III-nitride memristors: materials, devices, and applications
Abstract Memristors, with their compactness, nonvolatile storage, and dynamic resistance modulation, are poised to revolutionize next-generation memory and neuromorphic computing paradigms. III-nitride materials, such as boron nitride (BN), gallium nitride (GaN), and aluminum nitride (AlN), exhibit exceptional properties for advancing memristive technologies, including wide bandgaps (3.4–6.2 eV), high electron mobility (10 2 –10 3 cm 2 (V·s) −1 ), high thermal conductivity (up to 400 W (m·K) −1 ), and robust resistance to harsh environments (e.g. extreme temperatures, radiation). Coupled with inherent complementary metal-oxide-semiconductor (CMOS) compatibility, these attributes position nitride-based memristors as a transformative platform for scalable, energy-efficient, and reliable electronics. In this review, we systematically examine recent advancements in III-nitride memristors, with a focus on materials engineering, device structures, and emerging applications. We begin by outlining the unique advantages of III-nitride materials for memristor design, followed by a critical analysis of progress in BN, GaN, AlN, and AlScN-based devices. We then explore their hardware-level implementations, demonstrating their role in next-generation chip architectures. Finally, we discuss the challenges and future directions to advance nitride-based memristive technologies. Notably, III-nitride memristors unlock unprecedented opportunities for high-performance electronics in extreme environments while bridging the gap between bio-inspired computing paradigms and hardware scalability, enabling adaptive, high-speed, and energy-efficient intelligent systems.
Authors
- Qilin Hua (ORCID: https://orcid.org/0000-0002-5269-5532)
- Haotian Li (ORCID: https://orcid.org/0000-0002-8430-4487)
- Yang Yang (ORCID: https://orcid.org/0000-0001-8833-7641)
Institutions
- Beijing Institute of Technology (CN)
Publication Details
- Journal
- Materials Futures
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1088/2752-5724/ade5be
- Citations
- 7
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 5.11
Funders
- National Natural Science Foundation of China
- Beijing Institute of Technology Research Fund Program for Young Scholars
- Fundamental Research Funds for the Central Universities