使用双极WSe2 n型/p型铁电场效应晶体管的尖端神经网络的紧物理实现
Jiali Huo1, Lingqi Li1, Haofei Zheng1
1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.
ACS nano
|October 3, 2024
概括
这项研究展示了一种结合神经元和突触功能的新型设备,用于高效的尖端神经网络 (SNN). 集成的铁电晶体管在MNIST识别中实现了高精度,推进了神经形态计算.
科学领域:
- 材料科学与工程 材料科学与工程
- 神经科学和神经形态计算的神经科学和神经形态计算
背景情况:
- 尖端神经网络 (SNN) 模仿生物过程,以节能,事件驱动的计算.
- 现有的设备缺乏集成的神经元和突触功能.
- 铁电材料为先进的计算提供可调节的电子特性.
研究的目的:
- 通过实验展示一个单一的设备,整合神经元和突触功能.
- 为了利用双极WSe2和Hf0.5Zr0.5O2铁电材料来实现混合功能.
- 推进紧的神经形态计算应用程序和SNN开发.
主要方法:
- 使用Hf0.5Zr0.5O2.2.制造一个双极WSe2n型/p型铁电场效应晶体管 (n/p-FeFET),使用Hf0.5Zr0.5O2.2.
- 在n-FeFET中表征非挥发性突触行为,在p-FeFET中表征挥发性神经元行为.
- 模拟SNNs使用实验校准的设备模型进行性能评估.
主要成果:
- n/p-FeFET成功地将挥发性和非挥发性特性集成到一个单一的设备中.
- 非挥发性n-FeFET表现出线性突触行为,而挥发性p-FeFET表现出自我恢复的神经元动态.
- 使用开发的设备模型,模拟的SNN在MNIST数字识别上实现了93.8%的准确性.
结论:
- 集成的n/p-FeFET代表了迈向紧型神经形态计算系统的重要一步.
- 这种方法使SNN具有高生物模拟准确性和降低硬件复杂性的SNN.
- 该设备为节能,灵感来自大脑的人工智能提供了一个有前途的平台.
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