在SnP2S6记忆元中,用于神经形态计算的高密度导电状态和突触可塑性
Thaw Tint Te Tun1, Jiali Huo1, Kah-Wee Ang1
1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore. eleakw@nus.edu.sg.
Nanoscale horizons
|November 28, 2025
概括
几层锡六酸 (SnP2S6) 记忆器实现325个稳定状态,以实现节能模拟记忆. 这一突破为边缘AI系统增强了神经形态计算.
科学领域:
- 材料科学 材料科学 材料科学
- 固态物理 固态物理
- 计算机工程 计算机工程
背景情况:
- 记忆器为节能模拟内存和神经形态计算提供可编程导电.
- 高内存密度和计算效率需要每个设备的多个稳定导电状态.
研究的目的:
- 为了在少数层六酸盐 (SnP2S6) 记忆器中演示多层导电性调整.
- 为了在边缘AI中实现大量的稳定状态,以提高内存密度和计算效率.
主要方法:
- 在memristor制造中使用了少数层的锡六甲酸盐 (SnP2S6).
- 实施了基于脉冲的编程方案,并设计了电压脉冲方法来抑制电流噪声.
- 分析了导电丝的演变,以优化状态稳定性.
主要成果:
- 在SnP2S6的memristor中实现了325个稳定的多层导电状态.
- 已证明有效地抑制当前噪声,最大限度地提高设备启/关比内可区分的状态.
- 成功模拟了突触可塑性 (LTP/LTD),并在人工神经网络数字分类中验证了性能.
结论:
- SnP2S6记忆器显示出作为高分辨率模拟内存的巨大潜力.
- 这些设备作为紧和高效的神经形态计算架构的有希望的构建块.
- 这些发现为下一代边缘情报应用铺平了道路.
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