Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138663
Title: Mediating short-term plasticity in an artificial memristive synapse by the orientation of silica mesopores
Authors: Li, Bin
Liu, Yaqing
Wan, Changjin
Liu, Zhiyuan
Wang, Ming
Qi, Dianpeng
Yu, Jiancan
Cai, Pingqiang
Xiao, Meng
Zeng, Yi
Chen, Xiaodong
Keywords: Engineering::Materials
Issue Date: 2018
Source: Li, B., Liu, Y., Wan, C., Liu, Z., Wang, M., Qi, D., . . . Chen, X. (2018). Mediating short-term plasticity in an artificial memristive synapse by the orientation of silica mesopores. Advanced materials, 30(16), 1706395-. doi:10.1002/adma.201706395
Journal: Advanced Materials
Abstract: Memristive synapses based on resistive switching are promising electronic devices that emulate the synaptic plasticity in neural systems. Short-term plasticity (STP), reflecting a temporal strengthening of the synaptic connection, allows artificial synapses to perform critical computational functions, such as fast response and information filtering. To mediate this fundamental property in memristive electronic devices, the regulation of the dynamic resistive change is necessary for an artificial synapse. Here, it is demonstrated that the orientation of mesopores in the dielectric silica layer can be used to modulate the STP of an artificial memristive synapse. The dielectric silica layer with vertical mesopores can facilitate the formation of a conductive pathway, which underlies a lower set voltage (≈1.0 V) compared to these with parallel mesopores (≈1.2 V) and dense amorphous silica (≈2.0 V). Also, the artificial memristive synapses with vertical mesopores exhibit the fastest current increase by successive voltage pulses. Finally, oriented silica mesopores are designed for varying the relaxation time of memory, and thus the successful mediation of STP is achieved. The implementation of mesoporous orientation provides a new perspective for engineering artificial synapses with multilevel learning and forgetting capability, which is essential for neuromorphic computing.
URI: https://hdl.handle.net/10356/138663
ISSN: 0935-9648
DOI: 10.1002/adma.201706395
Rights: © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. All rights reserved. This paper was published in Advanced Materials and is made available with permission of WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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