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2D-3D perovskite memristor with low energy consumption and high stability for neural morphology calculation

用于神经形态学计算的低能耗、高稳定性2D-3D钙钛矿忆阻器

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Abstract

Recently, rapid progress has been made in the application of organic-inorganic halide perovskites in electronic devices, such as memristors and artificial synaptic devices. Organic-inorganic halide perovskite is considered as a promising candidate for the next generation of computing devices due to its ion migration property and advantages in manufacturing. In this work, a two-dimensional (2D)-3D organic-inorganic hybrid perovskite memristor was studied, using the stacking structure of indium tin oxide (ITO)/FA1−yMAyPbI3−xClx/(PEA)2PbI4/Au. The results show that this new type of memristor has novel resistance switching characteristics, such as scanning-rate-dependent current switching property, good current-voltage (I–V) curve repeatability, and ultralow energy consumption. A defect-modulated electron tunneling mechanism is demonstrated using the p-i-n junction model, and it is proven that the conductance state of the memristive device is determined by the defect concentration in the perovskite film near the electrode sides. In addition to the good memristive properties, this 2D-3D perovskite memristor can also function well as an artificial synapse, and its internal defect movement can faithfully simulate the inflow and extrusion of Ca2+ in biological synapses. Moreover, this perovskite-based artificial synapse has ultra-low power consumption due to the switchable p-i-n structure in organic-inorganic halide perovskites. Our finding highlights the immense application potential of the 2D-3D perovskite memristor in the future neuromorphic computing system.

摘要

近年来, 有机-无机卤化物钙钛矿在忆阻器和人工突触器件等电子器件中的应用取得了快速进展. 由于其离子迁移特性和制造上的优势, 有机-无机卤化物钙钛矿有望成为下一代计算设备的候选材料. 本文采用ITO/FA1−yMAyPbI3−xClx/(PEA)2PbI4/Au的叠层结构, 研究了2D-3D有机-无机杂化钙钛矿忆阻器. 结果表明, 这种新型忆阻器具有新颖的电阻开关特性, 如扫描速率相关的电流开关特性、良好的电流-电压曲线重复性和超低能耗. 利用p-i-n结模型证实了缺陷调制电子隧穿机制, 并证明了忆阻器件的电导状态由电极侧附近钙钛矿薄膜中的缺陷浓度决定. 除了良好的忆阻特性外, 这种2D-3D钙钛矿型忆阻器还可以很好地用作人工突触, 其内部缺陷运动可以真实地模拟生物突触中Ca2+的流入和挤出. 此外, 由于有机-无机卤化物钙钛矿中的可切换p-i-n结构, 这种基于钙钛矿的人工突触具有超低功耗. 我们的发现展示了2D-3D钙钛矿忆阻器在未来神经形态计算系统中的巨大应用潜力.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (61674050, 62004056, and 61874158), the Project of Distinguished Young of Hebei Province (A2018201231), the Support Program for the Top Young Talents of Hebei Province (70280011807), the Hundred Persons Plan of Hebei Province (E2018050004 and E2018050003), the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province (SLRC2019018), the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences (XDB44000000-7), the Special Support Funds for National High Level Talents (041500120001), Hebei Basic Research Special Key Project (F2021201045), the Science and Technology Project of Hebei Education Department (QN2020178 and QN2021026), and Singapore Ministry of Education (AcRF TIER 2-MOE2019-T2-2-075).

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Authors and Affiliations

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

    Kaixuan Sun  (孙凯旋), Jingjuan Wang  (王静娟) & Xiaobing Yan  (闫小兵)

  2. State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an, 710071, China

    Qingrui Wang  (王庆瑞), Long Zhou  (周龙) & Jingjing Chang  (常晶晶)

  3. Centre for Micro- and Nano-Electronics (CMNE), School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Rui Guo  (郭瑞) & Beng Kang Tay

  4. UMI 3288 CINTRA (CNRS-NTU-THALES Research Alliances), Nanyang Technological University, Singapore, 637553, Singapore

    Beng Kang Tay

Authors
  1. Kaixuan Sun  (孙凯旋)
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  2. Qingrui Wang  (王庆瑞)
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  3. Long Zhou  (周龙)
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  4. Jingjuan Wang  (王静娟)
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  5. Jingjing Chang  (常晶晶)
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  6. Rui Guo  (郭瑞)
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  7. Beng Kang Tay
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  8. Xiaobing Yan  (闫小兵)
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Contributions

Sun K, Wang Q, Zhou L performed the experiments. Sun K conducted the fitting and data analysis. Sun K and Guo R prepared the original manuscript. All the authors contributed to the general discussion, review and editing. Guo R, Yan X, and Tay BK contributed to the conceptualization and supervision.

Corresponding authors

Correspondence to Rui Guo  (郭瑞), Beng Kang Tay or Xiaobing Yan  (闫小兵).

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Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary information

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

Kaixuan Sun received his BSc degree in microelectronics science and engineering from the School of Physics and Optoelectronic Engineering, Xiangtan University, China in 2018, and ME degree in integrated circuit engineering from the College of Electronic and information Engineering at Hebei University. His current research interest is the field of memristor materials.

Beng Kang Tay is a pioneer in filtered catholic vacuum arc (FCVA) technology and has been working for many years in developing thin film materials especially in amorphous carbon, nanocomposites, graphene and carbon nanotubes. His current research interest includes the growth and application of 2D transition metal dichalcogenide materials and their heterostructures, the fabrication and characterization of nanostructures for field emission, thermal and high-frequency RF applications.

Xiaobing Yan is currently a professor at the College of Electronic and Information Engineering, Hebei University. He received his PhD degree from Nanjing University in 2011. From 2014 to 2016, he held a research fellowship at the National University of Singapore. His current research interest includes the study of novel flash memory, resistive memory, memristors and other novel electronic device integration and logic-controlled embedded circuit design for integration, and the study of memristor arrays for artificial neural networks.

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Sun, K., Wang, Q., Zhou, L. et al. 2D-3D perovskite memristor with low energy consumption and high stability for neural morphology calculation. Sci. China Mater. 66, 2013–2022 (2023). https://doi.org/10.1007/s40843-022-2317-0

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  • DOI: https://doi.org/10.1007/s40843-022-2317-0

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