Dr. Amran Mahfudh Yatmeidhy is a postdoctoral researcher at the Quantum Materials Simulation Group, MANA, NIMS, which he joined in February 2024. Previously, he worked as a postdoctoral researcher in the Department of Materials Science and Engineering at Tokyo Institute of Technology (now Institute of Science Tokyo), where he earned his Ph.D. in September 2023 under the guidance of Prof. Yoshihiro Gohda. His research interests include the study of magnetic anisotropy in magnetic materials and the analysis of defects in semiconductors. His recent work adopts machine-learning algorithms to characterize structural defects in semiconductors.

Gallium nitride (GaN) has emerged as a leading candidate to succeed silicon-based power technologies; however, achieving the desired p-type conductivity level remains a fundamental challenge. Although ion implantation is the most widely adopted approach for magnesium (Mg) doping in GaN, the process inevitably introduces defects with complex structural configurations that resist experimental characterization. An unsupervised approach combining locally averaged atomic fingerprints (LAAF) with the two-step locality preserving projection (TS-LPP) has demonstrated success in distinguishing local structures across various systems [1,2], thereby offering a rigorous framework for atomic-scale defect characterization in GaN.

Here, we apply the LAAF+TS-LPP methodology to investigate Mg-implantation-induced defects in GaN using the structural data from first-principles molecular dynamics simulations, combined with minimum covariance determinant (MCD) outlier detection method [3]. Figure 1(left) presents the first two TS-LPP components for Mg-implanted GaN (GaN:Mg), where each datapoint represents a Ga atom colored by robust distance dRB from the bulk reference distribution. The defect-related datapoints scatter broadly from the bulk cluster at the origin (shown in gray) and form several distinct local clusters, with corresponding Ga atoms visualized in real space in Fig. 1(right). Using this framework, we systematically characterize the Mg-implantation-induced defects through comparison with various point defect structures in GaN, with further details to be discussed in the presentation.

1. R. Tamura, et.al, Phys. Rev. B 105, 075107 (2022). DOI: 10.1103/PhysRevB.105.075107
2. A.K.A. Lu, et.al., Phys. Chem. Chem. Phys. 26, 11657-11666 (2024). DOI: 10.1039/d3cp06298h
3. A.M. Goryaeva, et.al., Nat. Commun. 11, 1 (2020). DOI: 10.1038/s41467-020-18282-2