NanoScience Technology Center, University of Central Florida, Orlando, Florida, 32826, USA.
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, 32816, USA.
Sci Rep. 2019 Jan 10;9(1):53. doi: 10.1038/s41598-018-35828-z.
With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal-oxide-semiconductor (CMOS) circuitry in the past. Recently, metal-insulator-transition materials have been used to realize artificial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS/graphene van der Waals heterojunction system to produce the integrate-and-fire response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS, enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-firing refractory period of a neuron and strength modulated frequency response. These results show that the developed artificial neuron can play a crucial role in neuromorphic computing.
随着对低功耗电子产品需求的不断增长，神经形态计算在最近引起了极大的关注。在硬件中实现神经形态计算将为涉及图像处理和模式识别等复杂过程的应用带来巨大的推动。人工神经元是神经形态电路的关键部分，过去已经使用复杂的互补金属氧化物半导体（CMOS）电路实现了。最近，金属-绝缘体-转变材料已被用于实现人工神经元。尽管忆阻器已被用于实现突触行为，但关于这些器件实现的神经元响应的工作却很少。在这项工作中，我们使用垂直-MoS/石墨烯范德华异质结系统的易失性阈值切换行为来产生神经元的积分和点火响应。我们使用大面积化学气相沉积（CVD）石墨烯和 MoS，使这些器件能够大规模实现。这些器件可以模拟神经元的最重要特性，包括全有或全无的尖峰、动作电位的阈值驱动尖峰、神经元的后点火不应期以及强度调制的频率响应。这些结果表明，所开发的人工神经元可以在神经形态计算中发挥关键作用。
