Related Experiment Video
Updated: Jul 24, 2025

09:49
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
Published on: May 13, 2020
4.1K
Nanoscale multistate resistive switching in WO3 through scanning probe induced proton evolution
Fan Zhang1,2, Yang Zhang1, Linglong Li1
1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.
Nature Communications
|July 4, 2023
Summary
This study introduces a new method for creating multistate resistive switching devices using scanning probes to control proton evolution in tungsten oxide (WO3). This technique enables nanoscale control over conductivity for neuromorphic computing applications.
Area of Science:
- Materials Science
- Nanotechnology
- Electronics
Background:
- Multistate resistive switching devices are crucial for energy-efficient neuromorphic computing.
- Scaling challenges exist for electric-field induced phase transitions in these devices.
Purpose of the Study:
- To demonstrate nanoscale control of resistive switching using scanning-probe-induced proton evolution in WO3.
- To overcome device scaling limitations in neuromorphic computing hardware.
Main Methods:
- Utilized a Pt-coated scanning probe as a hydrogen catalysis probe for controlled proton evolution in WO3.
- Employed voltage biasing to drive proton insertion and extraction, manipulating hydrogenation and conductivity.
- Visualized nanoscale conductivity manipulation through a printed portrait encoded by local conductivity.
Main Results:
- Achieved reversible insulator-to-metal transition (IMT) at the nanoscale.
- Demonstrated precise control over local conductivity via scanning probe manipulation.
- Successfully realized multistate resistive switching through successive set and reset operations.
Conclusions:
- Scanning-probe-induced hydrogen evolution offers a novel pathway for engineering memristors at the nanoscale.
- This approach provides a scalable solution for developing advanced neuromorphic computing devices.

