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渐变气杂的memristor用于内存计算
Menglan Li1,2, Yangyu Dong1,2, Jiamin Tian3
1Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China.
Nano letters
|August 4, 2025
概括
渐变兴奋剂 (GND) 增强了用于内存计算 (CIM) 的HfOx记忆器. 这种方法可以提高线性和统一性,从而实现可靠的数字和模拟应用.
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:
- 基于氧化物的memristors由于CMOS兼容性,对内存计算 (CIM) 有希望.
- 在memristors中形成不均的导电丝束限制了可靠CIM的线性和循环对循环均性.
研究的目的:
- 为了提高HfOx记忆元的线性和统一性,用于CIM应用.
- 为了研究渐变兴奋剂 (GND) 对memristor性能的影响.
主要方法:
- 使用梯度兴奋剂 (GND) 制造HfOx记忆器.
- 对氧离子迁移和导电丝 (CF) 形成的分析.
- 评估memristor的线性,均性和开关特性.
- 在有状态的布尔逻辑和长短期内存 (LSTM) 网络内核中实现GND内存.
主要成果:
- GND建立了一个类似楼梯的能量屏障,调节CF生长率并增强线性.
- 剂限制了CF的生长,实现了超小的开关电压 (3.1%) 和电阻 (5.7%) 的变化.
- 使用 LSTM 核心中的 GND 记忆器数组,证明了高精度的时间序列预测.
结论:
- GND是一种有效的策略,可以同时提高HfOx记忆器的线性和统一性.
- 改进的memristors显示了数字和模拟计算内存系统的巨大潜力.
- GND 记忆器为可靠和高性能 CIM 架构提供了一条可行的途径.
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