Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness
Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness
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DOI：
10.1109/ted.2021.3111846
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发表时间：
2020-10
影响因子：
3.1
通讯作者：
W. A. Misba;Tahmid Kaisar;Dhritiman Bhattacharya;J. Atulasimha
中科院分区：
文献类型：
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作者：
W. A. Misba;Tahmid Kaisar;Dhritiman Bhattacharya;J. Atulasimha
我们提出了一种高效的电压感应应变控制压电衬底上垂直磁化纳米赛道中的畴壁（DW），该畴壁可以实现用于神经形态计算平台的多状态突触。在具有显著的Dzyaloshinskii-Moriya相互作用（DMI）的系统中，压电体中产生的应变被机械地传递到磁道并调节垂直磁各向异性（PMA）。当施加不同的电压（即产生不同的应变），再加上重金属层中流过固定时间的固定电流所产生的自旋轨道转矩（SOT）时，dw会被转换到不同的距离，实现不同的突触权值。我们已经使用微磁模拟表明，五态和三态突触可以在包含自然边缘粗糙度和室温热噪声的赛道中实现。这些模拟显示了由于与粗糙度诱导的钉钉位点的相互作用，DWs的有趣动力学。因此，不需要制造缺口来实现多状态非易失性突触。这种应变控制突触的能量消耗约为1 fJ，因此可以非常有吸引力地实现节能量化神经网络，最近已经证明它可以达到与全精度神经网络接近的分类精度。
We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii–Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin–orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks.