使用基于a-HfSiOx的memristor模仿生物突触:对人工智能和记忆应用的含义
Muhammad Ismail1, Maria Rasheed1, Chandreswar Mahata1
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
Nano convergence
|July 10, 2023
概括
哈夫尼氧化记忆器在人工智能应用中表现出卓越的多层切换和突触功能. 这些设备具有很高的耐力和保留能力,证明了先进的神经形态计算系统的潜力.
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:
- 由于其类似突触的结构,记忆器对人工智能至关重要.
- 多层数据存储需要精确的传导控制和低过渡能量.
研究的目的:
- 研究用于多层切换内存和神经形态计算的无形氧化 (a-HfSiOx) 记忆器.
- 评估Pt/a-HfSiOx/TaN记忆器的电气和生物特性.
主要方法:
- 原子层沉积 (ALD) 用于记忆器制造.
- 用X射线衍射 (XRD) 和X射线光电子光谱 (XPS) 进行材料分析.
- 传输电子显微镜 (TEM) 用于结构确认.
主要成果:
- 证明了具有高耐久性 (1000 个周期) 和保留 (10 秒) 的模拟双极切换.
- 通过控制当前的合规性和重置电压,实现了多级切换.
- 展现了各种突触性质,包括短期可塑性和配对脉冲促进.
- 在神经网络模拟中达到94.6%的模式准确度.
结论:
- 基于a-HfSiOx的memristors显示出对多层级内存应用的重大承诺.
- 记忆器具有有效的神经形态计算系统的基本特征.
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