Memristors based on NdNiO nanocrystals film as sensory neurons for neuromorphic computing.

Journal: Materials horizons
PMID: 37555245

Abstract

By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.

Authors

  • Jianhui Zhao
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Yunfeng Ran
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China. xiaobing_yan@126.com.
  • Yifei Pei
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Yiheng Wei
    School of Mathematics, Southeast University, Nanjing, 211189, China. Electronic address: neudawei@seu.edu.cn.
  • Jiameng Sun
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China. xiaobing_yan@126.com.
  • Zixuan Zhang
    Department of Electrical & Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
  • Jiacheng Wang
    Vanderbilt University, Nashville TN 37215, USA.
  • Zhenyu Zhou
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China. xiaobing_yan@126.com.
  • Zhongrong Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Yong Sun
    School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Xiaobing Yan
    Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Key Laboratory of Optoelectronic Information Materials of Hebei Province, Hebei University, Baoding, Hebei, 071002, China.

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