极性控制的挥发性HfO2记忆器具有双模导电性,用于神经形态突触和储计算
Yuseong Jang1, Chanmin Hwang1, Myoungsu Chae2
1Department of Semiconductor Systems Engineering, Department of Electrical Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, 05006, South Korea.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 3, 2025
概括
这项研究引入了一种具有双模切换的新型HfO2记忆器,可以增强短期记忆 (STM) 并将数据存储容量翻一番. 这个memristor推进了神经形态计算平台.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 记忆器对下一代计算至关重要.
- 现有的memristor在数据存储和内存能力方面存在限制.
研究的目的:
- 为了引入基于HfO2的memristor与双模切换.
- 为了证明其增强短期记忆 (STM) 和增加数据存储的潜力.
- 探索其在改善神经形态系统的储库计算 (RC) 中的应用.
主要方法:
- 一个基于HfO2的memristor的制造,表现出双模切换.
- 根据输入刺激和读取电压极性调节设备导电量.
- 实现一个利用memristor能力的储计算系统.
- 在MNIST数据集上对分类准确性的评估.
主要成果:
- 记忆器表现出可靠的短期记忆 (STM) 行为.
- 它支持16个导电状态与4位脉冲输入.
- 逆转读取电压极性使每个电池的可辨别电导状态翻了一番.
- 储计算系统在MNIST数据集上实现了98.81%的分类准确性.
结论:
- 开发的memristor为神经形态计算提供了一个紧而节能的平台.
- 双模切换显著提高了表示能力和数据存储.
- 这个设备为基于memristor的先进架构铺平了道路.
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