双终端矿光电子突触用于快速训练的神经形态计算,具有高精度.
Linqi Guo1, Haoxuan Sun1, Liangliang Min1
1School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China.
Advanced materials (Deerfield Beach, Fla.)
|March 30, 2024
概括
本研究介绍了一种用于人工视觉的新型双终端设备,通过控制的离子迁移和同步的光电触发实现高识别精度. 这一突破为人工智能应用增强了神经形态传感器.
科学领域:
- 光电学是指光电子产品.
- 人工智能的人工智能
- 通过神经系统启发的计算.
背景情况:
- 神经形态视觉传感器整合了感知,记忆和计算,有望在人工智能领域取得进展.
- 现有的双终端设备在缺陷控制和离子特性方面面临挑战,这限制了它们的突触行为.
- 稳定,可重新配置的光感应突触行为通常需要独立的门口调制.
研究的目的:
- 在双终端设备中证明稳定,可重新配置和精确可控的突触后电流.
- 为了研究离子迁移障碍和读出电压之间的协同作用,用于突触行为.
- 为神经形态电路开发具有感知,记忆和计算能力的光电子设备.
主要方法:
- 利用离子迁移屏障和读出电压之间的协同作用来控制后突触电流.
- 实施光学和电信号同步触发作为预处理方法.
- 在训练期间利用渐进的离子积累来调节光电流和动态学习速率.
主要成果:
- 使用光学和电信号同步触发实现了96.5%的识别精度.
- 通过使用光电流演变作为动态学习速率参考,在10个时代内提升精度至97.8%.
- 在20ns光脉冲下,证明了 postsynaptic电流调制潜力.
结论:
- 离子迁移屏障和读出电压之间的协同作用对于在双端设备中稳定,可重新配置的突触后电流至关重要.
- 同步的光电触发和动态学习速率引用显著提高了识别准确性.
- 开发的光电子设备有可能推进光子神经形态电路和人工视觉系统.
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