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用于神经形态计算应用的超快低功耗二维BiOSe忆阻器

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

作者信息

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

机构信息

Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China.

Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.

出版信息

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

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

摘要

模拟突触可塑性的忆阻器是开启节能神经形态计算架构新时代的基石,这将克服冯·诺依曼瓶颈的限制。层状二维(2D)BiOSe作为下一代电子学的新兴材料,在提高忆阻器件的效率和性能方面具有重要意义。在此,通过将云母衬底面朝下配置在BiOSe粉末上生长出高质量的BiOSe纳米片。然后,制造出具有优异性能的双极BiOSe忆阻器,包括超快开关速度(<5 ns)和低功耗(<3.02 pJ)。此外,在BiOSe忆阻器中还展示了突触可塑性,如长时程增强/抑制(LTP/LTD)、双脉冲易化(PPF)和脉冲时间依赖可塑性(STDP)。此外,基于电导修改的模拟人工神经网络(ANN)进行MNIST识别可达到91%的高精度。值得注意的是,二维BiOSe使忆阻器具有超快和低功耗特性,在神经形态计算应用中显示出巨大潜力。

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