增强 (Co,Yb) 协ZnO记忆器的电磁性能,用于神经形态计算
Noureddine Elboughdiri1,2, Shahid Iqbal3, Sherzod Abdullaev4,5
1Chemical Engineering Department, College of Engineering, University of Ha'il P.O. Box 2440 Ha'il 81441 Saudi Arabia.
RSC advances
|December 13, 2023
概括
我们开发了用于神经形态计算的 (Co,Yb) 联合氧化物 (ZnO),增强导电性和铁磁性. 这种材料显示稳定的电阻切换,非常适合未来的电子设备和系统.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 纳米技术纳米技术
背景情况:
- 神经形态计算需要先进的材料来有效处理信息.
- 氧化 (ZnO) 是电子设备的一个有前途的半导体.
- 兴奋剂ZnO可以调整其电磁性质.
研究的目的:
- 为了研究用于神经形态应用的 (Co,Yb) 配ZnO的特性.
- 使用 (Co,Yb) co-ZnO.O.制造和描述一个memristor设备.
- 了解制造的memristor中的电阻开关机制.
主要方法:
- (Co,Yb) 联合化 ZnO 纳米颗粒的水热合成.
- 在Au电极之间制造具有 (Co,Yb) 同ZnO活性层的记忆器装置.
- 材料特性 (形态,电气,磁性) 和设备性能 (电阻切换,耐久性,保留) 的表征.
主要成果:
- 在ZnO中,Co和Yb兴奋剂增加了氧空缺和粒径,增强了导电性和室温铁磁性.
- 制造的memristor在低电阻和高电阻状态之间展示了可重复的模拟电阻切换.
- 该设备在100个循环中表现出稳定的耐用性,并保持长达1000秒的时间,其切换机制归因于Au离子迁移和线丝形成.
结论:
- Co和Yb联合合的ZnO是神经形态计算应用的一个有前途的材料.
- 记忆器设备显示了电子系统的优秀性能特征.
- 这些发现有助于开发用于下一代计算的先进材料.
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