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Piezo-strain induced nonvolatile control of magnetic skyrmion nonlinear dynamics for artificial synapse device applications

空中骑兵 非线性系统 动力学(音乐) 材料科学 突触 拉伤 光电子学 纳米技术 物理 凝聚态物理 声学 神经科学 生物 解剖 量子力学
作者
Jiawei Wang,Xin Wang,Yiting Li,Guoliang Yu,Yang Qiu,Yan Li,Mingmin Zhu,Hao-Miao Zhou
出处
期刊:Applied Physics Letters [American Institute of Physics]
卷期号:126 (8)
标识
DOI:10.1063/5.0250044
摘要

High-performance artificial synaptic devices that emulate the functions of biological synapses are crucial for advancing energy-efficient brain-inspired computing systems. Current studies predominantly focus on memristive devices, which achieve synaptic functions through nonvolatile electric current-assisted carrier modulation. However, these methods often suffer from excessive energy consumption. Here, a type of low-energy-consumption artificial synapse based on strain-mediated electric-field control of magnetic skyrmion's radius is demonstrated, where the energy consumption is 10 fJ per state and the non-volatility is achieved by local ferroelectric domain switching under bipolar electric fields. The proposed skyrmion-based synaptic device can replicate essential synaptic behaviors, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, paired-pulse depression, and spiking-time-dependent plasticity, aligning it closely with the biological synaptic system. The synaptic weight change and non-linearity of the artificial synapse are emulated by modulating the magnetic skyrmion's radius through precisely engineering the applied electric-field pulses. Simulation using the Modified National Institute of Standards and Technology database reveals that the pattern recognition rate decreases exponentially with increasing LTP/LTD non-linearity, quantifying the effect of the LTP/LTD non-linearity on the pattern recognition rate. This work underscores the potential of strain-mediated electric-field control of single skyrmion's radius as a groundbreaking approach for developing high density and low-energy consumption artificial synaptic devices.
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