Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing
View abstract on PubMed
Summary
This summary is machine-generated.This study presents dual resistive switching modes in VO2/LSMO memristive devices, enabling flexible emulation of brain functions. These devices offer a path towards low-power, high-density neuromorphic computing and avoid sneak-path currents.
Area Of Science
- Materials Science
- Neuroscience
- Electrical Engineering
Background
- Memristive devices are crucial for emulating brain functions like neurons and synapses.
- Integrating nonvolatile and volatile switching modes in a single device simplifies neuromorphic architectures.
- Transistor-based selectors in memristive networks increase power consumption and reduce integration density.
Purpose Of The Study
- To report dual resistive switching (RS) modes in VO2/LSMO bilayer memristive devices.
- To demonstrate the potential for creating low-power, high-density memristive spiking neural networks.
- To explore the application of these devices in neuromorphic computing and memristive crossbar arrays.
Main Methods
- Fabrication of VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices.
- Electrical characterization to identify and differentiate nonvolatile and volatile resistive switching mechanisms.
- Analysis of the role of oxygen vacancies and the metal-insulator transition (MIT) in controlling RS modes.
Main Results
- Demonstrated coexistence of nonvolatile (oxygen vacancy-driven) and volatile (VO2 MIT-driven) RS modes in a single device.
- Achieved electrical switching between the two RS modes, enabling 1 selector-1 resistor (1S1R) cell functionality.
- Confirmed stability and repeatability of both RS modes, reconfigurable via interfacial and phase transition properties.
Conclusions
- The VO2/LSMO bilayer memristive devices exhibit dual RS modes suitable for advanced neuromorphic applications.
- The 1S1R cell capability addresses sneak-path current issues in memristive crossbar arrays.
- These findings pave the way for efficient, high-density memristive neural networks and neuromorphic computing systems.
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