在基于的值切换记忆器中以尖峰编码和时空总和实现神经元动态,用于异步信号集成
Pei-Lin Lin1, Zih-Siao Liao1, Shuai-Ming Chen1
1Department of Materials Science and Engineering, National Cheng Kung University, Tainan 70101, Taiwan. jenschen@ncku.edu.tw.
Nanoscale horizons
|December 11, 2024
概括
这项研究引入了一种新的人工神经元电路,使用Pt/V/AlO/Pt值切换记忆器 (TSM). 这个设备模拟了生物神经元的功能,为神经形态计算提供了高效的尖端生成.
科学领域:
- 材料科学与工程 材料科学与工程
- 神经科学是一个神经科学.
- 电气工程 电气工程
背景情况:
- 人工神经元设备对于大脑模拟和生物启发的电子产品至关重要.
- 门切换记忆元 (TSM) 提供了模拟神经元动态的潜力.
研究的目的:
- 使用Pt/V/AlO/Pt TSM.设计和演示一个人工神经元电路.
- 评估其用于神经形态计算应用的性能,重点关注尖端生成.
主要方法:
- 基于 Pt/V/AlO/Pt TSM 的人工神经元电路的制造.
- 应用于电压脉冲来诱导泄漏的整合和火 (LIF) 行为和信号总和.
- 切换速度,稳定性和能源消耗的表征.
主要成果:
- 该电路成功地展示了LIF行为,空间和时空总和.
- 实现了超快的切换速度 (∼165 ns/310 ns) 和高耐久性 (>102周期).
- 低值电压 (∼0.84 V) 导致低能耗 (∼2.75 nJ 每个尖峰).
结论:
- 基于 Pt/V/AlO/Pt TSM 的人工神经元电路是神经形态系统中尖端生成器的有希望的候选者.
- 它的可调节峰值频率 (200 kHz800 kHz) 和低功耗提供了显著的优势.
- 这项技术推动了高效和生物启发的电子系统的发展.
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