Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well-suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of these artificial neuromorphic devices still require the use of CMOS, which decreases the efficiency of the system. To resolve this, we have previously proposed a number of artificial neurons and synapses that do not require CMOS for operation. Although these devices are a significant improvement over previous renditions, their ability to enable neural network learning and recognition is limited by their intrinsic activation functions. This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track. Linear and sigmoidal activation functions are demonstrated in this work, which can be extended through a similar approach to enable a wide variety of activation functions.

中文翻译：

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具有基于形状的可配置激活函数的域壁泄漏集成和发射神经元

互补金属氧化物半导体 (CMOS) 器件显示出易失性特性，不太适合模拟应用，例如神经形态计算。另一方面，自旋电子器件具有非易失性和模拟特性，非常适合神经形态计算。因此，这些新型器件处于超越 CMOS 人工智能应用的前沿。然而，大量这些人工神经形态器件仍然需要使用CMOS，这降低了系统的效率。为了解决这个问题，我们之前已经提出了一些不需要 CMOS 操作的人工神经元和突触。虽然这些设备比以前的版本有了显着的改进，它们使神经网络学习和识别的能力受到其内在激活函数的限制。这项工作提出了对这些自旋电子神经元的修改，通过控制磁畴壁轨道的形状来配置激活功能。在这项工作中展示了线性和 sigmoidal 激活函数，可以通过类似的方法对其进行扩展，以启用各种激活函数。