Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

作者：Changhong Wang, Wei He, Yi Tong, Yishu Zhang, Kejie Huang, Li Song, Shuai Zhong, Rajasekaran Ganeshkumar, Rong Zhao · 发表于：Small · 年份：2017 · DOI：10.1002/smll.201603435 · 被引用次数：63 · 研究领域：Advanced Memory and Neural Computing、Graphene research and applications、Carbon and Quantum Dots Applications

Memristive devices, having a huge potential as artificial synapses for low-power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability. Fundamentally, the poor repeatability of analog resistance states is closely associated with the random formation of conductive filaments, which consists of oxygen vacancies. In this work, graphene quantum dots (GQDs) are introduced into memristive devices. By virtue of the abundant oxygen anions released from GQDs, the GQDs can serve as nano oxygen-reservoirs and enhance the localization of filament formation. As a result, analog resistance states with highly tight distribution are achieved with nearly 85% reduction in variations. In addition the insertion of GQDs can alter the energy band alignment and boost the tunneling current, which leads to significant reduction in both switching voltages and their distribution variations. This work may pave the way for achieving artificial neural networks with accurate and efficient learning capability.