Wang Yu, Chen Xintong, Shen Daqi, Zhang Miaocheng, Chen Xi, Chen Xingyu, Shao Weijing, Gu Hong, Xu Jianguang, Hu Ertao, Wang Lei, Xu Rongqing, Tong Yi
College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gusu Laboratory of Materials, Suzhou 215000, China.
Nanomaterials (Basel). 2021 Oct 27;11(11):2860. doi: 10.3390/nano11112860.
Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(VC)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our VC-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., VC) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.
人工突触和神经元是构建硬件神经网络的两个关键的基本组件。由于其高密度集成、出色的非线性和可调制可塑性，忆阻器在模拟生物突触和神经元方面引起了越来越多的关注。然而，在无额外电子元件的情况下制造低功耗且稳健的基于忆阻器的人工神经元，对于受脑启发的系统来说仍是一项挑战。在这项工作中，我们展示了一种基于二维（2D）MXene（VC）的单阈值开关（TS）忆阻器，无需辅助电路即可模拟泄漏积分发放（LIF）神经元，其源于基于银扩散的丝状机制。此外，我们基于VC的人工神经元忠实地实现了多种神经功能，包括泄漏积分、阈值驱动发放、自我弛豫以及线性强度调制的脉冲频率特性。这项工作表明，三原子型MXene（例如VC）忆阻器可能为构建硬件神经形态计算系统提供一种有效的方法。
