journal article·2025-05

Examining composition-dependent radiation response in AlGaN alloys

Miaomiao Jin, Farshid Reza, Alexander Hauck, Mahjabin Mahfuz, Xing Wang, Rongming Chu, Blair Tuttle

Acta Materialia· Volume 289
Publisher page

Abstract

AlGaN alloys are widely used in GaN electronic devices, which are receiving significant interest for deployment in radiation environments. In this work, we aim to investigate the atomistic mechanisms of radiation-induced damage in AlGaN alloys, providing insights that bridge the gap between high-length-scale experimental data and atomic-level processes. Through extensive molecular dynamics simulations, we reveal the compositional dependence of radiation-induced defect production and evolution in Al$_x$Ga$1-x$N systems with $x$ ranging from 0 to 1. The damage accumulation characteristics observed in our simulations align notably well with available experimental data at temperatures up to room temperature. Our findings indicate that alloy composition significantly influences defect production and microstructural evolution, including the formation of dislocation loops and defect clusters. Specifically, with increasing Al content, defect production from individual recoil events is reduced; however, extended interstitial defects are more likely to form considering cumulative effects, leading to enhanced damage at high doses. Among the compositions studied, we find that 25\% Al content leads to the least overall radiation damage, suggesting an optimal alloying strategy for mitigating radiation effects. These findings underscore the interplay between defect formation, dynamic annealing, and damage cascade effects, offering insights for optimizing AlGaN materials for radiation resistance in practical applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: GaN-based semiconductor devices and materials · Ga2O3 and related materials · Thermal properties of materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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