III-nitride memristors: materials, devices, and applications

被引:0
作者
Yang, Yang [1 ]
Li, Haotian [1 ]
Hua, Qilin [1 ]
机构
[1] Beijing Inst Technol, Sch Integrated Circuits & Elect, Beijing 100081, Peoples R China
来源
MATERIALS FUTURES | 2025年 / 4卷 / 03期
基金
中国国家自然科学基金;
关键词
memristor; III-nitride; nonvolatile; neuromorphic computing; wurtzite; piezoelectric; RANDOM-ACCESS MEMORY; PHASE-CHANGE MATERIALS; BORON-NITRIDE; POLARIZATION; PROSPECTS; UNIVERSAL; FIELD; GAN;
D O I
10.1088/2752-5724/ade5be
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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 (102-103 cm2 (V<middle dot>s)-1), high thermal conductivity (up to 400 W (m<middle dot>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.
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页数:21
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