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Artificial nociceptor based on TiO2 nanosheet memristor

一种基于TiO2纳米片忆阻器的人工伤害感受器

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Abstract

With the development of technology, the learning and memory functions of artificial memristor synapses are necessary for realizing artificial neural networks and neural neuromorphic computing. Owing to their high scalability performance, nanosheet materials have been widely employed in cellular-level learning, but the behaviors of nociceptor based on nanosheet materials have rarely been studied. Here, we present a memristor with an Al/TiO2/Pt structure. After electroforming, the memristor device showed a gradual conductance regulation and could simulate synaptic functions such as the potentiation and depression of synaptic weights. We also designed a new scheme that verifies the pain sensitization, desensitization, allodynia, and hyperalgesia behaviors of real nociceptors in the fabricated memristor. Memristors with these behaviors can significantly improve the quality of intelligent electronic devices. Data fitting showed that the high resistance and low resistance states were consistent with the hopping conduction mechanism. This work promises the application of TiO2-based devices in next-generation neuromorphological systems.

摘要

人工忆阻突触的学习记忆功能是实现人工神经网络和神经 形态计算的必要条件. 纳米片材料由于其良好的可扩展性, 在细胞 级学习水平中得到了广泛的应用, 但基于纳米片材料的伤害感受 器行为研究却鲜有报道. 本文中, 我们提出了一种具有Al/TiO2/Pt 结构的忆阻器. 电铸后, 忆阻器呈现出逐渐的电导调节, 并能模拟 突触功能, 如突触重量的增加和降低. 我们还设计了一个新的方案 来验证真实伤害感受器的痛觉敏感、脱敏、超敏和痛觉过敏行为. 具有这些特性的忆阻器可以显著提高智能电子器件的性能. 数据 拟合表明, 高阻和低阻状态符合跳跃导电机制. 这项工作使得基于 TiO2的器件有望应用于下一代神经形态学系统.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (61674050 and 61874158), the Project of Distinguished Youth of Hebei Province (A2018201231), 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 Outstanding Young Scientific Research and Innovation Team of Hebei University, the Highlevel Talent Research Startup Project of Hebei University (521000981426), and the Special Support Funds for National High Level Talents (041500120001 and 521000981429).

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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 Electron and Information Engineering, Hebei University, Baoding, 071002, China

    Jinling Lan  (兰晋玲), Gang Cao  (曹刚), Jingjuan Wang  (王静娟) & Xiaobing Yan  (闫小兵)

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

    Xiaobing Yan  (闫小兵)

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  1. Jinling Lan  (兰晋玲)
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  2. Gang Cao  (曹刚)
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Contributions

Yan X proposed the idea of this research and revised the paper; Lan J completed the performance test of the device and prepared the manuscript; Cao G and Wang J coordinated the work. All authors contributed to the general discussion.

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Correspondence to Xiaobing Yan  (闫小兵).

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The authors declare that they have no conflict of interest.

Jinling Lan received her BSc degree in electronic information science and technology from the School of Information Technology, Hebei Normal University in 2018. She is currently a ME student of Hebei University. Her current research focuses on the field of memristors.

Gang Cao received his BSc degree in electronic information science and technology from the School of Electronic Information and Physics of Changzhi College in 2018. He is now a student of Hebei University. His current research focuses on the field of memristors.

Jingjuan Wang received her BSc degree in communication engineering from the Department of Electronic Information Engineering, Tangshan University, China, in 2016. She is currently a DE student at Hebei University. 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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Lan, J., Cao, G., Wang, J. et al. Artificial nociceptor based on TiO2 nanosheet memristor. Sci. China Mater. 64, 1703–1712 (2021). https://doi.org/10.1007/s40843-020-1564-y

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