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Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

基于二维α-In2Se3忆阻器的突触可塑性和学习行为

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Abstract

Nowadays, memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses, making them become a new generation of research hotspots for brain-like neurocomputing. In this work, we prepare a memristor based on two-dimensional α-In2Se3 nanosheets, which exhibits excellent electrical properties, faster switching speeds, and continuous tunability of device conduction. Meanwhile, most basic bio-synapse functions can be implemented faithfully, such as short-term memory (STM), long-term memory (LTM), four different types of spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF). More importantly, we systematically study three effective methods to achieve LTM, in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve. Therefore, we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.

摘要

忆阻器与生物突触极为相似, 可以实现生物突触的基本功能, 使其成为了新一代类脑神经计算的研究热点. 在这项工作中, 我们制造了基于二维α-In2Se3材料的忆阻器件, 其表现出了优异的电学性能、 较快的开关速度(16.4和18.0 ns)以及器件电导的连续可调性. 同时, 大多数基本的生物突触功能得以实现, 如短时记忆(STM)、 长时记忆(LTM)、 四种不同类型的尖峰时间依赖可塑性(STDP)和双脉冲易化行为(PPF). 更重要的是, 我们系统性地研究了三种实现长时记忆的有效方法, 其中, 根据艾宾浩斯遗忘曲线成功地模拟了强化学习功能. 这项工作将促进类脑神经计算以及人工智能在学习方面的研究发展.

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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 (61874158, 62004056 and 62104058), the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences (XDB44000000-7), Hebei Basic Research Special Key Project (F2021201045), 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 Young Scientific Research and Innovation Team of Hebei University (605020521001), the Special Support Funds for National High Level Talents (041500120001), the High-level Talent Research Startup Project of Hebei University (521000981426), the Science and Technology Project of Hebei Education Department (QN2020178 and QN2021026), and the Post-graduate’s Innovation Fund Project of Hebei Province (CXZZBS2022020).

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Author notes

  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

    Ying Zhao  (赵莹), Yifei Pei  (裴逸菲), Xiaoyu Li  (李晓钰), Jingjuan Wang  (王静娟), Lei Yan  (晏磊), Hui He  (何惠), Zhenyu Zhou  (周振宇), Jianhui Zhao  (赵建辉) & Xiaobing Yan  (闫小兵)

  2. Cambricon Technologies Corporation Limited, Beijing, 100000, China

    Zichang Zhang  (张子昌)

  3. Department of Materials Science and Engineering, National University of Singapore, Singapore, 117576, Singapore

    Jingsheng Chen  (陈景升)

Authors
  1. Ying Zhao  (赵莹)
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  2. Yifei Pei  (裴逸菲)
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  3. Zichang Zhang  (张子昌)
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  4. Xiaoyu Li  (李晓钰)
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  11. Xiaobing Yan  (闫小兵)
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Contributions

Author contributions Yan X proposed the idea, integrated interpretation and revised the paper. Zhao Y completed the performance test of the device and drafted the manuscript. Pei Y fabricated the device and assisted in writing the manuscript. Zhang Z revised the paper. Li X, Wang J, Yan L and He H helped with characterization analysis. Zhou Z, Zhao J, and Chen J coordinated the work. All authors commented on the final paper.

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 Details of the simulation and supporting data are available in the online version of this paper.

Ying Zhao received her BSc degree in applied electronic technology education from the School of Physics and Electronic Science, Hunan University of Science and Technology in 2019. She is currently an ME student of Hebei University. Her current research focuses on the field of memristors.

Yifei Pei received the MS degree from Hebei University, Baoding, China, in 2020. She is currently pursuing a PhD degree at the Department of Physics at Hebei University, Baoding, China. Her current research focuses on the field of memristors.

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.

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Zhao, Y., Pei, Y., Zhang, Z. et al. Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior. Sci. China Mater. 65, 1631–1638 (2022). https://doi.org/10.1007/s40843-021-1925-x

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