1. Morphology control of volatile resistive switching in La0.67Sr0.33MnO3 thin films on LaAlO3 (001)
- Author
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A. Jaman, A. S. Goossens, J. J. L. van Rijn, L. van der Zee, and T. Banerjee
- Subjects
volatile memristor ,resistive switching ,structural twin boundary (TB) ,neural network ,metal to insulator transition (MIT) ,correlated oxides ,Chemical technology ,TP1-1185 - Abstract
The development of in-memory computing hardware components based on different types of resistive materials is an active research area. These materials usually exhibit analog memory states originating from a wide range of physical mechanisms and offer rich prospects for their integration in artificial neural networks. The resistive states are classified as either non-volatile or volatile, and switching occurs when the material properties are triggered by an external stimulus such as temperature, current, voltage, or electric field. The non-volatile resistance state change is typically achieved by the switching layer’s local redox reaction that involves both electronic and ionic movement. In contrast, a volatile change in the resistance state arises due to the transition of the switching layer from an insulator to a metal. Here, we demonstrate volatile resistive switching in twinned LaAlO3 onto which strained thin films of La0.67Sr0.33MnO3 (LSMO) are deposited. An electric current induces phase transition that triggers resistive switching, close to the competing phase transition temperature in LSMO, enabled by the strong correlation between the electronic and magnetic ground states, intrinsic to such materials. This phase transition, characterized by an abrupt resistance change, is typical of a metallic to insulating behavior, due to Joule heating, and manifested as a sharp increase in the voltage with accompanying hysteresis. Our results show that such Joule heating-induced hysteretic resistive switching exhibits different profiles that depend on the substrate texture along the current path, providing an interesting direction toward new multifunctional in-memory computing devices.
- Published
- 2023
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