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Graphene oxide based synaptic memristor device for neuromorphic computing

机译：基于石墨烯氧化物的神经晶体计算突触膜装置

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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/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.
机译：大脑启发的神经形态计算由神经元和突触组成，具有执行复杂信息处理的能力，为克服冯·诺依曼瓶颈开辟了一种新的计算范式。电子突触忆阻器可以和生物突触竞争，这对于神经形态计算来说确实非常重要。在这项工作中，我们展示了我们开发和实现基于氧化石墨烯（GO）的记忆器件作为突触器件的努力，该器件模拟生物突触。事实上，这个装置展示了基本的突触学习行为，包括模拟记忆特征、增强和抑制。此外，通过设计突触前和突触后的尖峰，模拟了依赖于尖峰时间的可塑性学习规则。此外，还探讨了该器件的耐久性、保持性、多级开关等非易失性特性。这些结果表明，Ag/GO/氟掺杂氧化锡忆阻器器件确实是未来神经形态计算应用的潜在候选器件。

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