亚量子半金属二氧化和氧气空白丝记忆器用于神经形态计算
Chenyu Zhuge1, Jiandong Jiang1, Liang Chen1
1School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
ACS applied materials & interfaces
|June 3, 2025
概括
这项研究介绍了一种基于 (Bi) 的新型记忆器,用于非·诺伊曼计算. 该设备在人工神经网络模拟中表现出更高的可靠性和高精度,克服了当前memristor技术的局限性.
科学领域:
- 材料科学与工程 材料科学与工程
- 纳米技术 纳米技术
- 计算机工程 计算机工程
背景情况:
- 由于可扩展性,memristors对非·诺伊曼架构有希望.
- 当前的丝状记忆器由于随机的丝状形成造成的高可变性和低可靠性.
研究的目的:
- 提出并研究一种基于半金属 (Bi) 的新型记忆器.
- 为了阐明氧空位 (VO) -Bi纤维的切换机制.
- 为了评估拟议的memristor在人工神经网络 (ANN) 模拟中的性能.
主要方法:
- 一个基于Bi的memristor的制造和特征.
- 球形偏差校正扫描传输电子显微镜 (AC-STEM) 用于导线分析.
- 密度函数理论 (DFT) 计算用于机制阐明.
- 使用反向传播和储库计算 (RC) 的ANN模拟.
主要成果:
- 基于Bi的memristor表现出亚量子导电率变化,高切换一致性和可控制的重量更新线性.
- AC-STEM和DFT揭示了VO和Bi集群的形成机制,澄清了切换过程.
- 在ANN模拟中,高位数识别精度达到95.77% (反向传播) 和94.15% (RC).
结论:
- 基于Bi的memristor中的VO-Bi电线机制提供了一条克服传统memristor可靠性问题的途径.
- 拟议的memristor显示了高级计算应用的巨大潜力,包括神经形态系统.
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