Optimization of the multi-mem response of topotactic redox La1/2Sr1/2Mn1/2Co1/2O3−x

• Published in: APL materials Vol. 10; no. 1; pp. 011111 - 011111-14
• Main Authors: Román Acevedo, W., Aguirre, M. H., Ferreyra, C., Sánchez, M. J., Rengifo, M., van den Bosch, C. A. M., Aguadero, A., Noheda, B., Rubi, D.
• Format: Journal Article
• Language: English
• Published: AIP Publishing LLC 01.01.2022
• ISSN: 2166-532X; 2166-532X
• DOI: 10.1063/5.0073490

Memristive systems emerge as strong candidates for the implementation of resistive random access memories and neuromorphic computing devices, as they can mimic the electrical analog behavior or biological synapses. In addition, complementary functionalities, such as memcapacitance, could significantly improve the performance of bio-inspired devices in key issues, such as energy consumption. However, the physics of mem systems is not fully understood so far, hampering their large-scale implementation in devices. Perovskites that undergo topotactic transitions and redox reactions show improved performance as mem systems, compared to standard perovskites. In this paper, we analyze different strategies to optimize the multi-mem behavior (memristive and memcapacitive) of topotactic redox La1/2Sr1/2Mn1/2Co1/2O3−x (LSMCO) films grown on Nb:SrTiO3. We explored devices with different crystallinities (from amorphous to epitaxial LSMCO), out-of-plane orientation [(001) and (110)], and stimulated either with voltage or current pulses. We found that an optimum memory response is found for epitaxial (110) LSMCO stimulated with current pulses. Under these conditions, the system efficiently exchanges oxygen with the environment minimizing, at the same time, self-heating effects that trigger nanostructural and chemical changes that could affect the device integrity and performance. Our work contributes to pave the way for the integration of multi-mem topotactic redox oxide-based interfaces in multiple device architectures, in order to exploit their memristive and memcapacitive properties for data storage or neuromorphic computation.