在VO2/La0.7Sr0.3MnO3/Al2O3 (0001) 中进行重新配置的电阻开关,用于神经形态计算的记忆器件
Sundar Kunwar1, Nicholas Cucciniello1,2, Alessandro R Mazza1,3
1Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
ACS applied materials & interfaces
|April 5, 2024
概括
这项研究介绍了VO2/LSMO记忆设备中的双电阻切换模式,使大脑功能能够灵活模拟. 这些设备为低功耗,高密度的神经形态计算提供了途径,并避免了隐形路径电流.
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.
- 电气工程 电气工程
背景情况:
- 记忆器件对于模拟神经元和突触等大脑功能至关重要.
- 在单个设备中整合非挥发性和挥发性切换模式简化了神经形态架构.
- 记忆网络中的基于晶体管的选择器增加了功耗,降低了集成密度.
研究的目的:
- 在VO2/LSMO双层记忆器件中报告双电阻开关 (RS) 模式.
- 为了证明创建低功耗,高密度的记忆性尖端神经网络的潜力.
- 探索这些设备在神经形态计算和memristive交叉条数组中的应用.
主要方法:
- 制造VO2/La0.7Sr0.3MnO3 (LSMO) 双层记忆器件的设备.
- 电气表征用于识别和区分非挥发性和挥发性电阻开关机制.
- 分析氧气空缺和金属绝缘体过渡 (MIT) 在控制RS模式中的作用.
主要成果:
- 在单个设备中证明了非挥发性 (由氧空位驱动) 和挥发性 (由VO2 MIT驱动) RS模式的共存.
- 在两个RS模式之间实现了电转换,实现了1选择器-1电阻 (1S1R) 电池功能.
- 确认了两个RS模式的稳定性和可重复性,可通过接口和相位过渡属性进行重新配置.
结论:
- VO2/LSMO双层记忆器具有双 RS 模式,适合高级神经形态应用.
- 1S1R单元格功能解决了记忆性横条数组中的潜入路径当前问题.
- 这些发现为高效,高密度的记忆神经网络和神经形态计算系统铺平了道路.
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