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Nano Research 2022, 15(4): 3606-3613 https://doi.org/10.1007/s12274-021-3899-5
Research Article | Issue | Published: 10 December 2021

Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates

Show Author's Information Chao Wang§,  Tianyu Wang§,  Wendi Zhang,  Jun Jiang( ),  Lin Chen( ),  Anquan Jiang( )
State Key Laboratory of ASIC & Systems, School of Microelectronics, Fudan University, Shanghai 200433, China

§ Chao Wang and Tianyu Wang contributed equally to this work.

Keywords:
synaptic plasticity, memristor, domain wall, recognition, LiNbO3(LNO)
Topics:
Nanoelectronics
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Wang C, Wang T, Zhang W, et al. Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates. Nano Research, 2022, 15(4): 3606-3613. https://doi.org/10.1007/s12274-021-3899-5
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Abstract Full text Electronic supplementary material About this article

Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems, which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption. Here, we report synaptic devices made from highly insulating ferroelectric LiNbO3 (LNO) thin films bonded to SiO2/Si wafers. Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells, which are stimulated using positive/negative voltage pulses (synaptic plasticity), we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls. The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles, representing much better performance than that of random defect-based nonlinear memristors, which generally exhibit large-scale resistance dispersion. The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6% recognition accuracy for faces, thus approaching the theoretical yield of ideal neuromorphic computing devices.


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About this article

Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates

Show Author's information Chao Wang§, Tianyu Wang§, Wendi Zhang, Jun Jiang( ), Lin Chen( ), Anquan Jiang( )
State Key Laboratory of ASIC & Systems, School of Microelectronics, Fudan University, Shanghai 200433, China

§ Chao Wang and Tianyu Wang contributed equally to this work.

Abstract

Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems, which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption. Here, we report synaptic devices made from highly insulating ferroelectric LiNbO3 (LNO) thin films bonded to SiO2/Si wafers. Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells, which are stimulated using positive/negative voltage pulses (synaptic plasticity), we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls. The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles, representing much better performance than that of random defect-based nonlinear memristors, which generally exhibit large-scale resistance dispersion. The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6% recognition accuracy for faces, thus approaching the theoretical yield of ideal neuromorphic computing devices.

Keywords: synaptic plasticity, memristor, domain wall, recognition, LiNbO3(LNO)

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Publication history
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Acknowledgements

Publication history

Received: 09 August 2021
Revised: 13 September 2021
Accepted: 22 September 2021
Published: 10 December 2021
Issue date: April 2022

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Acknowledgements

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2019YFA0308500) and the National Natural Science Foundation of China (No. 61904034). We acknowledge the use of the Yale Face Database. We thank David MacDonald, MSc, from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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