改进了缩放纳米记忆器中的挥发性切换,以实现高性能和高效的储计算
Dongyeol Ju1, Jungwoo Lee1, Sungjun Kim1
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea.
The Journal of chemical physics
|July 2, 2024
概括
这项研究介绍了用于挥发性储库计算的缩放式memristor,证明了更好的切换稳定性和突触功能. 该设备在神经形态系统中实现了高模式准确性,从而实现了高效的时间学习.
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:
- 导电桥型随机访问存储器 (RAM) 提供适合物理储库的挥发性属性,适合储库计算中的物理储库.
- 由于它们的突触可塑性和记忆能力,记忆器对神经形态系统至关重要.
研究的目的:
- 制造和描述一个缩放的Cu/HfOx/n+-Si记忆器,用于增强的储计算应用.
- 为了评估设备的突触特征和神经形态系统中的模式识别精度.
主要方法:
- 用纳米底电极制造规模化的Cu/HfOx/n+-Si记忆器.
- 使用扫描电子显微镜 (SEM) 和传输电子显微镜 (TEM) 进行表征.
- 评估突触功能 (导电变化,配对脉冲促进,刺激后突触电流) 和储库计算性能.
主要成果:
- 与较大的细胞相比,缩放的memristor表现出优越的循环到循环切换可变性.
- 证实了神经网络仿真所必需的突触特征.
- 使用深度神经网络和修改的国家标准与技术研究所 (NIST) 数据库实现了高模式准确性.
- 演示了一个由输入脉冲振幅可控制的六个不同的状态的水库计算系统.
结论:
- 缩放式memristor是一个高性能,高效的组件,用于挥发性储库计算.
- 行为控制和系统级模拟验证了它在神经形态系统中的潜力.
- 这项工作推动了节能和可扩展的神经形态硬件的开发.
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