Understanding effect of distortions and vacancies in wurtzite AlScN ferroelectric memory materials: Vacancy-induced multiple defect state types and relaxation dependence in transition energy levels

Abstract

Energy-efficient compact alternatives to fully digital computing strategies could be achieved by implementations of artificial neural networks (ANNs) that borrow analog techniques. In-memory computing based on crossbar device architectures with memristive materials systems that execute, in an analog way, multiply-and-accumulate operations prevalent in ANN is a notable example. Ferroelectric (FE) materials are promising candidates for achieving ANN thanks to their excellent down-scalability, improved electrical control, and high energy efficiency. However, it remains challenging to develop a crossbar device architecture using FE materials. The difficulty stems from decreasing the leakage current of FE hardware and, simultaneously, reducing the film thickness for achieving compact systems. Here, we have performed density-functional-theory calculations to investigate the electronic, energy-based, and structural signatures of wurtzite FE material Al0.75Sc0.25N with a nitrogen vacancy (VN) in different charge states. We find that VN can introduce two defect states, viz., the singlet state above the valence band maximum (VBM) and a triplet state below the conduction band minimum in wurtzite AlScN models. The calculations reveal that the group of transition levels E3+/2+/E2+/1+ with small formation energies occur at ∼0.78/1.03 eV above the VBM in the wurtzite AlScN with a relaxed configuration, which may shift by a large degree to lower energy levels if atoms surrounding the defect are not fully relaxed. Theoretical studies elucidate the vacancy-enhanced increase in the leakage current utilizing large AlScN supercells. These findings render atomistic insights that can provide a path forward for the design of next-generation portable low-power electronic systems.