2019
DOI: 10.1021/acs.chemmater.9b02245
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Memristive Electronic Synapses Made by Anodic Oxidation

Abstract: Memristors have recently gained growing interest due to their potential application as electronic synapses to build artificial neural networks for artificial intelligence systems. However, modulating the conductivity of memristors in a dynamic way to emulate biological synaptic behaviors is very challenging. Here we show the first fabrication of memristive electronic synapses using a dielectric film (TiO2–x ) synthesized via an electrochemical anodization method. Pt/anodic TiO2–x /Ti memristive synapses show r… Show more

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Cited by 28 publications
(23 citation statements)
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References 54 publications
(86 reference statements)
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“…Recently, artificial intelligence and deep learning algorithms have attracted considerable attention while traditional von Neuman computers could not efficiently deal with the unstructured information because of the physical separation of the storage and processing units, as the “von Neumann bottleneck.”1–5 With the coming era of big data and artificial intelligence, the device with new architecture should be developed for comprehensive innovation of the computers. Brain‐inspired neuromorphic computation with efficient energy utilization, massive parallelism, and flexible adaptive capability, exhibits the great potential to realize multifunctional computing 6–8.…”
Section: Comparison Of the Energy Consumption From Other Researchesmentioning
confidence: 99%
“…Recently, artificial intelligence and deep learning algorithms have attracted considerable attention while traditional von Neuman computers could not efficiently deal with the unstructured information because of the physical separation of the storage and processing units, as the “von Neumann bottleneck.”1–5 With the coming era of big data and artificial intelligence, the device with new architecture should be developed for comprehensive innovation of the computers. Brain‐inspired neuromorphic computation with efficient energy utilization, massive parallelism, and flexible adaptive capability, exhibits the great potential to realize multifunctional computing 6–8.…”
Section: Comparison Of the Energy Consumption From Other Researchesmentioning
confidence: 99%
“…The strongest candidates as materials for memristors fabrication are valve metals such as Ti, Ta, Nb, or Hf [ 17 , 18 , 19 ]. Hafnium dioxide is typically a compact oxide with a high dielectric constant, large bandgap, and high chemical and thermal stability [ 20 ].…”
Section: Introductionmentioning
confidence: 99%
“…Oxides of valve metals have shown remarkable performances as memristive elements. 15 18 Studies on Hf- and Ta-based memristors reported excellent electrical and memory properties, such as multilevel switching, high endurance, and data retention. 16 , 17 The deposition of oxide layers is commonly done by atomic layer deposition 19 , 20 or sputtering.…”
mentioning
confidence: 99%