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High-Performance 2D Ambipolar MoTe2 Lateral Memristors by Mild Oxidation
Bochen Zhao1, Longlong Xu1, Ruixuan Peng1
1State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China.
Small (Weinheim an Der Bergstrasse, Germany)
|July 3, 2024
Summary
Mild oxidation enhances memristive performance in 2D transition metal dichalcogenides (TMDCs). This strategy boosts resistive switching ratios and enables advanced artificial synapse applications in brain-inspired computing.
Area of Science:
- Materials Science
- Nanotechnology
- Electronics
Background:
- 2D transition metal dichalcogenides (TMDCs) are key materials for memristors in brain-inspired computing.
- Oxidation typically degrades TMDCs, but its effect on ambipolar TMDCs' resistive switching is not fully understood.
Purpose of the Study:
- To investigate the impact of mild oxidation on the resistive switching behavior of 2D ambipolar TMDCs.
- To develop a strategy for enhancing memristive performance using controlled oxidation.
Main Methods:
- A mild oxidation strategy was applied to 2H-molybdenum ditelluride (MoTe2) lateral memristors.
- Resistive switching characteristics, doping effects, and gate voltage distribution were analyzed.
- Device performance was evaluated for artificial synapse applications, including long-term potentiation/depression and handwritten digit recognition.
Main Results:
- Mild oxidation increased the resistive switching ratio of 2H-MoTe2 memristors by over 10 times.
- Enhanced doping from O2 and H2O adsorption and optimized gate voltage distribution contributed to improved performance.
- The ambipolarity of 2H-MoTe2 allowed for a reversible change in resistive switching direction.
Conclusions:
- Controlled oxidation is a viable method to significantly enhance the memristive properties of 2D ambipolar materials.
- The study provides insights into the mechanisms underlying resistive switching in oxidized 2D materials.
- The developed MoTe2 memristors show promise as artificial synapses for neuromorphic computing applications.

