Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness

被引:14
作者
Al Misba, Walid [1 ]
Kaisar, Tahmid [1 ]
Bhattacharya, Dhritiman [1 ]
Atulasimha, Jayasimha [1 ,2 ]
机构
[1] Virginia Commonwealth Univ, Dept Mech & Nucl Engn, Med Coll Virginia Campus, Richmond, VA 23284 USA
[2] Virginia Commonwealth Univ, Dept Elect & Comp Engn, Med Coll Virginia Campus, Richmond, VA 23284 USA
基金
美国国家科学基金会;
关键词
Domain wall (DW); edge roughness; neuromorphic computing; spin-orbit torque (SOT); synapse; MEMORY; MICROMAGNETICS; NANOMAGNETS; MOTIONS; STRAIN;
D O I
10.1109/TED.2021.3111846
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 straincontrolled synapse has an energy consumption of similar to 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 thefullprecision neural networks.
引用
收藏
页码:1658 / 1666
页数:9
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