基于用于神经形态计算的多层LiSiOmemristor的多种长期强化和抑郁
1School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China.
Nanotechnology
|August 16, 2023
概括
这项研究介绍了一种用于人工突触的新型memristor设备,增强神经形态计算. 优化的设备实现了精确,连续的导电量状态,这对于高效的生物灵感系统至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 神经科学是一个神经科学.
背景情况:
- 基于memristor的神经形态计算为传统·诺伊曼架构的局限性提供了解决方案.
- 具有连续导电量变化的人工突触装置是开发生物启发的神经形态系统的关键.
研究的目的:
- 开发一种基于memristor的人工突触,具有稳定,多层次的阻力状态.
- 为神经形态应用优化人工突触的线性和性能.
主要方法:
- 制造一个Pt/LiSiO/TiN记忆体结构.
- 通过调整初始电阻以减少非线性来优化设置过程.
- 不易挥发的多层电阻状态和导电线性的表征.
主要成果:
- 实现了100个级别的连续调制导电量状态,非线性系数降低到1.31.
- 在长期强化/长期抑郁行为中表现出改善的线性.
- 改进归因于降低了Schottky屏障高度和进化的导电纤维.
结论:
- Pt/LiSiO/TiN记忆器有效地模拟了一个具有高性能的人工突触.
- 优化的设备显示了高性能多层数据存储和神经形态计算的巨大潜力.
- 在模式识别任务中实现了强大的~94.58%的识别率.
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