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Ultrafast and Low-Power 2D Bi<sub>2</sub>O<sub>2</sub>Se 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

2023Nano Letters112 citationsDOI

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) Bi 2 O 2 Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi 2 O 2 Se nanosheets are grown by configuring mica substrates face-down on the Bi 2 O 2 Se powder. Then, bipolar Bi 2 O 2 Se 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 Bi 2 O 2 Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi 2 O 2 Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.

Topics & Concepts

Neuromorphic engineeringMemristorMaterials scienceUltrashort pulseMNIST databaseVon Neumann architectureScalabilityBottleneckComputer scienceArtificial neural networkNanotechnologyOptoelectronicsElectronic engineeringArtificial intelligencePhysicsEngineeringEmbedded systemLaserDatabaseOperating systemOpticsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesTransition Metal Oxide Nanomaterials
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