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拓绝缘器增强超低功率的神经形态螺旋电子技术:推进高SOT效率的手写数字识别

Xi Guo¹, Junwei Zeng², Jijun Yun³

¹School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.

ACS applied materials & interfaces

|June 4, 2025

概括

研究人员使用拓绝缘体开发了超低功率的神经形状旋转电子设备. 这些设备证明了人工智能的高效率,显著降低了人工突触和神经元的功耗.

科学领域:

这就是Spintronics.
人工智能的人工智能
材料科学材料科学材料科学

背景情况:

神经形态旋转电子设备为人工智能提供了优势,但受到传统材料中低旋转轨道扭矩 (SOT) 效率的限制.
实现超低功耗对于推进高性能AI系统至关重要.

研究的目的:

为了展示低功耗的人工突触和神经元设备,提高SOT效率.
探索拓绝缘器对于高效的神经形态计算的潜力.

主要方法:

利用 (BiSb) 2Te3,一个拓绝缘体,以实现高SOT效率 (θSH = 1.11).
制造的人工突触和神经元设备,能够长期增强/抑制和刺激/抑制后突触潜在过程.
使用开发的设备实现了一个人工神经网络.

主要成果:

演示了人工突触和神经元设备,其超低激活电流密度为1.8 × 10^5 A/cm^2,比传统系统低1-2个数量级.
实现了同时长期的强化/抑郁和刺激/抑制的突触后潜能过程.
人工神经网络在手写数字识别中实现了92.8%的准确性.

结论:

像 (BiSb) 2Te3这样的拓绝缘体表现出极高的SOT效率,使得超低功率的神经形状旋转电子学成为可能.
开发的设备和人工神经网络显示出对未来节能人工智能的重大承诺.
这项工作为实用,低功耗的神经形态计算应用铺平了道路.

关键词:

人工神经网络的人工神经网络手写的数字识别手写的数字识别垂直的磁性异构性是垂直的旋转-轨道扭矩拓绝缘体的拓绝缘体是一个

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