Enhanced synaptic properties in HfO2-based trilayer memristor by using ZrO2-x oxygen vacancy reservoir layer for neuromorphic computing

doi:10.1016/j.jmst.2024.12.020

Abstract

Abstract: Neuromorphic computing devices leveraging HfO₂ and ZrO₂ materials have recently garnered significant attention due to their potential for brain-inspired computing systems. In this study, we present a novel trilayer Pt/HfO₂/ZrO_2-_x/HfO₂/TiN memristor, engineered with a ZrO_2-_x oxygen vacancy reservoir (OVR) layer fabricated via radio frequency (RF) sputtering under controlled oxygen ambient. The incorporation of the ZrO_2-_x OVR layer enables enhanced resistive switching characteristics, including a high ON/OFF ratio (∼8000), excellent uniformity, robust data retention (>10⁵ s), and multilevel storage capabilities. Furthermore, the memristor demonstrates superior synaptic plasticity with linear long-term potentiation (LTP) and depression (LTD), achieving low non-linearity values of 1.36 (LTP) and 0.66 (LTD), and a recognition accuracy of 95.3% in an MNIST dataset simulation. The unique properties of the ZrO_2-_x layer, particularly its ability to act as a dynamic oxygen vacancy reservoir, significantly enhance synaptic performance by stabilizing oxygen vacancy migration. These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.

Key words: HfO₂-based trilayer memristor, ZrO_2-_x oxygen vacancy reservoir, Synaptic plasticity, Non-volatile memory, Neuromorphic computing

Turgun Boynazarov, Joonbong Lee, Hojin Lee, Sangwoo Lee, Hyunbin Chung, Dae Haa Ryu, Haider Abbas, Taekjib Choi. Enhanced synaptic properties in HfO₂-based trilayer memristor by using ZrO_2-_x oxygen vacancy reservoir layer for neuromorphic computing[J]. J. Mater. Sci. Technol., 2025, 227: 164-173.

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Enhanced synaptic properties in HfO₂-based trilayer memristor by using ZrO_2-_x oxygen vacancy reservoir layer for neuromorphic computing

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