多态复合磁道交叉点突触用于数字识别
Anuj Kumar1, Dennis J X Lin2, Debasis Das3
1Physics Department, National University of Singapore, 117551 Singapore.
ACS applied materials & interfaces
|February 20, 2024
概括
研究人员开发了一种新的多态磁道结 (MTJ) 装置,用于生物灵感计算. 这种旋转轨道扭矩 (SOT) MTJ突触在人工神经网络中实现了高精度,为先进的神经形态硬件提供了途径.
科学领域:
- 这就是Spintronics.
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
背景情况:
- 目前的人工神经网络 (ANN) 研究旨在利用磁道结 (MTJ) 技术模拟多态突触.
- 现有的旋转转矩 (STT) MTJ ANNs面临着诸如低热可靠性和高临界电流密度等挑战.
- 旋转轨道扭矩 (SOT) MTJ使用域壁运动的ANN表现出小的读取信号和可扩展性问题.
研究的目的:
- 提出并演示使用复合SOT-MTJs的新型多态突触装置概念.
- 以改进的突触重量表示来模仿基于旋转的人工神经网络.
- 为神经形态计算应用提供一个行业兼容的平台.
主要方法:
- 在共享写入通道上开发了一种复合MTJ设备,具有多个SOT-MTJ (n=1-4).
- 使用电压脉冲,脉冲持续时间和平面内磁场调整突触电阻状态 (n+1状态).
- 制造和特征复合MTJ设备,测量道磁阻 (TMR) 差异.
主要成果:
- 通过控制电压脉冲 (±1.5-1.8V) 和持续时间 (100-300 ns) 来实现可调节的电阻状态 (突触重量).
- 在4细胞化合物MTJ中,在连续状态之间观察到显著的TMR差异 (>13.6%),是4倍的改善.
- 在使用与复合MTJ构建的ANN进行数字识别任务时,证明了高学习精度 (高达95.75%).
结论:
- 拟议的多态SOT-MTJ复合器件有效地模仿生物启发计算的突触行为.
- 与先前的技术相比,该设备在TMR差异和突触状态分辨率方面提供了显著的改进.
- 这项技术提供了一个行业兼容的途径,用于将高级突触集成到神经形态架构中.
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