High-Entropy Oxide Memristors for Neuromorphic Computing: From Material Engineering to Functional Integration - Yang, Jia‑Li; Tang, Xin‑Gui; Gu, Xuan; Sun, Qi‑Jun; Tang, Zhen‑Hua; Li, Wen‑Hua; Jiang, Yan‑Ping (Nano-Micro Letters, 2025)
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.