电气配置的铁电和基于Redox的memristors的综合设计,用于硬件实现的储计算
Jung-Kyu Lee1, Yongjin Park2, Euncho Seo2
1Department of Semiconductor Engineering, Gyeongsang National University, Jinju, Gyeongnam, 52828, Republic of Korea.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|June 10, 2025
概括
这项研究介绍了用于高效储库计算 (RC) 的新型多功能记忆器. 这些设备通过简化硬件实现了高精度的时间序列数据处理,为先进的神经形态系统铺平了道路.
科学领域:
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 储库计算 (RC) 提供了高效的时间序列数据处理,并降低了计算需求.
- 现有的RC系统通常需要复杂的架构和高的培训成本.
- 基于memristor的硬件为紧和节能的神经形态计算提供了一个有前途的途径.
研究的目的:
- 开发和演示一个硬件实现的储计算系统,使用新的多功能memristors.
- 在单一的制造工艺中将基于铁电的memristors (FM) 作为挥发性储存层和基于氧化还原的memristors (RM) 作为非挥发性读取层集成.
- 为了评估这些多功能memristors用于神经形态应用的性能和可扩展性.
主要方法:
- 制造能够在FM和RM状态之间进行电转换的双功能记忆器.
- 综合电气特性,包括低频噪声分析和突触重量线性评估.
- 实施离线和在线培训计划,以完成水库计算任务.
主要成果:
- 在强化和抑郁期间实现了高线性因子 (<2.3%的周期变化) 来调整突触重量.
- 基于铁电的memristor表现出稳定的储状态,周期变化很低 (3.52%).
- 在修改后的国家标准与技术研究所数据集上,离线培训的准确率为93.3%;在线培训使用增量脉冲方案实现了88.2%的准确率.
结论:
- 多功能memristors实际上可以用于构建可扩展和多功能神经形态系统.
- 集成的FM / RM结构提供无操作,无需额外的制造步骤.
- 这项工作为下一代计算技术奠定了坚实的基础,利用基于memristor的储计算.
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