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Multilevel state ferroelectric La:HfO2-based memristors and their implementations in associative learning circuit and face recognition

多阻态铁电HLO忆阻器及其在联想学习电路和人脸识别中的实现

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Abstract

As Moore’s Law approaches physical limits, traditional von Neumann buildings are facing challenges. The application of memristors in multilayer storage, neuromorphic systems and analog circuits has the potential to overcome the von Neumann architecture bottleneck. Here, we fabricated high-performance memristors based on the Pd/La: HfO2/La2/3Sr1/3MnO3 device on silicon substrate, which facilitate the compatibility with complementary metal oxide semiconductor processes. The memristor devices exhibited good cycling stability and multilevel resistive state storage capabilities. And the synaptic properties of the device, such as long-term potentiation/depression, short-term memory to long-term memory, spike time-dependent plasticity, and double-pulse facilitation, were also shown. Based on the brain-like synaptic behavior of the device, a high recognition rate of 91.11% was achieved in recognizing face images in neural-inspired computing. Through theoretical calculation and hardware associative learning circuit test, the hafnium-based ferroelectric memristor was successfully applied to biological associative learning behavior for the first time.

摘要

随着摩尔定律接近物理极限, 传统的冯诺依曼架构面临挑战. 忆阻器在多层存储、 神经形态系统和模拟电路中的应用具有克服冯诺依曼架构瓶颈的潜力. 在这里, 我们在硅衬底上生长了Pd/La:HfO2(HLO)/La2/3Sr1/3MnO3高性能忆阻器, 其有利于与互补式氧化物半导体工艺兼容. 该忆阻器器件表现出良好的循环稳定性和多级电阻状态存储能力, 以及器件的突触特性, 如长时增强/抑制、 短时记忆到长时记忆、 尖峰时间依赖性可塑性和双脉冲促进. 基于器件的类脑突触行为, 在神经启发计算中识别人脸图像时, 识别率高达91.11%. 通过理论计算和硬件联想学习电路测试, 基于铪基铁电忆阻器的生物联想学习行为得以实现.

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Acknowledgements

This work was financially supported by the National Key R&D Plan “Nano Frontier” Key Special Project (2021YFA1200502), the Cultivation Projects of National Major R&D Project (92164109), the National Natural Science Foundation of China (61674050 and 61874158), the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences (XDB44000000-7), Hebei Basic Research Special Key Project (F2021201045), the Natural Science Foundation of Hebei Province (F2022201054 and F2021201022), the Advanced Talents Incubation Program of Hebei University (521000981426, 521100221071, and 521000981363), Baoding Science and Technology Plan Project (2172P011), the Support Program for the Top Young Talents of Hebei Province (70280011807), the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province (SLRC2019018), the Outstanding Youth Scientific Research and Innovation Team of Hebei University (605020521001), the Special Support Funds for National High Level Talents (041500120001), the Science and Technology Project of Hebei Education Department (QN2020178 and QN2021026), the Interdisciplinary Key Research Program of Natural Science of Hebei University (DXK202101), the Institute of Life Sciences and Green Development (521100311), and the Post-graduate’s Innovation Fund Project of Hebei University (HBU2022ss021).

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  1. These authors contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China

    Jiangzhen Niu  (牛江珍), Ziliang Fang  (方子良), Gongjie Liu  (刘公杰), Zhen Zhao  (赵桢) & Xiaobing Yan  (闫小兵)

  2. Institute of Life Sciences and Green Development, Hebei University, Baoding, 071002, China

    Xiaobing Yan  (闫小兵)

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  1. Jiangzhen Niu  (牛江珍)
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Contributions

Author contributions Niu J prepared the samples and carried out the measurements. Yan X designed and supervised the experiments. Fang Z conducted the associative learning circuit design and testing. Liu G conducted the face recognition, Zhao Z did the data curation, and Yan X and Niu J wrote the manuscript. All authors contributed to refining the manuscript.

Corresponding author

Correspondence to Xiaobing Yan  (闫小兵).

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Conflict of interest The authors declare that they have no conflict of interest.

Additional information

Supplementary information Supporting data are available in the online version of the paper.

Jiangzhen Niu received her bachelor’s degree in electrical engineering and automation from the School of Electrical Engineering, North China Electric Power University Science and Technology College in 2019. She is currently an ME student of Hebei University. Her current research focuses on the field of memristors.

Ziliang Fang received a bachelor’s degree from Chuzhou University in 2020. He is a graduate student at the School of Electronic Information Engineering, Hebei University. His current research focuses on the circuit design for memristor applications.

Gongjie Liu received his BS degree from China West Normal University in 2020 and is currently pursuing his MS degree at Hebei University, where his research focuses on memristors and their applications.

Xiaobing Yan is currently a professor at the School of Electronic and Information Engineering, Hebei University. He received his PhD degree from Nanjing University in 2011. From 2014 to 2016, he held the research fellow position at the National University of Singapore. His current research focuses on the field of memristors.

Supplementary Information

40843_2022_2237_MOESM1_ESM.pdf

Multilevel state ferroelectric La:HfO2-based memristors and their implementations in associative learning circuit and face recognition

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Niu, J., Fang, Z., Liu, G. et al. Multilevel state ferroelectric La:HfO2-based memristors and their implementations in associative learning circuit and face recognition. Sci. China Mater. 66, 1148–1156 (2023). https://doi.org/10.1007/s40843-022-2237-2

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  • DOI: https://doi.org/10.1007/s40843-022-2237-2

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