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Recent progress in ferroelectric synapses and their applications

铁电突触器件及其应用的研究进展

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Abstract

Brain-like computing is an important direction for the development of integrated circuits in the post-Moore era, and the development of artificial synaptic devices that can simulate ideal synaptic behavior is the key to building brainlike computing chips with neuron-synapse-neuron connectivity. Ferroelectric thin film materials have unique nonvolatile polarization, and the plasticity of polarization is very similar to that of biological synapses, so ferroelectric synaptic devices have been widely concerned in recent years. In this paper, the research progress of ferroelectric synaptic devices is reviewed from two aspects, simulation of synaptic function in devices and brain-like computing applications. The results show that ferroelectric synaptic devices have two typical structures of two-terminal and three-terminal. In addition to effectively simulating biological synapse functions, ferroelectric synaptic devices also have the advantages of simple structure, low power consumption, high stability, large switching ratio, and fast programming speed. In terms of applications, a series of advances have been made in the study of ferroelectric synapse-based neural networks for image recognition; meanwhile, ferroelectric synapses have also been applied to tactile and visual bionics. Although abundant research progress has been made, ferroelectric synapses are still at the proof-of-principle stage. There are considerable challenges in the synaptic performance regulation mechanism, reliability evaluation criteria, array structure optimization design, high-density integration process, neuromorphic computing architecture design, and novel application scenario expansion, which are also the directions that need to be focused for future ferroelectric synapse research.

摘要

类脑计算是后摩尔时代集成电路发展的重要方向, 开发能够模拟理想突触行为的人工突触器件是构建神经元-突触-神经元连接方式的类脑计算芯片的关键. 铁电薄膜材料具有独特的非易失性极化, 极化的可塑性与生物突触的可塑性十分类似, 因此铁电突触器件近年来受到了广泛关注. 本文从器件突触功能模拟和类脑计算应用两个方面对铁电突触器件的研究进展进行了综述. 结果表明, 铁电突触器件具有两端和三端两种典型结构. 除了能有效模拟生物突触功能, 铁电突触器件还具有结构简单、 功耗低、 稳定性高、 开关比大及编程速度快等优点. 在应用层面, 基于铁电突触的神经网络在图像识别方面的研究取得了一系列进展. 此外铁电突触还被应用于触觉和视觉仿生. 虽然取得了丰富的研究进展, 但铁电突触器件目前仍停留在原理的提出和实验验证阶段, 在突触性能调控机理、 可靠性评价标准、 阵列结构优化设计、 高密度集成工艺、 神经形态计算架构设计、 新颖应用场景拓展等方面的研究都存在不小的挑战, 这些也是未来铁电突触研究所要聚焦的方向.

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Acknowledgements

This work was financially supported by the Scientific Research Foundation of Hunan Provincial Education Department (20C1784), the National Natural Science Foundation of China (61804130, 62104267, and 11832016), Hunan Provincial Science and Technology Innovation Major Project (2020GK2014), the National Key Research and Development Plan (2021YFB4000800), the Cultivation Projects of National Major R & D Project (92164108), the Key Projects of National Natural Science Foundation of China (11835008), and the Foundation of Innovation Center of Radiation Application (KFZC2020020901).

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Authors and Affiliations

  1. School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, China

    Shaoan Yan  (燕少安), Junyi Zang  (臧俊逸), Pei Xu  (徐佩), Yingfang Zhu  (朱颖方) & Xuejun Zheng  (郑学军)

  2. School of Materials Science and Engineering, Xiangtan University, Xiangtan, 411105, China

    Gang Li  (李刚) & Minghua Tang  (唐明华)

  3. School of Materials, Sun Yat-Sen University, Guangzhou, 510275, China

    Qilai Chen  (陈祺来)

  4. College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, 410082, China

    Zhuojun Chen  (陈卓俊)

  5. State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China

    Yan Zhang  (张妍)

Authors
  1. Shaoan Yan  (燕少安)
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  2. Junyi Zang  (臧俊逸)
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  3. Pei Xu  (徐佩)
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  4. Yingfang Zhu  (朱颖方)
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  5. Gang Li  (李刚)
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  6. Qilai Chen  (陈祺来)
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  7. Zhuojun Chen  (陈卓俊)
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  8. Yan Zhang  (张妍)
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  9. Minghua Tang  (唐明华)
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  10. Xuejun Zheng  (郑学军)
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Contributions

Author contributions Yan S, Zang J, and Zhu Y proposed the topic and outline of the review Zang J, Yan S, Zhu Y, and Xu P wrote the first draft of the review Li G, Chen Q, Chen Z, and Zhang Y participated in discussion and gave some valuable suggestions. Zhu Y, Tang M, and Zheng X revised the manuscript before submission. All authors co-edited the final version of the review.

Corresponding authors

Correspondence to Yingfang Zhu  (朱颖方), Minghua Tang  (唐明华) or Xuejun Zheng  (郑学军).

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

Additional information

Shaoan Yan received his PhD degree in materials science and engineering from Xiangtan University in 2016. He is currently an associate professor at the School of Mechanical Engineering and Mechanics, Xiangtan University. His research mainly focuses on ferroelectric MEMS devices, ferroelectric memories, and ferroelectric synaptic devices.

Yingfang Zhu received her PhD degree in physics from Nanjing University in 2021. She is currently working at the School of Mechanical Engineering and Mechanics, Xiangtan University. Her research interests include the design of advanced energy materials, machine learning, and neuromorphic computing.

Minghua Tang received his PhD degree in materials physics and chemistry from Xiangtan University in 2007. He is a professor at the School of Materials Science and Engineering, Xiangtan University. He was a visiting professor at Tsinghua University, Tokyo Institute of Technology, and Nanyang Technological University, focusing on the fabrication and the characteristics of ferroelectric thin film memory with 65 nm process. He is currently leading a research team working on ferroelectric thin-film memory (FeFET), resistive random access memory (RRAM), and neuromorphological devices for computing-in-memory applications.

Xuejun Zheng received his PhD degree in general mechanics and fundamentals of mechanics from Xiangtan University in 2002. He is a “Changjiang Scholar” distinguished professor at Xiangtan University and the leader of the innovation team of “Mechanics of Thin Film Materials and Devices” in the Ministry of Education. His research mainly focuses on mechanics of low-dimensional materials, NEMS and smart sensors, and advanced characterization technology of nanomaterials.

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Yan, S., Zang, J., Xu, P. et al. Recent progress in ferroelectric synapses and their applications. Sci. China Mater. 66, 877–894 (2023). https://doi.org/10.1007/s40843-022-2318-9

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  • DOI: https://doi.org/10.1007/s40843-022-2318-9

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