Ultrafast and Low-Power 2D Bi2O2Se Memristors for Neuromorphic Computing Applications

Zilong Dong; Qilin Hua; Jianguo Xi; Yuanhong Shi; Tianci Huang; Xinhuan Dai; Jianan Niu; Bingjun Wang; Zhong Lin Wang; Weiguo Hu

doi:10.1021/acs.nanolett.3c00322

Ultrafast and Low-Power 2D Bi₂O₂Se Memristors for Neuromorphic Computing Applications

Nano Lett. 2023 May 10;23(9):3842-3850. doi: 10.1021/acs.nanolett.3c00322. Epub 2023 Apr 24.

Authors

Zilong Dong^{1

2

3}, Qilin Hua⁴, Jianguo Xi², Yuanhong Shi^{1

2

3}, Tianci Huang^{1

2

3}, Xinhuan Dai^{2

3}, Jianan Niu^{2

3}, Bingjun Wang^{2

3}, Zhong Lin Wang^{2

3}, Weiguo Hu^{1

2

3}

Affiliations

¹ Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China.
² Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
³ School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
⁴ School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.

PMID: 37093653
DOI: 10.1021/acs.nanolett.3c00322

Abstract

Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi₂O₂Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi₂O₂Se nanosheets are grown by configuring mica substrates face-down on the Bi₂O₂Se powder. Then, bipolar Bi₂O₂Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi₂O₂Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi₂O₂Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.

Keywords: Bi2O2Se; artificial neural network; low power; memristor; synapse.