Journal of Materials Science

, Volume 47, Issue 21, pp 7498–7514 | Cite as

First principles calculations of oxygen vacancy-ordering effects in resistance change memory materials incorporating binary transition metal oxides

  • Blanka Magyari-Köpe
  • Seong Geon Park
  • Hyung-Dong Lee
  • Yoshio Nishi
First Principles Computations


Resistance change random access memories based on transition metal oxides had been recently proposed as promising candidates for the next generation of memory devices, due to their simplicity in composition and scaling capability. The resistance change phenomena had been observed in various materials, however the fundamental understanding of the switching mechanism and of its physical origin has not been agreed upon. We have employed first principles simulations based on density functional theory to elucidate the effect of oxygen vacancies on the electronic structure of rutile TiO2 and NiO using the local density and generalized gradient approximations with correction of on-site Coulomb interactions (LDA + U for TiO2 and GGA + U for NiO). We find that an ordered oxygen vacancy filament induces several defect states within the band gap of both materials, and can lead to the defect-assisted electron transport. This state may account for the “ON”-state low resistance conduction observed experimentally in rutile TiO2 and NiO. As the filament structure is perturbed by oxygen ions moving into the ordered chain of vacancies under applied electric field, charges are trapped and the conductivity can be significantly reduced. We predict this partially disordered arrangement of vacancies may correspond to the “OFF”-state of the resistance change memories.


TiO2 Oxygen Vacancy Defect State Resistive Switching Valence Band Maximum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The Stanford Non-Volatile Memory Technology Research Initiative (NMTRI), and the Marco Focus Center (MSD) sponsored this study. The computational study was carried out using the National Nanotechnology Infrastructure Network’s Computational Cluster at Stanford.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Blanka Magyari-Köpe
    • 1
  • Seong Geon Park
    • 2
  • Hyung-Dong Lee
    • 1
  • Yoshio Nishi
    • 1
  1. 1.Department of Electrical EngineeringStanford UniversityStanfordUSA
  2. 2.Department of Materials Science and EngineeringStanford UniversityStanfordUSA

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