Utilizing Native Defect Engineering and Doping in BaTiO <sub>3</sub> to Foster the Development of Memristive Devices Through First-Principles Calculations
BaTiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (BTO) is a promising candidate for multi-functional memristive devices, exhibiting outstanding ferroelectric behaviour and a high resistance contrast. The modulation of resistive switching in BTO is significantly influenced by doping and native-defect incorporation. In order to comprehend the effect of orbital engineering on the electronic properties and transport mechanisms of the BTO system for memristive applications, systematic density functional theory (DFT) investigations have been conducted on a variety of potential vacancies and doping on the A- and B-sites of BTO. In the case of vacancy-induced BTO, the electronic bandgap is evidently caused by an improvement in the overlapping of the O-2p and the Ti-3d orbitals, indicating these two orbitals are re-hybridizing. The establishment of conductive filaments and the associated charge conduction can be accurately predicted by analysing the formation energy, charge density, orbital splitting energy, and partial density-of-states. The results of this study demonstrate that a theoretical approach can be employed to investigate the defect states of systems such as transition metal oxides-based complex perovskites for memristive applications, especially utilizing the orbital degree of freedom.
Authors
- M. Julie Therese (ORCID: https://orcid.org/0000-0003-1404-5392)
- Shashi Pandey (ORCID: https://orcid.org/0000-0003-0755-5044)
- Tejendra Dixit (ORCID: https://orcid.org/0000-0002-7182-3907)
- Vikash Mishra (ORCID: https://orcid.org/0000-0002-2089-8415)
Institutions
- Indian Institute of Technology Bombay (IN)
- Manipal Academy of Higher Education (IN)
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram (IN)
Publication Details
- Journal
- IEEE Transactions on Materials for Electron Devices
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tmat.2025.3610406
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73