In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device
Abstract Resistive switching (RS) devices with ultra‐low‐voltage threshold and reliable switching repeatability exhibits great potential applications in energy‐efficient data storage and neuromorphic computing. Understanding switching mechanisms at nanoscale is critical to design RS devices with improved performance. In this work, a lamella memristive device using focused ion beam (FIB) method based on the metal/TiO x /TiN/Si structure device is fabricated. In situ transmission electron microscopy (TEM) and current–voltage ( I–V ) characteristic demonstrate that the lamella device shows a volatile RS behavior with a threshold switching at ≈ ± 0.4 V. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charge carriers such as oxygen vacancies migrate under positive/negative DC bias and modulate Schottky barriers at the top and bottom metal/semiconductor interfaces. The RS mechanism of the lamella device is based on the Schottky barriers modulation and Joule heating assisted electric field triggered thermal runaway (FTTR) occurred at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a perspective on interface‐type RS devices processing and opens up new technological opportunities of fabricating ultra‐low‐energy memristive devices.
Authors
- Rodney J. McCabe (ORCID: https://orcid.org/0000-0002-6684-7410)
- Sundar Kunwar (ORCID: https://orcid.org/0000-0001-6289-6613)
- Nicholas Cucciniello (ORCID: https://orcid.org/0000-0002-9399-4985)
- Pinku Roy (ORCID: https://orcid.org/0000-0002-3504-9571)
- Hongyi Dou (ORCID: https://orcid.org/0000-0003-3496-7933)
- Ye Cao (ORCID: https://orcid.org/0000-0002-7365-7447)
- Henry D. Tran
- Aiping Chen (ORCID: https://orcid.org/0000-0003-2639-2797)
- Winson Kuo (ORCID: https://orcid.org/0000-0003-2821-7909)
- Rohan Dhall
- Michael T. Pettes (ORCID: https://orcid.org/0000-0001-6862-6841)
- Jim Ciston (ORCID: https://orcid.org/0000-0002-8774-5747)
- Matthew M. Schneider (ORCID: https://orcid.org/0000-0002-7425-7736)
- John Watt (ORCID: https://orcid.org/0000-0002-8012-9837)
- Natanii R. Yazzie
- Haiyan Wang (ORCID: https://orcid.org/0000-0002-7397-1209)
- Di Zhang (ORCID: https://orcid.org/0000-0002-9199-5634)
- Cun Li (ORCID: https://orcid.org/0000-0001-9076-4802)
- Chengyu Song (ORCID: https://orcid.org/0000-0003-2419-8675)
- Daniel Appuing
Institutions
- Los Alamos National Laboratory (US)
- Lawrence Berkeley National Laboratory (US)
- The University of Texas at Arlington (US)
- Purdue University West Lafayette (US)
- Center for Integrated Nanotechnologies (US)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1002/adfm.202517173
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Science Foundation
- U.S. Department of Energy
- Purdue University
- University of Texas at Arlington
- Center for Integrated Nanotechnologies
- Office of Science
- National Nuclear Security Administration
- Division of Materials Research
- Basic Energy Sciences
- Los Alamos National Laboratory