论文标题
用于神经形态计算的基于氧化石墨烯的突触概念装置
Graphene oxide based synaptic memristor device for neuromorphic computing
论文作者
论文摘要
由脑启发的神经形态计算，构成神经元和突触，具有执行复杂信息处理的能力，已经展开了一个新的计算范式，以克服von Neumann的瓶颈。可以与生物突触竞争的电子突触征房设备对于神经形态计算确实很重要。在这项工作中，我们证明了我们为开发和实现基于氧化石墨烯（GO）的Memristor设备作为突触设备的努力，该设备模仿了生物突触。实际上，该设备表现出必不可少的突触学习行为，包括模拟记忆特征，增强和抑郁。此外，通过工程前后突触后的尖刺来模仿依赖峰值的塑性学习规则。此外，还探索了诸如耐力，保留率，设备多级切换之类的非挥发性属性。这些结果表明，AG/GO/FTO Memristor设备确实是未来神经形态计算应用程序的潜在候选者。 关键字：RRAM，氧化石墨烯，神经形态计算，突触设备，增强，抑郁症
Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/FTO memristor device would indeed be a potential candidate for future neuromorphic computing applications. Keywords: RRAM, Graphene oxide, neuromorphic computing, synaptic device, potentiation, depression
