基于SnO的存储器,具有用于高级数据存储和计算的丝状切换机制.
Muhammad Ismail1, Chandreswar Mahata1, Myounggon Kang2
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Nanomaterials (Basel, Switzerland)
|September 28, 2023
概括
这项研究介绍了一种用于神经形态计算的新型氧化 (SnOx) 记忆装置. 该设备表现出卓越的耐力,保留和多层次的阻力状态,模仿高级AI应用程序的突触功能.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机工程 计算机工程
背景情况:
- 神经形态计算旨在模仿人类大脑的结构和功能.
- 开发高效可靠的记忆器件对于神经形态系统至关重要.
- 锡氧化物 (SnOx) 是一个有前途的材料,用于电阻切换内存应用.
研究的目的:
- 制造和描述用于神经形态计算的Pt/TiN/SnOx/Pt内存设备.
- 调查TiON接口层在设备操作中的作用.
- 在耐力,保留和突触行为模拟方面评估设备的性能.
主要方法:
- 使用反应式喷雾制造Pt/TiN/SnOx/Pt内存设备的制造.
- 电气性质的表征,包括耐力,保留和电阻状态.
- 基于氧空位度梯度的丝状切换机制的分析.
主要成果:
- 基于SNO的设备显示了超过200个直流循环,开/关比 (>20) 和10s的保留.
- 实现了低设置 (9.89%) 和重置 (3.2%) 的电压变化.
- 该设备成功模拟了突触行为,包括长期强化 (LTP) 和长期抑郁 (LTD).
结论:
- Pt/TiN/SnOx/Pt设备在神经形态计算应用中表现出卓越的性能.
- TiON接口层促进氧气空位迁移,使可靠的切换成为可能.
- 基于SnOx的设备具有高密度数据存储和先进AI的巨大潜力.
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