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Materials (Basel). 2018 Oct 26;11(11). pii: E2102. doi: 10.3390/ma11112102.

Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing.

Wang R1,2, Shi T3,4, Zhang X5,6, Wang W7, Wei J8,9, Lu J10,11, Zhao X5, Wu Z12,13, Cao R14,15, Long S16, Liu Q17,18, Liu M19,20.

Author information

1
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wangrui@ime.ac.cn.
2
University of Chinese Academy of Sciences, Beijing 100049, China. wangrui@ime.ac.cn.
3
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. shituo@ime.ac.cn.
4
University of Chinese Academy of Sciences, Beijing 100049, China. shituo@ime.ac.cn.
5
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. zhaoxiaolong@ime.ac.cn.
6
University of Chinese Academy of Sciences, Beijing 100049, China. zhaoxiaolong@ime.ac.cn.
7
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wangwei_esss@nudt.edu.cn.
8
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. weijinsong@ime.ac.cn.
9
University of Science and Technology of China, Hefei 230026, China. weijinsong@ime.ac.cn.
10
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. lujian@ime.ac.cn.
11
University of Science and Technology of China, Hefei 230026, China. lujian@ime.ac.cn.
12
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wuzuheng@ime.ac.cn.
13
University of Chinese Academy of Sciences, Beijing 100049, China. wuzuheng@ime.ac.cn.
14
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. caorongrong@ime.ac.cn.
15
University of Chinese Academy of Sciences, Beijing 100049, China. caorongrong@ime.ac.cn.
16
University of Science and Technology of China, Hefei 230026, China. longshibing@ime.ac.cn.
17
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. liuqi@ime.ac.cn.
18
University of Chinese Academy of Sciences, Beijing 100049, China. liuqi@ime.ac.cn.
19
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. liuming@ime.ac.cn.
20
University of Chinese Academy of Sciences, Beijing 100049, 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/TaOx/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:

artificial synapse; memristor; neuromorphic computing

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