-Ga₂O₃.
• This advancement facilitates more reliable large-scale atomistic simulations and real-time defect monitoring in semiconductor research.