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

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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.

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

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  1. State Key Laboratory of ASIC & Systems, School of Microelectronics, Fudan University, Shanghai, 200433, China

    Chao Wang, Tianyu Wang, Wendi Zhang, Jun Jiang, Lin Chen & Anquan Jiang

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  1. Chao Wang
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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 Res. 15, 3606–3613 (2022). https://doi.org/10.1007/s12274-021-3899-5

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