Farcis Louis, Teixeira Bruno M S, Talatchian Philippe, Salomoni David, Ebels Ursula, Auffret Stéphane, Dieny Bernard, Mizrahi Frank A, Grollier Julie, Sousa Ricardo C, Buda-Prejbeanu Liliana D
Université Grenoble Alpes, CEA, CNRS, Grenoble-INP, SPINTEC, Grenoble 38000, France.
Unité Mixte de Physique CNRS/Thales, Université Paris-Saclay, 91767 Palaiseau, France.
Nano Lett. 2023 Sep 13;23(17):7869-7875. doi: 10.1021/acs.nanolett.3c01597. Epub 2023 Aug 17.
Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short- and long-term memory, nonlinear fast response, and relatively small footprint. Here we demonstrate experimentally how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions can emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two-terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic the neuron response in a dense neural network. The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks to sub-100 nm size elements.
由于自旋电子器件具有用于短期和长期记忆的非易失性、非线性快速响应以及相对较小的占地面积，最近在非传统计算领域引起了广泛关注。在此，我们通过实验证明了双自由层垂直磁隧道结的电压驱动磁化动力学如何在硬件中模拟脉冲神经元。通过改变器件两端的直流偏置电压来控制输出脉冲率。这种两端器件的无场操作及其对外加磁场的鲁棒性使其成为在密集神经网络中模拟神经元响应的合适候选者。该器件的低能耗（4 - 16皮焦耳/脉冲）及其可扩展性对于嵌入式应用而言是重要优势。这种紧凑的垂直磁隧道结结构最终可能使脉冲神经网络缩小到亚100纳米尺寸的元件。
