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Ultrafast and Low-Power 2D Bi<sub>2</sub>O<sub>2</sub>Se Memristors for Neuromorphic Computing Applications

Zilong DongBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaQilin HuaSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaJianguo XiBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaYuanhong ShiBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaTianci HuangBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaXinhuan DaiBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaJianan NiuBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaBingjun WangBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaZhong Lin WangBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, ChinaWeiguo HuBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China

2023en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1021/acs.nanolett.3c00322

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