Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing.

Rui Wang, Tuo Shi, Xumeng Zhang, Wei Wang, Jinsong Wei, Jian Lu, Xiaolong Zhao, Zuheng Wu, Rongrong Cao, Shibing Long, Qi Liu, Ming Liu
Author Information
  1. Rui Wang: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wangrui@ime.ac.cn.
  2. Tuo Shi: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. shituo@ime.ac.cn. ORCID
  3. Xumeng Zhang: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. zhaoxiaolong@ime.ac.cn.
  4. Wei Wang: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wangwei_esss@nudt.edu.cn.
  5. Jinsong Wei: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. weijinsong@ime.ac.cn.
  6. Jian Lu: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. lujian@ime.ac.cn.
  7. Xiaolong Zhao: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. zhaoxiaolong@ime.ac.cn.
  8. Zuheng Wu: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wuzuheng@ime.ac.cn.
  9. Rongrong Cao: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. caorongrong@ime.ac.cn.
  10. Shibing Long: University of Science and Technology of China, Hefei 230026, China. longshibing@ime.ac.cn.
  11. Qi Liu: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. liuqi@ime.ac.cn.
  12. Ming Liu: Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. liuming@ime.ac.cn.

Abstract

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al₂O₃/TaO/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (<1%) can be obtained with random initial states. However, the device shows non-linear conductance change characteristics, and a nearly linear conductance change behavior is obtained by optimizing the training scheme. Based on these results, the device is a promising emulator for biology synapses, which could be of great benefit to memristor-based neuromorphic computing.

Keywords

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Grants

  1. 2017YFB0405603/National High Technology Research Development Program
  2. 61521064/National Natural Science Foundation of China
  3. 61732020/National Natural Science Foundation of China
  4. 61751401/National Natural Science Foundation of China
  5. 61522408/National Natural Science Foundation of China

Word Cloud

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