期刊
ADVANCED FUNCTIONAL MATERIALS
卷 28, 期 1, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.201705320
关键词
artificial synapses; conductive filaments; memristors; nanoscale Ag clusters
类别
资金
- National, Natural Science Foundation of China [61306098, 61674050, 61422407]
- Natural Science Foundation of Hebei Province [E2012201088, E2013201176]
- Science Research Program of University in Hebei Province [ZH2012019]
- Top-notch Youth Project of University in Hebei Province [BJ2014008]
- project of enhancement comprehensive strength of the Midwest universities of Hebei University
- Outstanding Youth Project of Hebei Province [F2016201220]
- Hebei University [2015JQY01, CL201602]
Memristor, based on the principle of biological synapse, is recognized as one of the key devices in confronting the bottleneck of classical von Neumann computers. However, conventional memristors are difficult to continuously adjust the conduction and dutifully mimic the biosynapse function. Here, TiO2 films with self-assembled Ag nanoclusters implemented by gradient Ag dopant are employed to achieve enhanced memristor performance. The memristors exhibit gradual both potentiating and depressing conduction under positive and negative pulse trains, which can fully emulate excitation and inhibition of biosynapse. Moreover, comprehensive biosynaptic functions and plasticity, including the transition from short-term memory to long-term memory, long-term potentiation and depression, spike-timing-dependent plasticity, and paired-pulse facilitation, are implemented with the fabricated memristors in this work. The applied pulses with a width of hundreds of nanoseconds timescale are beneficial to realize fast learning and computing. High-resolution transmission electron microscopy observations clearly demonstrate that Ag clusters redistribute to form Ag conductive filaments between Ag and Pt electrode under electrical field at ON-state device. The experimental data confirm that the oxides doped with Ag clusters have the potential for mimicking biosynaptic behavior, which is essential for the further creation of artificial neural systems.
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