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Creators/Authors contains: "Ganesh, Panchapakesan"

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  1. Free, publicly-accessible full text available July 27, 2023
  2. Abstract

    We developed a physical model to fundamentally understand the conductive filament (CF) formation and growth behavior in the switching layer during electroforming process in the metal-oxide-based resistive random-access memories (RRAM). The effects of the electrode and oxide layer properties on the CF morphology evolution, current-voltage characteristic, local temperature, and electrical potential distribution have been systematically explored. It is found that choosing active electrodes with lower oxygen vacancy formation energy and oxides with small Lorenz number (ratio of thermal and electrical conductivity) enables CF formation at a smaller electroforming voltage and creates a CF with more homogeneous morphology. This work advances our understanding of the kinetic behaviors of the CF formation and growth during the electroforming process and could potentially guide the oxide and electrode materials selection to realize a more stable and functional RRAM.

  3. Ferroelectric materials such as barium titanate (BaTiO3) have a wide range of applications in nano scale electronic devices due to their outstanding properties. In this study, we developed an easily extendable atomistic ReaxFF reactive force field for BaTiO3 that can capture both its field- as well as temperature-induced ferroelectric hysteresis and corresponding changes due to surface chemistry and bulk defects. Using our force field, we were able to reproduce and explain a number of experimental observations: (1) existence of a critical thickness of 4.8 nm below which ferroelectricity vanishes in BaTiO3; (2) migration and clustering of oxygen vacancies (OVs) in BaTiO3 and reduction in the polarization and the curie temperature due to the OVs; (3) domain wall interaction with surface chemistry to influence ferroelectric switching and polarization magnitude. This new computational tool opens up a wide range of possibilities for making predictions for realistic ferroelectric interfaces in energy-conversion, electronic and neuromorphic systems.