论文标题
神经元扭矩结构壁器件中神经元和突触功能的仿真
Emulation of Neuron and Synaptic Functions in Spin-Orbit Torque Domain Wall Devices
论文作者
论文摘要
神经形态计算(NC)体系结构已显示其适合节能计算。在几个系统中,基于自旋轨道扭矩(SOT)域壁(DW)设备是NC最节能的竞争者之一。要实现基于自旋的NC结构,需要开发诸如合成神经元和突触之类的计算元素。但是,关于DW神经元和突触的实验研究很少。本研究通过使用新颖的阅读和写作策略来证明神经元和突触的节能操作。我们已经使用了基于COFEB的节能SOT机制来驱动低电流密度的DWS。我们已经使用了曲折设备的概念来实现突触功能。通过这样做,我们在实验中实现了9种不同的电阻状态。我们通过实验证明了功能性尖峰和阶跃神经元。此外,我们通过将几对与常规大厅的十字融合在一起,以提高灵敏度以及信噪比(SNR)来设计异常的大厅杆。我们进行了微磁模拟和运输测量,以证明上述功能。
Neuromorphic computing (NC) architecture has shown its suitability for energy-efficient computation. Amongst several systems, spin-orbit torque (SOT) based domain wall (DW) devices are one of the most energy-efficient contenders for NC. To realize spin-based NC architecture, the computing elements such as synthetic neurons and synapses need to be developed. However, there are very few experimental investigations on DW neurons and synapses. The present study demonstrates the energy-efficient operations of neurons and synapses by using novel reading and writing strategies. We have used a W/CoFeB-based energy-efficient SOT mechanism to drive the DWs at low current densities. We have used the concept of meander devices for achieving synaptic functions. By doing this, we have achieved 9 different resistive states in experiments. We have experimentally demonstrated the functional spike and step neurons. Additionally, we have engineered the anomalous Hall bars by incorporating several pairs, in comparison to conventional Hall crosses, to increase the sensitivity as well as signal-to-noise ratio (SNR). We performed micromagnetic simulations and transport measurements to demonstrate the above-mentioned functionalities.