Deep-Learning-Accelerated Dopant Selection for High-k HfO2 Dielectrics: A Disorder-Resolved Study of Y, Si and Al

Abstract

Hafnium oxide (HfO2) is the cornerstone high-k dielectric in modern silicon technology. Since the constraints of silicon device fabrication rule out replacing the material itself, dopant incorporation is the principal means available to engineer its band gap and dielectric constant within existing process flows. However, dopant selection is still largely empirical due to the coupled interplay among thermodynamic stability, electronic insulation, and dielectric response. Here, we present a high-throughput computational framework integrating special quasi-random structures (SQS), machine-learning potentials (SevenNet), and graph neural networks (ALIGNN) to systematically evaluate doped-HfO2 compositions across three dopants (Al, Si, Y) and two technologically relevant polymorphs (monoclinic and orthorhombic). Our analysis uncovers a fundamental design principle: formation energy, band gap, and dielectric constant are decoupled parameters requiring application-specific prioritization rather than simultaneous optimization. Yttrium achieves the lowest formation energy (-3.763 eV/atom) and favors orthorhombic phase stabilization at process-compatible thermal budgets; silicon preserves near-pristine band gaps (around 5.72 eV) critical for suppressing leakage in gate dielectric applications; and aluminum enables concentration-tunable band gap widening (5.6-5.9 eV) suited for voltage scaling. Validation against experimental literature and density functional theory (DFT) confirms quantitative accuracy (0.02 eV band gap error for Si-doping, mean absolute error less than 0.001 eV/atom formation energy). This framework provides rational, property-decoupled guidance for dopant engineering in HfO2-based dielectrics and related high-k oxide systems.