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Manipulating the Resistive Switching in Epitaxial SrCoO<sub>2.5</sub> Thin-Film-Based Memristors by Strain Engineering

Xuepeng Xiang, Jingjing Rao, Chaesung Lim, Qicheng Huang, Shijun Zhao, Zhen Fan, Jeong Woo Han, Yan Chen

2022ACS Applied Electronic Materials18 citationsDOI

Abstract

Diverse synaptic and neuronal functionalities often require different switching dynamics for memristors. Here, we demonstrate an effective approach to modulating the switching dynamics in epitaxial thin-film-based memristors by using strain engineering. We apply 0 and −2.74% compressive strains to the brownmillerite SrCoO2.5 (BM-SCO) thin films by epitaxially growing them on (001)-oriented SrTiO3 (STO) and LaAlO3 (LAO) substrates, with Au and La0.7Sr0.3MnO3 (LSM) acting as top and bottom electrodes, respectively. When applying multiple DC voltage sweeps, the Au/BM-SCO/LSM/LAO memristor with compressive strain displays small yet narrowly distributed ON/OFF ratios (averaged at ∼3.5 with a cycle-to-cycle fluctuation of ∼17%), while the Au/BM-SCO/LSM/STO memristor with zero strain exhibits larger ON/OFF ratios distributed in a wider range (averaged at ∼15.5 with a cycle-to-cycle fluctuation of ∼33%). In the retention tests, the Au/BM-SCO/LSM/LAO memristor exhibits relatively stable high and low resistance states (HRSs and LRSs, respectively). By contrast, for the Au/BM-SCO/LSM/STO memristor, while the HRS remains stable, the LRS relaxes first and eventually converts to a stable intermediate state. Such significant differences in ON/OFF ratio and retention between the two memristors may be associated with the epitaxial strain-mediated oxygen vacancy generation and migration, which effectively modulated the filament growth and rupture dynamics. Therefore, strain engineering represents a rational route for modulating memristor performance for various neuromorphic applications.

Topics & Concepts

MemristorNeuromorphic engineeringStrain engineeringMaterials scienceEpitaxyThin filmStrain (injury)ElectrodeOptoelectronicsNanotechnologyComposite materialElectronic engineeringChemistrySiliconComputer scienceInternal medicineLayer (electronics)Machine learningMedicineEngineeringArtificial neural networkPhysical chemistryAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering
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