基于二维范德瓦尔斯铁电半导体的互锁铁电贡献高性能记忆器的计算研究
Zhuo Chen1,2, Yu-Chen Li2, Tie-Lin Kong1,2
1National Laboratory of Solid State Microstructures and Department of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210023, China.
ACS applied materials & interfaces
|May 8, 2024
概括
一种新的计算方法准确地预测了memristor电阻切换,使人工智能能够设计出新的铁电memristor. 这种方法支持探索先进计算应用的memristor机制和材料.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 人工智能 硬件 硬件
背景情况:
- ·诺伊曼计算机架构面临着人工智能 (AI) 的局限性.
- 记忆器为内存和神经形态计算提供了一个有前途的解决方案.
- 缺乏对memristors的系统计算研究,阻碍了材料和机制的探索.
研究的目的:
- 开发一种用于模拟memristor电阻切换的计算方法.
- 设计和研究使用铁电半导体的新型多终端记忆器.
- 为memristor开发和材料选提供理论支持.
主要方法:
- 开发了一种使用第一原则计算参数进行电阻切换的计算方法.
- 将该方法应用于双终端金属/铁电半导体/金属记忆器.
- 设计和分析了利用互锁铁电的各种多终端记忆器.
主要成果:
- 计算方法与实验测量结果有很好的一致性.
- 成功设计了表现出异质突触可塑性的多终端记忆器.
- 证明了在设计设备中描述异质突触行为的能力.
结论:
- 开发的计算方法可以作为模拟铁电记忆器的范例.
- 这项研究激发了进一步的计算探索,并提供了一个设备优化策略.
- 基于2D范德瓦尔斯铁电半导体的拟议异质突触记忆器正在等待实验验证.
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