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Published on: May 13, 2020
Area-Dependent Resistive Switching and Interfacial Dynamics in GCMO-Based Memristors
Anni Antola1, Johanna Laaksonen2, Hannu Huhtinen1
1Wihuri Physical Laboratory, Department of Physics and Astronomy, University of Turku, FI-20014 Turku, Finland.
Gd0.2Ca0.8MnO3 (GCMO) memristors show area-dependent resistive switching, crucial for neuromorphic computing. Their tunable resistance states and interface-type switching make them ideal for artificial neural networks.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Nanotechnology
Background:
- Memristors are key components for neuromorphic computing, mimicking biological synapses.
- Resistive switching (RS) in materials like Gd0.2Ca0.8MnO3 (GCMO) is essential for memristor functionality.
- Understanding area-dependent RS characteristics is vital for device scalability and reliability.
Purpose of the Study:
- To investigate the area-dependent resistive switching (RS) properties of GCMO-based memristors with Al and Au electrodes.
- To explore the potential of GCMO memristors for neuromorphic computing applications.
- To elucidate the role of the interfacial layer in the RS mechanism.
Main Methods:
- Fabrication of GCMO memristors with varying device areas and Al/Au electrodes.
- Electrical characterization including resistance measurements in high-resistance (HRS) and low-resistance (LRS) states.
- X-ray photoelectron spectroscopy (XPS) for interfacial layer analysis and depth profiling.
Main Results:
- Demonstrated predictable area-scaling of HRS (107–108 Ω) and LRS (105–107 Ω) resistances, supporting interface-type RS.
- XPS revealed compositional differences in the AlOx interfacial layer between HRS and LRS, with HRS showing higher oxide content and wider interface.
- Achieved multistate resistive switching with up to ten distinct levels by voltage modulation.
Conclusions:
- GCMO exhibits promising area-dependent resistive switching characteristics suitable for scalable memristor applications.
- The interfacial layer plays a critical role in the resistive switching mechanism of GCMO memristors.
- GCMO is a viable material for synaptic weight storage in energy-efficient, scalable artificial neural networks.
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