单个设备几何学中的双SOT切换模式用于神经形态计算
Abhijeet Ranjan1, Tamkeen Farooq2, Chong-Chi Chi3
1Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
Nano letters
|April 17, 2025
概括
本研究介绍了一种用于神经形态计算的新型设备,该设备使用双旋转轨道扭矩 (SOT) 切换模式. 这一突破使高级人工神经网络 (ANN) 的二进制和多层切换成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 物理 物理学 物理
背景情况:
- 神经形态计算试图利用人工神经元和突触模仿大脑的效率.
- 对于这些功能来说,二进制和多级切换是非常重要的.
- 现有的设备往往需要修改,以实现双模式切换.
研究的目的:
- 为神经形态应用提供一种能够实现二进制和多层次切换的单一设备.
- 为了展示该设备在人工神经网络 (ANN) 中的潜力.
- 为了突出提出的设备的能源效率和简化制造.
主要方法:
- 一个PtMn/(Co/Pd) 4/Ta装置的制造.
- 使用双旋转轨道扭矩 (SOT) 切换机制.
- 调查不同当前治疗 (中度与高度) 引起的结构变化.
主要成果:
- 该设备在同一结构内独特地展示了二进制和多层切换.
- 二进制切换发生在~65mA的域壁传播中.
- 多级切换是通过在85mA的域核化实现的,这归因于结构变化.
- 该设备在使用ANN的MNIST数据集上实现了96%的数字/字母识别准确度.
- 在一个小的平面内场下,证明了强大的垂直磁性异构性 (PMA) 和双模式切换.
结论:
- 开发的设备为神经形态计算提供了一个有希望的,节能的解决方案.
- 它的双模式切换功能消除了对设备修改的需求.
- 简化制造和强大的性能使其适用于先进的AI应用.
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