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旋转轨道扭矩驱动的双终端巨型磁电阻记忆器件用于内存计算.
Tianli Jin1,2, Bo Zhang1,3, Dihua Wu4
1School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
ACS applied materials & interfaces
|June 9, 2025
概括
本研究介绍了一种双终端旋转轨道扭矩驱动的巨型磁阻记忆装置,用于可扩展的内存处理. 该设备集成了存储和逻辑,在AI任务中实现了高精度,并证明了神经形态计算的突触可塑性.
科学领域:
- 材料科学 材料科学 材料科学
- 这就是Spintronics.
- 神经形态计算是一种神经形态计算.
背景情况:
- 人工智能和数据密集型应用程序越来越复杂,需要先进的计算架构.
- 当前的spintronic内存处理 (PIM) 解决方案虽然有前途,但由于其庞大的设备足迹,面临着可扩展性问题.
- 记忆设备对于高性能和可扩展的PIM至关重要.
研究的目的:
- 为了展示一个可扩展的双终端旋转轨道扭矩 (SOT) 驱动的巨型磁阻 (GMR) 记忆装置.
- 将数据存储和逻辑函数集成到单个单元中,以实现高效的内存计算.
- 探索该设备在人工智能 (AI) 和神经形态应用中的潜力.
主要方法:
- 使用Pt/Co/Cu/CoTb堆制造一个双终端GMR记忆装置.
- 调节SOT电流幅度以实现多个非挥发性电阻状态.
- 调整CoTb合金组成,以创建具有相反极性的额外电阻状态.
- 通过电流脉冲调节来证明突触可塑性 (长期强化和抑郁).
- 使用GMR设备模拟一个深度神经网络 (DNN).
主要成果:
- 通过调节SOT电流,实现了十个非挥发性电阻状态.
- 通过调整合金组成,创建了具有相反极性的额外状态.
- 成功证明了突触可塑性功能.
- 模拟DNN在手写数字识别和图像可视化方面实现了92%的准确性.
- 展示了基本的布尔逻辑函数,证实了处理能力.
结论:
- 开发的双终端GMR记忆器件为先进的记忆器和内存计算提供了可扩展的解决方案.
- 这个设备集成了存储和逻辑,为更高效的AI硬件铺平了道路.
- 在突触可塑性和布尔逻辑中展示的功能突出显示了它在神经形态计算应用中的潜力.
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