LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

An Atomistic Model of Field-Induced Resistive Switching in Valence Change Memory.

Photo from wikipedia

In valence change memory (VCM) cells, the conductance of an insulating switching layer is reversibly modulated by creating and redistributing point defects under an external field. Accurately simulating the switching… Click to show full abstract

In valence change memory (VCM) cells, the conductance of an insulating switching layer is reversibly modulated by creating and redistributing point defects under an external field. Accurately simulating the switching dynamics of these devices can be difficult due to their typically disordered atomic structures and inhomogeneous arrangements of defects. To address this, we introduce an atomistic framework for modeling VCM cells. It combines a stochastic kinetic Monte Carlo approach for atomic rearrangement with a quantum transport scheme, both parametrized at the ab initio level by using inputs from density functional theory. Each of these steps operates directly on the underlying atomic structure. The model thus directly relates the energy landscape and electronic structure of the device to its switching characteristics. We apply this model to simulate field-induced nonvolatile switching between high- and low-resistance states in a TiN/HfO2/Ti/TiN stack and analyze both the kinetics and stochasticity of the conductance transitions. We also resolve the atomic nature of current flow resulting from the valence change mechanism, finding that conductive paths are formed between the undercoordinated Hf atoms neighboring oxygen vacancies. The model developed here can be applied to different material systems to evaluate their resistive switching potential, both for use as conventional memory cells and as neuromorphic computing primitives.

Keywords: change; change memory; valence change; field induced

Journal Title: ACS nano
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.