Issue

Vol. 18 (2026)

High-Entropy Oxide Memristors for Neuromorphic Computing: From Material Engineering to Functional Integration

https://doi.org/10.1007/s40820-025-01891-1
Jia‑Li Yang
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Xin‑Gui Tang
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Xuan Gu
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Qi‑Jun Sun
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Zhen‑Hua Tang
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Wen‑Hua Li
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
Yan‑Ping Jiang
School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China

Corresponding Author: Xin‑Gui Tang

xgtang@gdut.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 41

Abstract

High-entropy oxides (HEOs) have emerged as a promising class of memristive materials, characterized by entropy-stabilized crystal structures, multivalent cation coordination, and tunable defect landscapes. These intrinsic features enable forming-free resistive switching, multilevel conductance modulation, and synaptic plasticity, making HEOs attractive for neuromorphic computing. This review outlines recent progress in HEO-based memristors across materials engineering, switching mechanisms, and synaptic emulation. Particular attention is given to vacancy migration, phase transitions, and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems. Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined. While encouraging results have been achieved at the device level, challenges remain in conductance precision, variability control, and scalable integration. Addressing these demands a concerted effort across materials design, interface optimization, and task-aware modeling. With such integration, HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.


Highlights:
1 Comprehensive overview of high-entropy oxides (HEOs) in memristive devices, emphasizing their potential in neuromorphic computing and their ability to simulate synaptic plasticity and multilevel conductance modulation.
2 Detailed exploration of resistive switching mechanisms in HEO-based memristors, focusing on vacancy migration, phase transitions, and valence-state dynamics, which underpin their performance in brain-inspired electronics.
3 Insightful discussion on the challenges and opportunities for integrating HEO-based memristors into large-scale neuromorphic systems, highlighting the need for advancements in material design, interface optimization, and scalability.

Keywords

High-entropy oxides Memristors Neuromorphic computing Configurational entropy Resistive switching
Yang, Jia‑Li, Xin‑Gui Tang, Xuan Gu, Qi‑Jun Sun, Zhen‑Hua Tang, Wen‑Hua Li, and Yan‑Ping Jiang. 2025. “High-Entropy Oxide Memristors for Neuromorphic Computing: From Material Engineering to Functional Integration”. Nano-Micro Letters 18 (August):41. https://doi.org/10.1007/s40820-025-01891-1.
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