Sodium-Doped Titania Self-Rectifying Memristors for Crossbar Array Neuromorphic Architectures

© 2021 Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 6 vom: 12. Feb., Seite e2106913
1. Verfasser: Kim, Sung-Eun (VerfasserIn)
Weitere Verfasser: Lee, Jin-Gyu, Ling, Leo, Liu, Stephanie E, Lim, Hyung-Kyu, Sangwan, Vinod K, Hersam, Mark C, Lee, Hong-Sub
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article Na-doped TiO2 artificial neural networks atomic layer deposition long-term potentiation/depression synaptic response
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520 |a Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for nonvolatile memory elements in artificial neural networks. However, the relatively low reliability of memristors coupled with crosstalk and sneak currents in CAAs have limited the realization of the full potential of this technology. Here, high-reliability Na-doped TiO2  memristors grown in situ by atomic layer deposition (ALD) are demonstrated, where reversible Na migration underlies the resistive-switching mechanism. By employing ALD growth with an aqueous NaOH reactant in deionized water, uniform implantation of Na dopants is achieved in the crystallized TiO2  thin films at 250 °C without post-annealing. The resulting Na-doped TiO2  memristors show electroforming-free and self-rectifying resistive-switching behavior, and they are ideally suited for selectorless CAAs. Effective addressing of selectorless nodes is demonstrated via electrical measurement of individual memristors in a 6 × 6 crossbar using a read current of less than 1 µA with negligible sneak current at or below the noise level of ≈100 pA. Finally, the long-term potentiation and depression synaptic behavior from these Na-doped TiO2  memristors achieves greater than 99.1% accuracy for image-recognition tasks using a convolutional neural network based on the selectorless of crossbar arrays 
650 4 |a Journal Article 
650 4 |a Na-doped TiO2 
650 4 |a artificial neural networks 
650 4 |a atomic layer deposition 
650 4 |a long-term potentiation/depression 
650 4 |a synaptic response 
700 1 |a Lee, Jin-Gyu  |e verfasserin  |4 aut 
700 1 |a Ling, Leo  |e verfasserin  |4 aut 
700 1 |a Liu, Stephanie E  |e verfasserin  |4 aut 
700 1 |a Lim, Hyung-Kyu  |e verfasserin  |4 aut 
700 1 |a Sangwan, Vinod K  |e verfasserin  |4 aut 
700 1 |a Hersam, Mark C  |e verfasserin  |4 aut 
700 1 |a Lee, Hong-Sub  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Advanced materials (Deerfield Beach, Fla.)  |d 1998  |g 34(2022), 6 vom: 12. Feb., Seite e2106913  |w (DE-627)NLM098206397  |x 1521-4095  |7 nnns 
773 1 8 |g volume:34  |g year:2022  |g number:6  |g day:12  |g month:02  |g pages:e2106913 
856 4 0 |u http://dx.doi.org/10.1002/adma.202106913  |3 Volltext 
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